Articles | Volume 17, issue 11
https://doi.org/10.5194/essd-17-6217-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-6217-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
18 Nov 2025
Data description article |
| 18 Nov 2025
| 18 Nov 2025
Bright: a globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response
Hongruixuan Chen
Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
RIKEN Center for Advanced Intelligence Project (AIP), RIKEN, Tokyo, Japan
Jian Song
Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
RIKEN Center for Advanced Intelligence Project (AIP), RIKEN, Tokyo, Japan
Olivier Dietrich
Department of Photogrammetry and Remote Sensing, ETH Zürich, Zürich, Switzerland
Clifford Broni-Bediako
RIKEN Center for Advanced Intelligence Project (AIP), RIKEN, Tokyo, Japan
Weihao Xuan
Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
RIKEN Center for Advanced Intelligence Project (AIP), RIKEN, Tokyo, Japan
Junjue Wang
Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
Xinlei Shao
Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
Yimin Wei
Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
Junshi Xia
RIKEN Center for Advanced Intelligence Project (AIP), RIKEN, Tokyo, Japan
Cuiling Lan
Microsoft Research Asia, Beijing, China
Konrad Schindler
Department of Photogrammetry and Remote Sensing, ETH Zürich, Zürich, Switzerland
Naoto Yokoya
CORRESPONDING AUTHOR
Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
RIKEN Center for Advanced Intelligence Project (AIP), RIKEN, Tokyo, Japan
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Total article views: 5,615 (including HTML, PDF, and XML)
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Total article views: 4,548 (including HTML, PDF, and XML)
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Cited
21 citations as recorded by crossref.
- Building damage detection from multi-feature fusion of Sentinel-1/2 imagery using variational autoencoder and MLP-Mixer network: insights from the Jishishan earthquake J. Wang et al.
- Ultra-high-resolution SAR and optical image registration: From global benchmark dataset to frequency-guided registration method H. Yan et al.
- ChangeVFM: unleashing the power of vision foundation models for semantic change detection in remote sensing images H. Huang et al.
- Semantic change detection of roads and bridges: A fine-grained dataset and multimodal frequency-driven detector Q. Shu et al.
- Sample selection for remote sensing image change detection with unsupervised knowledge representation quantification L. Zhu et al.
- SAR-based individual-building damage identification and large-scale earthquake damage prediction H. Liu et al.
- DeltaVLM: Interactive Remote Sensing Image Change Analysis via Instruction-Guided Difference Perception P. Deng et al.
- RAM-CD: Dynamic Deformation Alignment With Rank-Guided Enhancement for Remote Sensing Image Change Detection J. Li et al.
- Multimodal Building Damage Assessment Method Fusing Adaptive Attention Mechanism and State-Space Modeling R. Zhu & X. Lan
- Optimized Reinforcement Learning-Driven Model for Remote Sensing Change Detection Y. Zhao et al.
- Map-Guided Cross-Training for Building Detection A. Zawadzka et al.
- A Deep Learning Framework for Building Damage Assessment Using VHR SAR and Geospatial Data: Demonstration on the 2023 Türkiye Earthquake L. Russo et al.
- FWDNNet: Cross-Heterogeneous Encoder Fusion via Feature-Level TensorDot Operations for Land-Cover Mapping B. Mwubahimana et al.
- Multimodal remote sensing change detection: An image matching perspective H. Chen et al.
- CD-Lamba: Boosting Remote Sensing Change Detection via a Cross-Temporal Locally Adaptive State Space Model Z. Wu et al.
- NF-SemiCD: semi-supervised remote sensing change detection with normalizing flows G. Yu et al.
- OpenEarthMap-SAR: A benchmark synthetic aperture radar dataset for global high-resolution land cover mapping [Software and Data Sets] J. Xia et al.
- RivAIrSet: A multitemporal high-resolution UAV imagery dataset for machine learning-based river water segmentation M. La Salandra et al.
- Lightweight Change Detection in Heterogeneous Remote Sensing Images With Online All-Integer Pruning Training C. Zhang et al.
- SARLANG-1M: A Benchmark for Vision–Language Modeling in SAR Image Understanding Y. Wei et al.
- Low-data cross-modal adaptation for remote sensing with proxy-enhanced multi-granularity feature caching Y. Sun et al.
21 citations as recorded by crossref.
- Building damage detection from multi-feature fusion of Sentinel-1/2 imagery using variational autoencoder and MLP-Mixer network: insights from the Jishishan earthquake J. Wang et al.
- Ultra-high-resolution SAR and optical image registration: From global benchmark dataset to frequency-guided registration method H. Yan et al.
- ChangeVFM: unleashing the power of vision foundation models for semantic change detection in remote sensing images H. Huang et al.
- Semantic change detection of roads and bridges: A fine-grained dataset and multimodal frequency-driven detector Q. Shu et al.
- Sample selection for remote sensing image change detection with unsupervised knowledge representation quantification L. Zhu et al.
- SAR-based individual-building damage identification and large-scale earthquake damage prediction H. Liu et al.
- DeltaVLM: Interactive Remote Sensing Image Change Analysis via Instruction-Guided Difference Perception P. Deng et al.
- RAM-CD: Dynamic Deformation Alignment With Rank-Guided Enhancement for Remote Sensing Image Change Detection J. Li et al.
- Multimodal Building Damage Assessment Method Fusing Adaptive Attention Mechanism and State-Space Modeling R. Zhu & X. Lan
- Optimized Reinforcement Learning-Driven Model for Remote Sensing Change Detection Y. Zhao et al.
- Map-Guided Cross-Training for Building Detection A. Zawadzka et al.
- A Deep Learning Framework for Building Damage Assessment Using VHR SAR and Geospatial Data: Demonstration on the 2023 Türkiye Earthquake L. Russo et al.
- FWDNNet: Cross-Heterogeneous Encoder Fusion via Feature-Level TensorDot Operations for Land-Cover Mapping B. Mwubahimana et al.
- Multimodal remote sensing change detection: An image matching perspective H. Chen et al.
- CD-Lamba: Boosting Remote Sensing Change Detection via a Cross-Temporal Locally Adaptive State Space Model Z. Wu et al.
- NF-SemiCD: semi-supervised remote sensing change detection with normalizing flows G. Yu et al.
- OpenEarthMap-SAR: A benchmark synthetic aperture radar dataset for global high-resolution land cover mapping [Software and Data Sets] J. Xia et al.
- RivAIrSet: A multitemporal high-resolution UAV imagery dataset for machine learning-based river water segmentation M. La Salandra et al.
- Lightweight Change Detection in Heterogeneous Remote Sensing Images With Online All-Integer Pruning Training C. Zhang et al.
- SARLANG-1M: A Benchmark for Vision–Language Modeling in SAR Image Understanding Y. Wei et al.
- Low-data cross-modal adaptation for remote sensing with proxy-enhanced multi-granularity feature caching Y. Sun et al.
Saved (final revised paper)
Latest update: 30 Apr 2026
Short summary
Natural disasters often damage buildings and threaten lives, especially in areas with limited resources. To help improve emergency response, we created a global dataset called BRIGHT using both optical and radar images to detect building damage in any weather. We tested many artificial intelligence models and showed how well they work in real disaster scenes. This work can guide better tools for future disaster recovery and help save lives faster.
Natural disasters often damage buildings and threaten lives, especially in areas with limited...
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