Open access battle damage detection via Pixel-Wise T-Test on Sentinel-1 imagery

In the context of recent, highly destructive conflicts in Gaza and Ukraine, reliable estimates of building damage are essential for an informed public discourse, human rights monitoring, and humanitarian aid provision. Given the contentious nature of conflict damage assessment, these estimates must be fully reproducible , explainable, and derived from open access data. This paper introduces a new method for building damage detection– the Pixel-Wise T-Test (PWTT)– that satisfies these conditions. Using a combination of freely-available synthetic aperture radar imagery and statistical change detection, the PWTT generates accurate conflict damage estimates across a wide area at regular time intervals. Accuracy is assessed using an original dataset of over 2 million labeled building footprints spanning 30 cities across Palestine, Ukraine, Sudan, Syria, and Iraq. Despite being simple and lightweight, the algorithm achieves building-level accuracy statistics (AUC=0.87 in the full sample) rivaling state of the art methods that use deep learning and high resolution imagery. The workflow is open source and deployed entirely within the Google Earth Engine environment, allowing for the generation of interactive Battle Damage Dashboards for Ukraine and Gaza that update in near-real time, enabling the public and humanitarian practitioners to immediately get estimates of damaged buildings in a given area. • A new algorithm for war-induced building damage detection is developed. • The workflow is fully open source and requires no training. • Accuracy is assessed using >2 million labeled building footprints across 30 cities. • Despite being unsupervised, accuracy rivals that of deep learning-based methods. • These properties enable near-real time, wide area conflict damage monitoring.