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

TL;DR

This paper introduces a fusion of multi-spectral and high-resolution imagery with deep learning to efficiently localize brick kilns in South Asia, aiming to reduce their climate and health impacts.

Contribution

It proposes a novel method combining multi-spectral data and YOLOv3 for accurate, fast kiln detection, improving speed significantly over existing benchmarks.

Findings

21 times faster detection compared to benchmarks

Achieved high accuracy in kiln localization

Effective in multiple South Asian countries

Abstract

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.