Monitoring war destruction from space using machine learning

doi:10.1073/pnas.2025400118

Monitoring war destruction from space using machine learning

2021-06-03

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DOI：10.1073/pnas.2025400118

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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.

Keywords:

conflict

destruction

deep learning

remote sensing

Syria

Monitoring war destruction from space using machine learning

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