  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 half a million labeled building footprints spanning 12 cities across
Ukraine, Palestine, Syria, and Iraq. Despite being simple and lightweight, the
algorithm achieves building-level accuracy statistics (AUC=0.88 across Ukraine,
0.81 in Gaza) rivalling 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, allowing the public and humanitarian practitioners to
immediately get estimates of damaged buildings in a given area.
