<span style="" class="text smallcaps">Bright</span>: a globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response

Chen, Hongruixuan; Song, Jian; Dietrich, Olivier; Broni-Bediako, Clifford; Xuan, Weihao; Wang, Junjue; Shao, Xinlei; Wei, Yimin; Xia, Junshi; Lan, Cuiling; Schindler, Konrad; Yokoya, Naoto

doi:10.5194/essd-17-6217-2025

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.

https://doi.org/10.5194/essd-17-6217-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 11

Data description paper

|

18 Nov 2025

Data description paper |

| 18 Nov 2025

Bright: a globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response

Hongruixuan Chen, Jian Song, Olivier Dietrich, Clifford Broni-Bediako, Weihao Xuan, Junjue Wang, Xinlei Shao, Yimin Wei, Junshi Xia, Cuiling Lan, Konrad Schindler, and Naoto Yokoya

Data sets

BRIGHT Hongruixuan Chen et al. https://zenodo.org/records/14619797

BRIGHT Hongruixuan Chen et al. https://huggingface.co/datasets/Kullervo/BRIGHT

Model code and software

BRIGHT Hongruixuan Chen et al. https://github.com/ChenHongruixuan/BRIGHT

BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response (v1.0) Hongruixuan Chen et al. https://doi.org/10.5281/zenodo.15349462

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.