An Open-Source Tool for Mapping War Destruction at Scale in Ukraine using Sentinel-1 Time Series

TL;DR

This paper introduces an open-source, scalable machine learning approach using Sentinel-1 SAR time series data to map and assess building damage caused by war in Ukraine, accessible via Google Earth Engine.

Contribution

It presents a novel method combining SAR time series analysis with probabilistic damage estimation, integrated into accessible dashboards for large-scale war impact assessment.

Findings

Effective damage mapping at building level in Ukraine

Open-source tools enable rapid, large-scale assessments

Flexible confidence intervals support diverse user needs

Abstract

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.