DOI: 10.5281/zenodo.15385983
title: BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response

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 14 regions worldwide, with a particular focus on developing countries. About 4,200 paired optical and SAR images containing over 380,000 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 (Closed, All Data Available)

BRIGHT also serves as the official dataset of IEEE GRSS DFC 2025 Track II. Now, DFC 25 is over. We recommend using the full version of the dataset along with the corresponding split names provided in our Github repo. Yet, we also retain the original files used in DFC 2025 for download.



Please download dfc25_track2_trainval.zip and unzip it. It contains training images & labels and validation images. 

Please download dfc25_track2_test.zip and unzip it. It contains test images for the final test phase.

Please download dfc25_track2_val_labels.zip for validation labels, redownload dfc25_track2_test_new.zip for test images with geo-coordinates and dfc25_track2_test_labels.zip for testing labels.

The official leaderboard is located on the Codalab-DFC2025-Track II page. 


 

Benchmark for multimodal disaster scenes



For building damage assessment, please download pre-event.zip, post-event.zip, and target.zip. Note that for the optical pre-event data in Ukraine, Myanmar, and Mexico, please follow our instructions/tutorials to download.

For the benchmark code and evaluation protocal for supervised building damage assessment, cross-event transfer, and unsupervised multimodal change detection, please see our Github repo. You can download our provided models' checkpoint in Zenodo repo. 

BRIGHT supports the evaluation of Unsupervised Multimodal Image Matching (UMIM) algorithms for their performance in large-scale disaster scenarios. Please download data with the prefix "umim", such as umim_noto_earthquake.zip, and use our code to test the exsiting algorithms' performance. 


 

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,
    author = {Chen, H. and Song, J. and Dietrich, O. and Broni-Bediako, C. and Xuan, W. and Wang, J. and Shao, X. and Wei, Y. and Xia, J. and Lan, C. and Schindler, K. and Yokoya, N.},
    title = {\textsc{Bright}: a globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response},
    journal = {Earth System Science Data},
    volume = {17},
    year  = {2025},
    number = {11},
    pages = {6217--6253},
    doi = {10.5194/essd-17-6217-2025}
}

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.