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SalienTime: User-driven Selection of Salient Time Steps for Large-Scale Geospatial Data Visualization

Published 6 Mar 2024 in cs.HC and cs.LG | (2403.03449v1)

Abstract: The voluminous nature of geospatial temporal data from physical monitors and simulation models poses challenges to efficient data access, often resulting in cumbersome temporal selection experiences in web-based data portals. Thus, selecting a subset of time steps for prioritized visualization and pre-loading is highly desirable. Addressing this issue, this paper establishes a multifaceted definition of salient time steps via extensive need-finding studies with domain experts to understand their workflows. Building on this, we propose a novel approach that leverages autoencoders and dynamic programming to facilitate user-driven temporal selections. Structural features, statistical variations, and distance penalties are incorporated to make more flexible selections. User-specified priorities, spatial regions, and aggregations are used to combine different perspectives. We design and implement a web-based interface to enable efficient and context-aware selection of time steps and evaluate its efficacy and usability through case studies, quantitative evaluations, and expert interviews.

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Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Christos H. Papadimitriou, Prabhakar Raghavan, Hisao Tamaki, and Santosh S. Vempala. 1998. Latent semantic indexing: a probabilistic analysis. J. Comput. Syst. Sci. 61 (1998), 217–235. https://doi.org/10.1006/jcss.2000.1711 Paszke et al. (2019) Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). 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Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. 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Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. 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Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. 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ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. 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Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Shaoming Pan, Yanwen Chong, Hang Zhang, and Xicheng Tan. 2017. A Global User-Driven Model for Tile Prefetching in Web Geographical Information Systems. PLOS ONE 12, 1 (Jan. 2017), e0170195. https://doi.org/10.1371/journal.pone.0170195 Papadimitriou et al. (1998) Christos H. Papadimitriou, Prabhakar Raghavan, Hisao Tamaki, and Santosh S. Vempala. 1998. Latent semantic indexing: a probabilistic analysis. J. Comput. Syst. Sci. 61 (1998), 217–235. https://doi.org/10.1006/jcss.2000.1711 Paszke et al. (2019) Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Christos H. Papadimitriou, Prabhakar Raghavan, Hisao Tamaki, and Santosh S. Vempala. 1998. Latent semantic indexing: a probabilistic analysis. J. Comput. Syst. Sci. 61 (1998), 217–235. https://doi.org/10.1006/jcss.2000.1711 Paszke et al. (2019) Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. 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Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. 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Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. 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Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. 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Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Christos H. Papadimitriou, Prabhakar Raghavan, Hisao Tamaki, and Santosh S. Vempala. 1998. Latent semantic indexing: a probabilistic analysis. J. Comput. Syst. Sci. 61 (1998), 217–235. https://doi.org/10.1006/jcss.2000.1711 Paszke et al. (2019) Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). 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Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. 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Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. 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Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. 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ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. 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Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Christos H. Papadimitriou, Prabhakar Raghavan, Hisao Tamaki, and Santosh S. Vempala. 1998. Latent semantic indexing: a probabilistic analysis. J. Comput. Syst. Sci. 61 (1998), 217–235. https://doi.org/10.1006/jcss.2000.1711 Paszke et al. (2019) Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. 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Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. 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Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. 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Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. 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Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. 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Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. 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IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. 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ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. 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Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. 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Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. 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Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. 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(2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. 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(2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. 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Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. 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Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Ian T Joliffe and BJT Morgan. 1992. Principal component analysis and exploratory factor analysis. 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Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Shaoming Pan, Yanwen Chong, Hang Zhang, and Xicheng Tan. 2017. A Global User-Driven Model for Tile Prefetching in Web Geographical Information Systems. PLOS ONE 12, 1 (Jan. 2017), e0170195. https://doi.org/10.1371/journal.pone.0170195 Papadimitriou et al. (1998) Christos H. Papadimitriou, Prabhakar Raghavan, Hisao Tamaki, and Santosh S. Vempala. 1998. Latent semantic indexing: a probabilistic analysis. J. Comput. Syst. Sci. 61 (1998), 217–235. https://doi.org/10.1006/jcss.2000.1711 Paszke et al. (2019) Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Christos H. Papadimitriou, Prabhakar Raghavan, Hisao Tamaki, and Santosh S. Vempala. 1998. Latent semantic indexing: a probabilistic analysis. J. Comput. Syst. Sci. 61 (1998), 217–235. https://doi.org/10.1006/jcss.2000.1711 Paszke et al. (2019) Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. 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Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. 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Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. 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Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. 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A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. 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Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Shaoming Pan, Yanwen Chong, Hang Zhang, and Xicheng Tan. 2017. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Christos H. Papadimitriou, Prabhakar Raghavan, Hisao Tamaki, and Santosh S. Vempala. 1998. Latent semantic indexing: a probabilistic analysis. J. Comput. Syst. Sci. 61 (1998), 217–235. https://doi.org/10.1006/jcss.2000.1711 Paszke et al. (2019) Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). 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ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. 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Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. 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Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. 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Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. 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(2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. 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Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. 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IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. 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ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. 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Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. 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Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. 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Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. 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(2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. 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A Global User-Driven Model for Tile Prefetching in Web Geographical Information Systems. PLOS ONE 12, 1 (Jan. 2017), e0170195. https://doi.org/10.1371/journal.pone.0170195 Papadimitriou et al. (1998) Christos H. Papadimitriou, Prabhakar Raghavan, Hisao Tamaki, and Santosh S. Vempala. 1998. Latent semantic indexing: a probabilistic analysis. J. Comput. Syst. Sci. 61 (1998), 217–235. https://doi.org/10.1006/jcss.2000.1711 Paszke et al. (2019) Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. 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A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. 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Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. 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Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. 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Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. 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Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. 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Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Christos H. Papadimitriou, Prabhakar Raghavan, Hisao Tamaki, and Santosh S. Vempala. 1998. Latent semantic indexing: a probabilistic analysis. J. Comput. Syst. Sci. 61 (1998), 217–235. https://doi.org/10.1006/jcss.2000.1711 Paszke et al. (2019) Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). 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Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. 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Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. 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Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. 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ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. 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Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. 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Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Shaoming Pan, Yanwen Chong, Hang Zhang, and Xicheng Tan. 2017. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Christos H. Papadimitriou, Prabhakar Raghavan, Hisao Tamaki, and Santosh S. Vempala. 1998. Latent semantic indexing: a probabilistic analysis. J. Comput. Syst. Sci. 61 (1998), 217–235. https://doi.org/10.1006/jcss.2000.1711 Paszke et al. (2019) Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). 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ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. 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Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. 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Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. 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Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. 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(2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. 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A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. 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Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. 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Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. 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Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. 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Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. 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Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. 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Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Christos H. Papadimitriou, Prabhakar Raghavan, Hisao Tamaki, and Santosh S. Vempala. 1998. Latent semantic indexing: a probabilistic analysis. J. Comput. Syst. Sci. 61 (1998), 217–235. https://doi.org/10.1006/jcss.2000.1711 Paszke et al. (2019) Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. 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Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. 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Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. 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Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. 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Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. 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A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. 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Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. 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Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. 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Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. 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Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. 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Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. 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Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Christos H. Papadimitriou, Prabhakar Raghavan, Hisao Tamaki, and Santosh S. Vempala. 1998. Latent semantic indexing: a probabilistic analysis. J. Comput. Syst. Sci. 61 (1998), 217–235. https://doi.org/10.1006/jcss.2000.1711 Paszke et al. (2019) Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. 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Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. 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Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. 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Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. 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Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. 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A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. 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Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. 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Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. 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Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. 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Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. 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Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. 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Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Christos H. Papadimitriou, Prabhakar Raghavan, Hisao Tamaki, and Santosh S. Vempala. 1998. Latent semantic indexing: a probabilistic analysis. J. Comput. Syst. Sci. 61 (1998), 217–235. https://doi.org/10.1006/jcss.2000.1711 Paszke et al. (2019) Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. 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Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. 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Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. 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Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. 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Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. 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A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. 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Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. 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Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. 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Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. 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Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. 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Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. 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Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Shaoming Pan, Yanwen Chong, Hang Zhang, and Xicheng Tan. 2017. 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Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Christos H. Papadimitriou, Prabhakar Raghavan, Hisao Tamaki, and Santosh S. Vempala. 1998. Latent semantic indexing: a probabilistic analysis. J. Comput. Syst. Sci. 61 (1998), 217–235. https://doi.org/10.1006/jcss.2000.1711 Paszke et al. (2019) Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. 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ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. 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Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. 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Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. 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Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. 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Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. 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Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. 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Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Christos H. Papadimitriou, Prabhakar Raghavan, Hisao Tamaki, and Santosh S. Vempala. 1998. Latent semantic indexing: a probabilistic analysis. J. Comput. Syst. Sci. 61 (1998), 217–235. https://doi.org/10.1006/jcss.2000.1711 Paszke et al. (2019) Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. 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Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. 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(ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. 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Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. 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Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. 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(2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. 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(VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Christos H. Papadimitriou, Prabhakar Raghavan, Hisao Tamaki, and Santosh S. Vempala. 1998. Latent semantic indexing: a probabilistic analysis. J. Comput. Syst. Sci. 61 (1998), 217–235. https://doi.org/10.1006/jcss.2000.1711 Paszke et al. (2019) Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. 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TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. 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Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. 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Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. 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Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. 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A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Christos H. Papadimitriou, Prabhakar Raghavan, Hisao Tamaki, and Santosh S. Vempala. 1998. Latent semantic indexing: a probabilistic analysis. J. Comput. Syst. Sci. 61 (1998), 217–235. https://doi.org/10.1006/jcss.2000.1711 Paszke et al. (2019) Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. 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TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. 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Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). 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Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. 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Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). 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Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. 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Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. 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TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. 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Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. 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User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. 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Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. (NIPS) 32 (2019). Paterek (2007) Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. 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Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. 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(ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. 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Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. 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Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. 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Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. 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Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. Porter et al. (2019) William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. 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Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. 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Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 William P. Porter, Yunhao Xing, Blaise R. Von Ohlen, Jun Han, and Chaoli Wang. 2019. A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data. In 2019 IEEE Visualization Conf. (VIS). IEEE, Vancouver, BC, Canada, 1–5. https://doi.org/10.1109/VISUAL.2019.8933759 Pulido et al. (2021) Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. 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Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. 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A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. 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A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. 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Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). 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Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. 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TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. 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Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. 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Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. 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Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Jesus Pulido, John Patchett, Manish Bhattarai, Boian Alexandrov, and James Ahrens. 2021. Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition. In EuroVis 2021 - Short Papers. The Eurographics Association. https://doi.org/10.2312/evs.20211055 Radford et al. (2015) Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. Orthop. abs/1511.06434 (2015). https://doi.org/10.23919/chicc.2018.8482813 SentinelHub (2020) SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. 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Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. 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Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. 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Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. 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Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Clin. 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Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. 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Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. 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Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. 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Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 SentinelHub. 2020. Sentinel Hub EO Browser. https://apps.sentinel-hub.com/eo-browser/ Shen et al. (2019) Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. 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TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. 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Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. 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Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. 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Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. 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Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Fang Shen, Rugang Tang, Xuerong Sun, and Dongyan Liu. 2019. Simple Methods for Satellite Identification of Algal Blooms and Species Using 10-Year Time Series Data from the East China Sea. Remote Sens. Environ. 235 (Dec. 2019), 111484. https://doi.org/10.1016/j.rse.2019.111484 Shen et al. (2018) Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. 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User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Huamin Qu. 2018. StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views. IEEE Trans. Visual. Comput. Graphics 24, 1 (Jan. 2018), 1004–1013. https://doi.org/10.1109/TVCG.2017.2744159 Shneiderman (1996) B. Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. IEEE Symp. Visual Lang. 336–343. https://doi.org/10.1109/VL.1996.545307 Singh et al. (2016) Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 B. Shneiderman. 1996. 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Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. 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TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pushpendra Singh, Shiv Dutt Joshi, Rakesh Kumar Patney, and Kaushik Saha. 2016. Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms. Circuits Syst. Signal Process. 35 (2016), 3700–3715. https://doi.org/10.1007/s00034-015-0225-z Soro and Lee (2019) Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. 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Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bedionita Soro and Chaewoo Lee. 2019. A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors 19 (2019). https://doi.org/10.3390/s19081790 Stockhause (2013) Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. 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Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. 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User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. 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Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. 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Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. 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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. 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Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. 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Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Martina Stockhause. 2013. User Driven Data Access Mechanisms. In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. 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Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. 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Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. 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Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. 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In Earth System Modelling - Volume 6: ESM Data Archives in the Times of the Grid, Wolfgang Hiller, Reinhard Budich, and René Redler (Eds.). Springer, Berlin, Heidelberg, 33–47. https://doi.org/10.1007/978-3-642-37244-5_5 Tang et al. (2014) Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. 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Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Duyu Tang, Furu Wei, Nan Yang, M. Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. Annu. Meet. Assoc. Comput. Linguist. (ACL). https://doi.org/10.3115/v1/p14-1146 Teng-Yok Lee and Han-Wei Shen (2009a) Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. 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IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. 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Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. 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Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009a. Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 1359–1366. https://doi.org/10.1109/TVCG.2009.200 Teng-Yok Lee and Han-Wei Shen (2009b) Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. 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Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. 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ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Teng-Yok Lee and Han-Wei Shen. 2009b. Visualizing Time-Varying Features with TAC-based Distance Fields. In IEEE Pacific Visual. Symp. (PacificVIS). IEEE, Beijing, China, 1–8. https://doi.org/10.1109/PACIFICVIS.2009.4906831 Tolman et al. (2014) H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. 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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 H. Tolman, M. Accensi, H. Alves, F. Ardhuin, J. Bidlot, N. Booij, A. Bennis, Tim Campbell, D. Chalikov, Arun Chawla, J. Filipot, M. Foreman, P. Janssen, F. Leckler, Jian-Guo Li, K. Lind, M. Orzech, R. Padilla-Hernández, W. Rogers, A. Rawat, A. Roland, M. D. Sikiric, M. Szyszka, B. Tracy, G. V. Vledder, A. J. Westhuysen, and S. Zieger. 2014. User Manual and System Documentation of WAVEWATCH III (R) Version 4.18. NOAA (National Oceanic and Atmospheric Administration). Tong et al. (2012) Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xin Tong, Teng-Yok Lee, and Han-Wei Shen. 2012. Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping. In IEEE Symp. Large Data Anal. Visualization (LDAV). 49–56. https://doi.org/10.1109/LDAV.2012.6378975 Traynor and Williams (1995) Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. 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Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. 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TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. 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Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. 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Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. 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Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370
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Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Carol Traynor and Marian G. Williams. 1995. Why Are Geographic Information Systems Hard to Use?. In Proc. 1995 CHI Conf. Human Factors Comput. Syst. ACM Press, Denver, Colorado, United States, 288–289. https://doi.org/10.1145/223355.223678 U.S. Geological Survey (2023) U.S. Geological Survey. 2023. USGS EarthExplorer. https://earthexplorer.usgs.gov/ Vincent et al. (2008) Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. Int. Conf. Mach. Learn. (ICML). https://doi.org/10.1145/1390156.1390294 Woodring and Han-Wei Shen (2009) J. Woodring and Han-Wei Shen. 2009. Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet. IEEE Trans. Vis. Comput. Graphics 15, 1 (Jan. 2009), 123–137. https://doi.org/10.1109/TVCG.2008.69 Wu et al. (2022) Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. 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Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaosong Yang. 2010. 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ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaosong Yang. 2010. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. 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Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. 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VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. 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Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. 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Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. 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Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Mengxi Wu, Yi-Jen Chiang, and Christopher Musco. 2022. Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41, 3 (June 2022), 309–320. https://doi.org/10.1111/cgf.14542 Yang et al. (2010) Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Robert Raskin, Michael Goodchild, and Mark Gahegan. 2010. Geospatial Cyberinfrastructure: Past, Present and Future. Comput. Environ. Urban Syst. 34, 4 (July 2010), 264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001 Yang et al. (2017) Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang, and Yun Li. 2017. Utilizing Cloud Computing to Address Big Geospatial Data Challenges. Comput. Environ. Urban Syst. 61 (Jan. 2017), 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010 Yang (2010) Xiaosong Yang. 2010. Climate Data Assimilation. https://www.gfdl.noaa.gov/climate-data-assimilation/ Yao et al. (2019) Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaosong Yang. 2010. 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ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, and Dehai Zhu. 2019. Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens. 12, 1 (Dec. 2019), 62. https://doi.org/10.3390/rs12010062 Ye et al. (2023) Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. 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Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. 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Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. 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Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370
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Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Huayuan Ye, Chenhui Li, Yang Li, and Changbo Wang. 2023. InvVis: Large-Scale Data Embedding for Invertible Visualization. IEEE Trans. Vis. Comput. Graphics PP (2023), 1–11. Issue 99. https://doi.org/10.1109/tvcg.2023.3326597 arXiv:2307.16176 Yi Gu and Chaoli Wang (2011) Yi Gu and Chaoli Wang. 2011. TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data. IEEE Trans. Vis. Comput. Graphics 17, 12 (Dec. 2011), 2015–2024. https://doi.org/10.1109/TVCG.2011.246 Zhang et al. (2021) Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. 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Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370 Peiying Zhang, Chenhui Li, and Changbo Wang. 2021. VisCode: Embedding Information in Visualization Images Using Encoder-Decoder Network. IEEE Trans. Vis. Comput. Graphics 27 (2021), 326–336. Issue 2. https://doi.org/10.1109/TVCG.2020.3030343 arXiv:2009.03817 Zhou and Chiang (2018) Bo Zhou and Yi-Jen Chiang. 2018. Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard. Comput. Graph. Forum 37, 3 (June 2018), 37–49. https://doi.org/10.1111/cgf.13399 Zhou et al. (2023) Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, and Wei Chen. 2023. A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks. IEEE Trans. Human-Mach. Syst. (2023), 1–10. https://doi.org/10.1109/THMS.2023.3296692 Ziegler and Chasins (2023) Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. 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  71. Parker Ziegler and Sarah E. Chasins. 2023. A Need-Finding Study with Users of Geospatial Data. In Proc. 2023 CHI Conf. Human Factors Comput. Syst. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3581370

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