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.

翻译：在近期加沙和乌克兰发生的极具破坏性的冲突背景下，可靠的建筑损毁评估对于知情的公共讨论、人权监测及人道主义援助至关重要。鉴于冲突损害评估具有争议性，这些评估必须完全可复现、可解释，并基于开放获取数据。本文提出了一种满足这些条件的建筑损毁检测新方法——逐像素t检验（PWTT）。该方法结合免费合成孔径雷达影像与统计变化检测技术，可按固定时间间隔在大范围内生成准确的冲突损害评估结果。精度验证使用了覆盖乌克兰、巴勒斯坦、叙利亚和伊拉克12个城市的超过50万个标记建筑足迹原始数据集。尽管该算法简单轻量，其建筑级精度统计指标（乌克兰AUC=0.88，加沙0.81）可与采用深度学习与高分辨率影像的最先进方法相媲美。该工作流程完全开源并部署在谷歌地球引擎环境中，可生成乌克兰与加沙地区近乎实时更新的交互式战损仪表板，使公众和人道主义工作者能够立即获取特定区域的受损建筑评估结果。