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

arXiv: https://arxiv.org/abs/2406.02506 · HTML: https://mlsanity.com/paper/2406.02506/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 61 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 7 of 7)

- [xBD: A Dataset for Assessing Building Damage from Satellite Imagery](https://mlsanity.com/paper/1911.09296/) (2019)
- [Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks](https://mlsanity.com/paper/1910.06444/) (2019)
- [Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images](https://mlsanity.com/paper/2208.02205/) (2022)
- [Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniques](https://mlsanity.com/paper/2011.14004/) (2020)
- [Deep Learning-based Damage Mapping with InSAR Coherence Time Series](https://mlsanity.com/paper/2105.11544/) (2021)
- [Monitoring War Destruction from Space: A Machine Learning Approach](https://mlsanity.com/paper/2010.05970/) (2020)
- [Open Access Battle Damage Detection via Pixel-Wise T-Test on Sentinel-1 Imagery](https://mlsanity.com/paper/2405.06323/) (2024)

## Built on by (most cited first, 3 of 3)

- [BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response](https://mlsanity.com/paper/2501.06019/) (2025)
- [The Potential of Copernicus Satellites for Disaster Response: Retrieving Building Damage from Sentinel-1 and Sentinel-2](https://mlsanity.com/paper/2511.05461/) (2025)
- [A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake](https://mlsanity.com/paper/2506.22338/) (2025)

## Abstract (arXiv)

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.