Monitoring war destruction from space using machine learning
Gröger, Andre (Universitat Autònoma de Barcelona)
Mueller, Hannes (Barcelona School of Economics (BSE))
Hersh, Jonathan (Chapman University)
Matranga, Andrea (Chapman University)
Serrat, Joan (Universitat Autònoma de Barcelona)

Publicación:	Barcelona: GSE Barcelona Graduate School of Economics, 2021
Descripción:	14 pàg.
Resumen:	Satellite imagery is becoming ubiquitous and is released with ever higher frequency. Research hasdemonstrated that Artificial Intelligence (AI) applied to satellite imagery holds promise for automateddetection of war-related building destruction. While these results are promising, monitoring inreal-world applications requires consistently high precision, especially when destruction is sparseand detecting destroyed buildings is equivalent to looking for a needle in a haystack. We demonstratethat exploiting the persistent nature of building destruction can substantially improve the trainingof automated destruction monitoring. We also propose an additional machine learning stage thatleverages images of surrounding areas and multiple successive images of the same area which furtherimproves detection significantly. By combining these steps, we construct an automated classificationof building destruction which allows real-world applications and we illustrate this in the context ofthe Syrian civil war.
Ayudas:	"la Caixa" Foundation CG-2017-04 Ministerio de Ciencia e Innovación CEX2019-000915-S Agencia Estatal de Investigación PGC2018-096133-B-I00 Agencia Estatal de Investigación PGC2018-094364-B-I00
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Lengua:	Anglès
Colección:	GSE Working papers 1257
Documento:	Working paper ; recerca ; Versió publicada
Materia:	Conflict ; Deep learning ; Destruction ; Remote sensing ; Syria
Publicado en:	GSE Barcelona Graduate School of Economics Working Papers, 2021

Adreça alternativa: https://bse.eu/research/working-papers/monitoring-war-destruction-space-using-machine-learning

14 p, 5.6 MB

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