Monitoring war destruction from space using machine learning
Mueller, Hannes (Institut d'Anàlisi Econòmica)
Gröger, Andre (Universitat Autònoma de Barcelona. Departament d'Economia i d'Història Econòmica)
Hersh, Jonathan (Chapman University. Argyros School of Business)
Matranga, Andrea (Chapman University. Argyros School of Business)
Serrat Gual, Joan (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)

Date:	2021
Abstract:	Satellite imagery is becoming ubiquitous and is released with ever higher frequency. Research has demonstrated that Artificial Intelligence (AI) applied to satellite imagery holds promise for automated detection of war-related building destruction. While these results are promising, monitoring in real-world applications requires consistently high precision, especially when destruction is sparse and detecting destroyed buildings is equivalent to looking for a needle in a haystack. We demonstrate that exploiting the persistent nature of building destruction can substantially improve the training of automated destruction monitoring. We also propose an additional machine learning stage that leverages images of surrounding areas and multiple successive images of the same area which further improves detection significantly. By combining these steps, we construct an automated classification of building destruction which allows real-world applications and we illustrate this in the context of the Syrian civil war.
Grants:	Ministerio de Ciencia e Innovación CEX2019-000915-S Ministerio de Ciencia e Innovación PGC-096133-B-100 Ministerio de Ciencia e Innovación PGC2018-094364-B-100
Note:	Altres ajuts: "la Caixa" Foundation project grant number CG-2017-04, title: "Analysing Conflict from Space"
Rights:	Tots els drets reservats.
Language:	Anglès
Document:	Article ; recerca ; Versió acceptada per publicar
Subject:	Conflict ; Destruction ; Deep Learning ; Remote Sensing ; Syria
Published in:	Proceedings of the National Academy of Sciences of the United States of America, Vol. 118, Issue 23 (June 2021) , art. e2025400118, ISSN 1091-6490

DOI: 10.1073/pnas.2025400118
PMID: 34083439

Postprint 14 p, 5.7 MB

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