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Industrial air pollution has a direct health impact and is a majorcontributor to climate change. Small scale industries particularly bull-trenchbrick kilns are one of the key sources of air pollution in South Asia oftencreating hazardous levels of smog that is injurious to human health. Tomitigate the climate and health impact of the kiln industry, fine-grained kilnlocalization at different geographic locations is needed. Kiln localizationusing multi-spectral remote sensing data such as vegetation indices can resultin a noisy estimates whereas relying solely on high-resolution imagery isinfeasible due to cost and compute complexities. This paper proposes a fusionof spatio-temporal multi-spectral data with high-resolution imagery fordetection of brick kilns within the "Brick-Kiln-Belt" of South Asia. We firstperform classification using low-resolution spatio-temporal multi-spectral datafrom Sentinel-2 imagery by combining vegetation, burn, build up and moistureindices. Next, orientation aware object detector YOLOv3 (with theta value) isimplemented for removal of false detections and fine-grained localization. Ourproposed technique, when compared with other benchmarks, results in a 21 timesimprovement in speed with comparable or higher accuracy when tested overmultiple countries.

Mitigating climate and health impact of small-scale kiln industry using multi-spectral classifier and deep learning