mlsanityData to 7 Oct 2026

The Potential of Copernicus Satellites for Disaster Response: Retrieving Building Damage from Sentinel-1 and Sentinel-2

arXiv 2511.05461 ↗First posted 7 Nov 2025cs.CVcited by 1

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Abstract (arXiv)

Natural disasters demand rapid damage assessment to guide humanitarian response. Here, we investigate whether medium-resolution Earth observation images from the Copernicus program can support building damage assessment, complementing very-high resolution imagery with often limited availability. We introduce xBD-S12, a dataset of 10,315 pre- and post-disaster image pairs from both Sentinel-1 and Sentinel-2, spatially and temporally aligned with the established xBD benchmark. In a series of experiments, we demonstrate that building damage can be detected and mapped rather well in many disaster scenarios, despite the moderate 10,m ground sampling distance. We also find that, for damage mapping at that resolution, architectural sophistication does not seem to bring much advantage: more complex model architectures tend to struggle with generalization to unseen disasters, and geospatial foundation models bring little practical benefit. Our results suggest that Copernicus images are a viable data source for rapid, wide-area damage assessment and could play an important role alongside VHR imagery. We release the xBD-S12 dataset, code, and trained models to support further research at https://github.com/prs-eth/xbd-s12 .

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 37 references that are in the map. First posted on arXiv 7 Nov 2025.

Builds on

17 of 37 references.

References · 17

  1. 2018Unified Perceptual Parsing for Scene Understandingcited by 1,265
  2. 2024Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applicationscited by 139
  3. 2019xBD: A Dataset for Assessing Building Damage from Satellite Imagerycited by 138
  4. 2024Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observationcited by 97
  5. 2024TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Datacited by 69
  6. 2025BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster responsecited by 39
  7. 2025DisasterM3: A Remote Sensing Vision-Language Dataset for Disaster Damage Assessment and Responsecited by 35
  8. 2019Building Damage Detection in Satellite Imagery Using Convolutional Neural Networkscited by 34
  9. 2020Building Disaster Damage Assessment in Satellite Imagery with Multi-Temporal Fusioncited by 27
  10. 2020RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagerycited by 24
  11. 2022Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Imagescited by 24
  12. 2025TerraTorch: The Geospatial Foundation Models Toolkitcited by 24
5 more
  1. 2018Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagerycited by 22
  2. 2022Dual-Tasks Siamese Transformer Framework for Building Damage Assessmentcited by 19
  3. 2023High-Resolution Building and Road Detection from Sentinel-2cited by 15
  4. 2020An Attention-Based System for Damage Assessment Using Satellite Imagerycited by 11
  5. 2024An Open-Source Tool for Mapping War Destruction at Scale in Ukraine using Sentinel-1 Time Seriescited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.