Scalable Methods for Brick Kiln Detection and Compliance Monitoring from Satellite Imagery: A Deployment Case Study in India
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917594953416704 |
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| author | Mondal, Rishabh Patel, Zeel B Jani, Vannsh Batra, Nipun |
| author_facet | Mondal, Rishabh Patel, Zeel B Jani, Vannsh Batra, Nipun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13796 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scalable Methods for Brick Kiln Detection and Compliance Monitoring from Satellite Imagery: A Deployment Case Study in India Mondal, Rishabh Patel, Zeel B Jani, Vannsh Batra, Nipun Computer Vision and Pattern Recognition 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. |
| title | Scalable Methods for Brick Kiln Detection and Compliance Monitoring from Satellite Imagery: A Deployment Case Study in India |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.13796 |