气候变化领域集成服务门户(CCPortal): Scalable deep learning to identify brick kilns and aid regulatory capacity

CCPortal

DOI	10.1073/pnas.2018863118
	Scalable deep learning to identify brick kilns and aid regulatory capacity
	Lee J.; Brooks N.R.; Tajwar F.; Burke M.; Ermon S.; Lobell D.B.; Biswas D.; Luby S.P.
发表日期	2021
ISSN	00278424
卷号	118 期号:17
英文摘要	Improving compliance with environmental regulations is critical for promoting clean environments and healthy populations. In South Asia, brick manufacturing is a major source of pollution but is dominated by small-scale, informal producers who are difficult to monitor and regulate—a common challenge in low-income settings. We demonstrate a low-cost, scalable approach for locating brick kilns in high-resolution satellite imagery from Bangladesh. Our approach identifies kilns with 94.2% accuracy and 88.7% precision and extracts the precise GPS coordinates of every brick kiln across Bangladesh. Using these estimates, we show that at least 12% of the population of Bangladesh (>18 million people) live within 1 km of a kiln and that 77% and 9% of kilns are (illegally) within 1 km of schools and health facilities, respectively. Finally, we show how kilns contribute up to 20.4 µg/m3 of PM2.5 (particulate matter of a diameter less than 2.5 µm) in Dhaka when the wind blows from an unfavorable direction. We document inaccuracies and potential bias with respect to local regulations in the government data. Our approach demonstrates how machine learning and Earth observation can be combined to better understand the extent and implications of regulatory compliance in informal industry. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Air pollution; Bangladesh; Deep learning; Environmental regulations; Satellite imagery
语种	英语
scopus关键词	article; Bangladesh; deep learning; government; health care facility; human; human experiment; particulate matter 2.5; satellite imagery
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179714
作者单位	Computer Science Department, Stanford University, Stanford, CA 94305, United States; Emmett Interdisciplinary Program in Environment and Resources, Stanford University, Stanford, CA 94305, United States; Woods Institute for the Environment, Stanford University, Stanford, CA 94305, United States; Department of Earth System Science, Stanford University, Stanford, CA 94305, United States; International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, 1212, Bangladesh; Center for Innovation in Global Health, Stanford University, Stanford, CA 94305, United States
推荐引用方式 GB/T 7714	Lee J.,Brooks N.R.,Tajwar F.,et al. Scalable deep learning to identify brick kilns and aid regulatory capacity[J],2021,118(17).
APA	Lee J..,Brooks N.R..,Tajwar F..,Burke M..,Ermon S..,...&Luby S.P..(2021).Scalable deep learning to identify brick kilns and aid regulatory capacity.Proceedings of the National Academy of Sciences of the United States of America,118(17).
MLA	Lee J.,et al."Scalable deep learning to identify brick kilns and aid regulatory capacity".Proceedings of the National Academy of Sciences of the United States of America 118.17(2021).