Brick Kiln Dataset for Pakistan's IGP Region Using AI
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arXiv
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| Format: | Preprint |
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2024
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| author | Hamdani, Muhammad Suleman Ali Zakir, Khizer Kushwaha, Neetu Fatima, Syeda Eman Sheikh, Hassan Aftab |
| author_facet | Hamdani, Muhammad Suleman Ali Zakir, Khizer Kushwaha, Neetu Fatima, Syeda Eman Sheikh, Hassan Aftab |
| contents | Brick kilns are a major source of air pollution in Pakistan, with many operating without regulation. A key challenge in Pakistan and across the Indo-Gangetic Plain is the limited air quality monitoring and lack of transparent data on pollution sources. To address this, we present a two-fold AI approach that combines low-resolution Sentinel-2 and high-resolution imagery to map brick kiln locations. Our process begins with a low-resolution analysis, followed by a post-processing step to reduce false positives, minimizing the need for extensive high-resolution imagery. This analysis initially identified 20,000 potential brick kilns, with high-resolution validation confirming around 11,000 kilns. The dataset also distinguishes between Fixed Chimney and Zigzag kilns, enabling more accurate pollution estimates for each type. Our approach demonstrates how combining satellite imagery with AI can effectively detect specific polluting sources. This dataset provides regulators with insights into brick kiln pollution, supporting interventions for unregistered kilns and actions during high pollution episodes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00052 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Brick Kiln Dataset for Pakistan's IGP Region Using AI Hamdani, Muhammad Suleman Ali Zakir, Khizer Kushwaha, Neetu Fatima, Syeda Eman Sheikh, Hassan Aftab Computer Vision and Pattern Recognition Brick kilns are a major source of air pollution in Pakistan, with many operating without regulation. A key challenge in Pakistan and across the Indo-Gangetic Plain is the limited air quality monitoring and lack of transparent data on pollution sources. To address this, we present a two-fold AI approach that combines low-resolution Sentinel-2 and high-resolution imagery to map brick kiln locations. Our process begins with a low-resolution analysis, followed by a post-processing step to reduce false positives, minimizing the need for extensive high-resolution imagery. This analysis initially identified 20,000 potential brick kilns, with high-resolution validation confirming around 11,000 kilns. The dataset also distinguishes between Fixed Chimney and Zigzag kilns, enabling more accurate pollution estimates for each type. Our approach demonstrates how combining satellite imagery with AI can effectively detect specific polluting sources. This dataset provides regulators with insights into brick kiln pollution, supporting interventions for unregistered kilns and actions during high pollution episodes. |
| title | Brick Kiln Dataset for Pakistan's IGP Region Using AI |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.00052 |