---
language:
- en
license: cc-by-sa-4.0
task_categories:
- image-segmentation
- feature-extraction
- zero-shot-classification
size_categories:
- 1B<n<10B
tags:
- earth-observation
- remote-sensing
- disaster-response
- artificial-intelligence
- building-damage-mapping
pretty_name: Bright
---

**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.  
<p align="center">
  <img src="./overall.jpg" alt="accuracy" width="97%">
</p>
  

**Benchmark 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](https://github.com/ChenHongruixuan/BRIGHT/blob/master/tutorial.md) 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](https://github.com/ChenHongruixuan/BRIGHT).
* You can download models' checkpoints in this [repo](https://zenodo.org/records/15349462). 


**Unsupervised multimodal image matching**  
* 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](https://github.com/ChenHongruixuan/BRIGHT) to test the exsiting algorithms' performance.  


**IEEE GRSS Data Fusion Contest 2025 (Closed, All Data Available)**  
* BRIGHT also serves as the official dataset of [IEEE GRSS DFC 2025 Track II](https://www.grss-ieee.org/technical-committees/image-analysis-and-data-fusion/). 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](https://github.com/ChenHongruixuan/BRIGHT). 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 for the development phase.
* 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.
* Benchmark code related to the DFC 2025 can be found at this [Github repo](https://github.com/ChenHongruixuan/BRIGHT).    
* The official leaderboard is located on the [Codalab-DFC2025-Track II](https://codalab.lisn.upsaclay.fr/competitions/21122) page.

  
**Paper & Reference**

Details of BRIGHT can be refer to our [paper](https://essd.copernicus.org/articles/17/6217/2025/essd-17-6217-2025.html). 

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](https://www.maxar.com/open-data), following CC-BY-NC-4.0 license. The optical images related to Hawaii-wildifire-2023 are from [High-Resolution Orthoimagery project](https://coast.noaa.gov/digitalcoast/data/highresortho.html) 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](https://www.capellaspace.com/earth-observation/gallery) and [Umbra Space Open Data Program](https://umbra.space/open-data/), following CC-BY-4.0 license.