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.

翻译：空气污染每年导致700万人死亡。砖瓦制造业是第二大煤炭消费行业，在印度次大陆人口稠密的印度-恒河平原地区，其造成的空气污染占比达8%-14%。由于砖窑属于非正规部门且数量庞大，检测诸如与居住区距离等政策违规行为并非易事。空气质量及其他领域专家依赖人工标注来维护砖窑清单。以往研究采用基于计算机视觉的机器学习方法从卫星图像中检测砖窑，但这些方法局限于特定地理区域，且数据标注工作劳动强度大。本文提出一个可扩展的砖窑检测系统框架，适用于印度等大国，并在印度-恒河平原5个邦28个区成功识别出7477个新砖窑。随后我们展示了高效检查政策违规的方法，例如区域砖窑高空间密度以及随时间异常增多。研究表明，德里国家首都辖区90%的砖窑违反了密度相关政策。我们的框架可直接被全球各国政府采用，以实现砖窑相关政策法规的自动化监管。