  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.
