Datasets:
Formats:
imagefolder
Languages:
English
Size:
1K - 10K
Tags:
earth-observation
remote-sensing
disaster-response
artificial-intelligence
building-damage-mapping
DOI:
License:
| 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. |