  Access to detailed war impact assessments is crucial for humanitarian
organizations to effectively assist populations most affected by armed
conflicts. However, maintaining a comprehensive understanding of the situation
on the ground is challenging, especially in conflicts that cover vast
territories and extend over long periods. This study presents a scalable and
transferable method for estimating war-induced damage to buildings. We first
train a machine learning model to output pixel-wise probability of destruction
from Synthetic Aperture Radar (SAR) satellite image time series, leveraging
existing, manual damage assessments as ground truth and cloud-based geospatial
analysis tools for large-scale inference. We further post-process these
assessments using open building footprints to obtain a final damage estimate
per building. We introduce an accessible, open-source tool that allows users to
adjust the confidence interval based on their specific requirements and use
cases. Our approach enables humanitarian organizations and other actors to
rapidly screen large geographic regions for war impacts. We provide two
publicly accessible dashboards: a Ukraine Damage Explorer to dynamically view
our pre-computed estimates, and a Rapid Damage Mapping Tool to easily run our
method and produce custom maps.
