Unsupervised detection of building destruction during war from publicly available radar satellite imagery.

Automated detection of building destruction in conflict zones is crucial for human rights monitoring, humanitarian response, and academic research. However, existing approaches (i) rely on proprietary satellite imagery, both expensive and of limited availability at wartime, (ii) require labeled training data, usually not available in war-affected regions, or (iii) use optical imagery, regularly obstructed by cloud cover. This study addresses these challenges by introducing an unsupervised method to detect destruction at the building level using freely and globally available Sentinel-1 synthetic aperture radar images from the European Space Agency. By statistically assessing interferometric coherence changes over time, unlike existing approaches, our method enables the detection of destruction from a single satellite image, allowing for near real-time destruction assessments every 12 days. We provide a continuous, statistically grounded probability measure for the likelihood of destruction at both the building and pixel level, thereby quantifying the level of uncertainty of the detection. Using ground truth data and reported sequences of events, we validate our approach both quantitatively and qualitatively, across three case studies in Beirut, Mariupol, and Gaza, demonstrating its ability to accurately identify the spatial patterns and timing of destruction events. Using open-access data, our method offers a scalable, global, and cost-effective solution for monitoring building destruction in conflict zones.