Access to detailed war impact assessments is crucial for humanitarian organizations to assist affected populations effectively. However, maintaining a comprehensive understanding of the situation on the ground is challenging, especially in widespread and prolonged conflicts. Here we present a scalable method for estimating building damage resulting from armed conflicts. By training a machine learning model on Synthetic Aperture Radar image time series, we generate probabilistic damage estimates at the building level, leveraging existing damage assessments and open building footprints. To allow large-scale inference and ensure accessibility, we tie our method to run on Google Earth Engine. Users can adjust confidence intervals to suit their needs, enabling rapid and flexible assessments of war-related damage across large areas. We provide two publicly accessible dashboards: a Ukraine Damage Explorer to dynamically view our precomputed estimates, and a Rapid Damage Mapping Tool to run our method and generate custom maps.

翻译：获取详细的战争影响评估对于人道主义组织有效援助受灾民众至关重要。然而，在广泛且持久的冲突中，保持对实地情况的全面了解具有挑战性。本文提出一种可扩展的方法，用于评估武装冲突导致的建筑物损毁。通过在合成孔径雷达图像时间序列上训练机器学习模型，我们结合现有损毁评估与开放建筑物足迹数据，生成了建筑物级别的概率性损毁估计。为实现大规模推断并确保可访问性，我们将该方法部署于Google Earth Engine平台运行。用户可根据需求调整置信区间，从而实现对广大区域战争相关损毁的快速灵活评估。我们提供了两个公开可访问的仪表板：用于动态查看预计算估计结果的“乌克兰损毁探索器”，以及用于运行本方法并生成定制地图的“快速损毁测绘工具”。