Scalable Methods for Brick Kiln Detection and Compliance Monitoring from Satellite Imagery: A Deployment Case Study in India (2402.13796v1)
Abstract: Air pollution kills 7 million people annually. Brick manufacturing industry is the second largest consumer of coal contributing to 8%-14% of air pollution in Indo-Gangetic plain (highly populated tract of land in the Indian subcontinent). As brick kilns are an unorganized sector and present in large numbers, detecting policy violations such as distance from habitat is non-trivial. Air quality and other domain experts rely on manual human annotation to maintain brick kiln inventory. Previous work used computer vision based machine learning methods to detect brick kilns from satellite imagery but they are limited to certain geographies and labeling the data is laborious. In this paper, we propose a framework to deploy a scalable brick kiln detection system for large countries such as India and identify 7477 new brick kilns from 28 districts in 5 states in the Indo-Gangetic plain. We then showcase efficient ways to check policy violations such as high spatial density of kilns and abnormal increase over time in a region. We show that 90% of brick kilns in Delhi-NCR violate a density-based policy. Our framework can be directly adopted by the governments across the world to automate the policy regulations around brick kilns.
- Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. 1357–1366.
- Generating interpretable poverty maps using object detection in satellite images. arXiv preprint arXiv:2002.01612 (2020).
- Efficient poverty mapping from high resolution remote sensing images. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 12–20.
- camx. 2019. camx. https://www.camx.com/. [Online; accessed 02-December-2024].
- Real-time tropical cyclone intensity estimation by handling temporally heterogeneous satellite data. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 14721–14728.
- A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597–1607.
- Superpixels and graph convolutional neural networks for efficient detection of nutrient deficiency stress from aerial imagery. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2950–2959.
- Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition. Ieee, 248–255.
- Sarath Guttikunda and KA Nishadh. 2022. Evolution of India’s PM 2.5 pollution between 1998 and 2020 using global reanalysis fields coupled with satellite observations and fuel consumption patterns. Environmental Science: Atmospheres 2, 6 (2022), 1502–1515.
- Air quality, emissions, and source contributions analysis for the Greater Bengaluru region of India. Atmospheric Pollution Research 10, 3 (2019), 941–953.
- Deep Residual Learning for Image Recognition. arXiv:1512.03385 [cs.CV]
- Densely Connected Convolutional Networks. arXiv:1608.06993 [cs.CV]
- What makes ImageNet good for transfer learning? arXiv preprint arXiv:1608.08614 (2016).
- Combining satellite imagery and machine learning to predict poverty. Science 353, 6301 (2016), 790–794.
- Scalable deep learning to identify brick kilns and aid regulatory capacity. Proceedings of the National Academy of Sciences 118, 17 (2021), e2018863118.
- Brick kiln detection in north India with sentinel imagery using deep learning of small datasets. In Proc. 40th Asian Conf. Remote Sens. 2594–2601.
- Detection of Illegal Kiln Activity During SMOG Period. In 2023 International Conference on Robotics and Automation in Industry (ICRAI). IEEE, 1–6.
- Kiln-net: A gated neural network for detection of brick kilns in South Asia. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13 (2020), 3251–3262.
- Mehdi Noroozi and Paolo Favaro. 2016. Unsupervised learning of visual representations by solving jigsaw puzzles. In European conference on computer vision. Springer, 69–84.
- Assessment of air pollutant emissions from brick kilns. Atmospheric Environment 98 (2014), 549–553. https://doi.org/10.1016/j.atmosenv.2014.08.075
- Multi3net: segmenting flooded buildings via fusion of multiresolution, multisensor, and multitemporal satellite imagery. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 702–709.
- Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. International Journal of Computer Vision 128, 2 (oct 2019), 336–359. https://doi.org/10.1007/s11263-019-01228-7
- Grad-CAM: Why did you say that? arXiv preprint arXiv:1611.07450 (2016).
- Mingxing Tan and Quoc V. Le. 2020. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv:1905.11946 [cs.LG]
- UNEP. 2019. Emissions Gap Report 2019. , 11 pages.
- WorldBank. 2020. DIRTY STACKS, HIGH STAKES: An Overview of Brick Sector in South Asia.
- Predicting forest fire using remote sensing data and machine learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 14983–14990.
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