Unsupervised Detection of Building Destruction During War From Publicly Available Radar Satellite Imagery

MCML Authors

Xiaoxiang Zhu

Prof. Dr.

Principal Investigator

Data Science in Earth Observation

Abstract

The timely automated detection of building destruction in conflict zones is crucial for human rights monitoring, humanitarian response, and academic research. However, current approaches rely on expensive proprietary satellite imagery, limiting their scalability and accessibility. This study addresses these challenges by introducing an automated and unsupervised method that uses freely available Sentinel-1 synthetic aperture radar (SAR) imagery from the European Space Agency (ESA). By statistically assessing interferometric coherence changes over time, our approach enables the timely detection of building destruction at scale without requiring labeled training data, which are often not available in conflict-affected regions. We validate our method across three case studies, Beirut, Mariupol, and Gaza, demonstrating its ability to capture diverse patterns of destruction and their spatio-temporal dynamics, despite the moderate resolution of Sentinel-1 imagery. Our approach offers a scalable, global, and cost-effective solution for detecting building destruction in conflict zones.

misc

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Preprint

Feb. 2025

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Authors

D. Racek • Q. Zhang • P. Thurner • X. Zhu • G. Kauermann

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Links

DOI

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Research Area

 C3 | Physics and Geo Sciences