  Industrial air pollution has a direct health impact and is a major
contributor to climate change. Small scale industries particularly bull-trench
brick kilns are one of the key sources of air pollution in South Asia often
creating hazardous levels of smog that is injurious to human health. To
mitigate the climate and health impact of the kiln industry, fine-grained kiln
localization at different geographic locations is needed. Kiln localization
using multi-spectral remote sensing data such as vegetation indices can result
in a noisy estimates whereas relying solely on high-resolution imagery is
infeasible due to cost and compute complexities. This paper proposes a fusion
of spatio-temporal multi-spectral data with high-resolution imagery for
detection of brick kilns within the "Brick-Kiln-Belt" of South Asia. We first
perform classification using low-resolution spatio-temporal multi-spectral data
from Sentinel-2 imagery by combining vegetation, burn, build up and moisture
indices. Next, orientation aware object detector YOLOv3 (with theta value) is
implemented for removal of false detections and fine-grained localization. Our
proposed technique, when compared with other benchmarks, results in a 21 times
improvement in speed with comparable or higher accuracy when tested over
multiple countries.
