Tackling Climate Change with Machine Learning workshop at ICLR
2023
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 major causes 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 index results in a
noisy estimates whereas use of 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. Then
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 21x improvement in
speed with comparable or higher accuracy when tested over multiple countries.
Usman Nazir, Murtaza Taj, Momin Uppal, Sara Khalid
2023-03-21
