BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response
Creators
-
Chen, Hongruixuan
(Project leader)1, 2
- Song, Jian (Project member)1, 2
-
Dietrich, Olivier
(Project member)3
-
Broni-Bediako, Clifford
(Project member)2
-
Xuan, Weihao
(Project member)1, 2
-
Wang, Junjue
(Project member)1
-
Shao, Xinlei
(Project member)1
- Yimin, Wei (Project member)1, 2
-
Xia, Junshi
(Supervisor)2
-
Lan, Cuiling
(Supervisor)4
-
Schindler, Konrad
(Supervisor)3
-
Yokoya, Naoto
(Supervisor)1, 2
Description
Overview
BRIGHT is the first open-access, globally distributed, event-diverse multimodal dataset specifically curated to support AI-based disaster response. It covers five types of natural disasters and two types of man-made disasters across 12 regions worldwide, with a particular focus on developing countries. About 4,500 paired optical and SAR images containing over 350,000 building instances in BRIGHT, with a spatial resolution between 0.3 and 1 meters, provides detailed representations of individual buildings, making it ideal for precise damage assessment.
IEEE GRSS Data Fusion Contest 2025
BRIGHT also serves as the official dataset of IEEE GRSS DFC 2025 Track II.
Please download dfc25_track2_trainval.zip and unzip it. It contains training images & labels and validation images.
Benchmark code related to the DFC 2025 can be found at this Github repo.
The official leaderboard is located on the Codalab-DFC2025-Track II page.
Paper & Reference
Details of BRIGHT can be refer to our paper.
If BRIGHT is useful to research, please kindly consider cite our paper
@article{chen2025bright,
title={BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response},
author={Hongruixuan Chen and Jian Song and Olivier Dietrich and Clifford Broni-Bediako and Weihao Xuan and Junjue Wang and Xinlei Shao and Yimin Wei and Junshi Xia and Cuiling Lan and Konrad Schindler and Naoto Yokoya},
journal={arXiv preprint arXiv:2501.06019},
year={2025},
url={https://arxiv.org/abs/2501.06019},
}
License
Label data of BRIGHT are provided under the same license as the optical images, which varies with different events.
With the exception of two events, Hawaii-wildfire-2023 and La Palma-volcano eruption-2021, all optical images are from Maxar Open Data Program, following CC-BY-NC-4.0 license. The optical images related to Hawaii-wildifire-2023 are from High-Resolution Orthoimagery project of NOAA Office for Coastal Management. The optical images related to La Palma-volcano eruption-2021 are from IGN (Spain) following CC-BY 4.0 license.
The SAR images of BRIGHT is provided by Capella Open Data Gallery and Umbra Space Open Data Program, following CC-BY-4.0 license.
Files
dfc25_track2_trainval.zip
Files
(10.2 GB)
Name | Size | Download all |
---|---|---|
md5:2c435bb50345d425390eff59a92134ac
|
10.2 GB | Preview Download |
Additional details
Identifiers
- arXiv
- arXiv:2501.06019
Related works
- References
- Preprint: arXiv:2501.06019 (arXiv)
Dates
- Available
-
2025-01-09Version for IEEE GRSS DFC 2025 Track II
Software
- Repository URL
- https://github.com/ChenHongruixuan/BRIGHT
- Programming language
- Python
- Development Status
- Active