GloSoFarID: Global multispectral dataset for Solar Farm IDentification in satellite imagery (2404.05180v2)
Abstract: Solar Photovoltaic (PV) technology is increasingly recognized as a pivotal solution in the global pursuit of clean and renewable energy. This technology addresses the urgent need for sustainable energy alternatives by converting solar power into electricity without greenhouse gas emissions. It not only curtails global carbon emissions but also reduces reliance on finite, non-renewable energy sources. In this context, monitoring solar panel farms becomes essential for understanding and facilitating the worldwide shift toward clean energy. This study contributes to this effort by developing the first comprehensive global dataset of multispectral satellite imagery of solar panel farms. This dataset is intended to form the basis for training robust machine learning models, which can accurately map and analyze the expansion and distribution of solar panel farms globally. The insights gained from this endeavor will be instrumental in guiding informed decision-making for a sustainable energy future. https://github.com/yzyly1992/GloSoFarID
- V. Fthenakis, “Sustainability of photovoltaics: The case for thin-film solar cells,” Renewable and Sustainable Energy Reviews, vol. 13, no. 9, pp. 2746–2750, 2009.
- P. Choudhary and R. K. Srivastava, “Sustainability perspectives-a review for solar photovoltaic trends and growth opportunities,” Journal of Cleaner Production, vol. 227, pp. 589–612, 2019.
- L. Kruitwagen, K. Story, J. Friedrich, L. Byers, S. Skillman, and C. Hepburn, “A global inventory of photovoltaic solar energy generating units,” Nature, vol. 598, no. 7882, pp. 604–610, 2021.
- M. Drusch, U. Del Bello, S. Carlier, O. Colin, V. Fernandez, F. Gascon, B. Hoersch, C. Isola, P. Laberinti, P. Martimort et al., “Sentinel-2: Esa’s optical high-resolution mission for gmes operational services,” Remote sensing of Environment, vol. 120, pp. 25–36, 2012.
- M. M. Khomiakov, J. Radzikowski, C. Schmidt, M. Sørensen, M. Andersen, M. Andersen, and J. Frellsen, “Solardk: A high-resolution urban solar panel image classification and localization dataset,” in NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning, 2022. [Online]. Available: https://www.climatechange.ai/papers/neurips2022/3
- P. Parhar, R. Sawasaki, A. Todeschini, C. Reed, H. Vahabi, N. Nusaputra, and F. Vergara, “Hyperionsolarnet: Solar panel detection from aerial images,” in NeurIPS 2021 Workshop on Tackling Climate Change with Machine Learning, 2021. [Online]. Available: https://www.climatechange.ai/papers/neurips2021/41
- J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 3431–3440.
- H. Lu, Y. She, J. Tie, and S. Xu, “Half-unet: A simplified u-net architecture for medical image segmentation,” Frontiers in Neuroinformatics, vol. 16, p. 911679, 2022.
- O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer, 2015, pp. 234–241.