An Open-Source Tool for Mapping War Destruction at Scale in Ukraine using Sentinel-1 Time Series
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916622074118144 |
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| author | Dietrich, Olivier Peters, Torben Garnot, Vivien Sainte Fare Sticher, Valerie Whelan, Thao Ton-That Schindler, Konrad Wegner, Jan Dirk |
| author_facet | Dietrich, Olivier Peters, Torben Garnot, Vivien Sainte Fare Sticher, Valerie Whelan, Thao Ton-That Schindler, Konrad Wegner, Jan Dirk |
| contents | 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 | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02506 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Open-Source Tool for Mapping War Destruction at Scale in Ukraine using Sentinel-1 Time Series Dietrich, Olivier Peters, Torben Garnot, Vivien Sainte Fare Sticher, Valerie Whelan, Thao Ton-That Schindler, Konrad Wegner, Jan Dirk Computer Vision and Pattern Recognition 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. |
| title | An Open-Source Tool for Mapping War Destruction at Scale in Ukraine using Sentinel-1 Time Series |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.02506 |