An open-source tool for mapping war destruction at scale in Ukraine using Sentinel-1 time series
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 presen...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Communications Earth & Environment |
| Online Access: | https://doi.org/10.1038/s43247-025-02183-7 |
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| author | Olivier Dietrich Torben Peters Vivien Sainte Fare Garnot Valerie Sticher Thao Ton-That Whelan Konrad Schindler Jan Dirk Wegner |
| author_facet | Olivier Dietrich Torben Peters Vivien Sainte Fare Garnot Valerie Sticher Thao Ton-That Whelan Konrad Schindler Jan Dirk Wegner |
| author_sort | Olivier Dietrich |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-67000a415fbb46edae976bccea2ae672 |
| institution | DOAJ |
| issn | 2662-4435 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Earth & Environment |
| spelling | doaj-art-67000a415fbb46edae976bccea2ae6722025-08-20T02:41:36ZengNature PortfolioCommunications Earth & Environment2662-44352025-03-016111010.1038/s43247-025-02183-7An open-source tool for mapping war destruction at scale in Ukraine using Sentinel-1 time seriesOlivier Dietrich0Torben Peters1Vivien Sainte Fare Garnot2Valerie Sticher3Thao Ton-That Whelan4Konrad Schindler5Jan Dirk Wegner6Photogrammetry and Remote Sensing, ETH ZurichPhotogrammetry and Remote Sensing, ETH ZurichEcoVision Lab, Department of Mathematical Modeling and Machine Learning, University of ZurichDepartment of Political Science, University of ZurichInternational Committee of the Red CrossPhotogrammetry and Remote Sensing, ETH ZurichEcoVision Lab, Department of Mathematical Modeling and Machine Learning, University of ZurichAbstract 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.https://doi.org/10.1038/s43247-025-02183-7 |
| spellingShingle | Olivier Dietrich Torben Peters Vivien Sainte Fare Garnot Valerie Sticher Thao Ton-That Whelan Konrad Schindler Jan Dirk Wegner An open-source tool for mapping war destruction at scale in Ukraine using Sentinel-1 time series Communications Earth & Environment |
| title | An open-source tool for mapping war destruction at scale in Ukraine using Sentinel-1 time series |
| title_full | An open-source tool for mapping war destruction at scale in Ukraine using Sentinel-1 time series |
| title_fullStr | An open-source tool for mapping war destruction at scale in Ukraine using Sentinel-1 time series |
| title_full_unstemmed | An open-source tool for mapping war destruction at scale in Ukraine using Sentinel-1 time series |
| title_short | An open-source tool for mapping war destruction at scale in Ukraine using Sentinel-1 time series |
| title_sort | open source tool for mapping war destruction at scale in ukraine using sentinel 1 time series |
| url | https://doi.org/10.1038/s43247-025-02183-7 |
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