Hamdani Muhammad Suleman Ali, Zakir Khizer, Kushwaha Neetu, Fatima Syeda Eman, Sheikh Hassan Aftab
School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad, 46000, Pakistan.
Department of Geoinformatics - Z_GIS, University of Salzburg, 5020, Salzburg, Austria.
Sci Data. 2025 May 20;12(1):830. doi: 10.1038/s41597-025-05148-9.
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.
砖窑是巴基斯坦空气污染的主要来源，许多砖窑在无监管的情况下运营。在巴基斯坦以及整个印度-恒河平原，一个关键挑战是空气质量监测有限，且缺乏关于污染源的透明数据。为解决这一问题，我们提出了一种双重人工智能方法，该方法结合了低分辨率的哨兵2号卫星图像和高分辨率图像来绘制砖窑位置。我们的流程始于低分辨率分析，随后是一个后处理步骤，以减少误报，从而将对大量高分辨率图像的需求降至最低。该分析最初识别出20000个潜在砖窑，通过高分辨率验证确认了约11000个砖窑。该数据集还区分了固定烟囱窑和之字形窑，能够对每种类型进行更准确的污染估计。我们的方法展示了如何将卫星图像与人工智能相结合，有效地检测特定污染源。该数据集为监管机构提供了有关砖窑污染的见解，支持对未注册砖窑的干预以及在高污染事件期间采取的行动。
