Monitoring War Destruction from Space: A Machine Learning Approach

• URL: http://arxiv.org/abs/2010.05970v2
• Date: Wed, 14 Oct 2020 03:47:28 GMT
• Title: Monitoring War Destruction from Space: A Machine Learning Approach
• Authors: Hannes Mueller, Andre Groger, Jonathan Hersh, Andrea Matranga and Joan Serrat
• Abstract summary: Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep learning techniques. We apply this method to the Syrian civil war and the evolution of damage in major cities across the country.
• Score: 1.0149624140985478
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 data augmentation to expand training samples. We apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. The approach allows generating destruction data with unprecedented scope, resolution, and frequency - only limited by the available satellite imagery - which can alleviate data limitations decisively.

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