气候变化领域集成服务门户(CCPortal): Monitoring war destruction from space using machine learning

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DOI	10.1073/pnas.2025400118
	Monitoring war destruction from space using machine learning
	Mueller H.; Groeger A.; Hersh J.; Matranga A.; Serrat J.
发表日期	2021
ISSN	0027-8424
卷号	118 期号:23
英文摘要	Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete, and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human-rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep-learning techniques combined with label augmentation and spatial and temporal smoothing, which exploit the underlying spatial and temporal structure of destruction. As a proof of concept, we apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. Our approach allows generating destruction data with unprecedented scope, resolution, and frequency-and makes use of the ever-higher frequency at which satellite imagery becomes available. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Conflict; Deep learning; Destruction; Remote sensing; Syria
语种	英语
scopus关键词	article; city; deep learning; destruction; human; human experiment; human rights; humanitarian aid; proof of concept; satellite imagery; Syrian Arab Republic; witness
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/238715
作者单位	Institute of Economic Analysis, Spanish National Research Council (CSIC), Bellaterra, 08193, Spain; Barcelona Graduate School of Economics, Barcelona, 08005, Spain; Department of Economics and Economic History, Universitat Autònoma de Barcelona, Bellaterra, 08193, Spain; Argyros School of Business, Chapman University, Orange, CA 92868, United States; Smith Institute for Political Economy and Philosophy, Chapman University, Orange, CA 92868, United States; Computer Science Department, Universitat Autònoma de Barcelona, Bellaterra, 08193, Spain; Computer Vision Center, Universitat Autònoma de Barcelona, Bellaterra, 08193, Spain
推荐引用方式 GB/T 7714	Mueller H.,Groeger A.,Hersh J.,et al. Monitoring war destruction from space using machine learning[J],2021,118(23).
APA	Mueller H.,Groeger A.,Hersh J.,Matranga A.,&Serrat J..(2021).Monitoring war destruction from space using machine learning.Proceedings of the National Academy of Sciences of the United States of America,118(23).
MLA	Mueller H.,et al."Monitoring war destruction from space using machine learning".Proceedings of the National Academy of Sciences of the United States of America 118.23(2021).