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 | |
| size_categories: | |
| - 1B<n<10B | |
| tags: | |
| - earth-observation | |
| - remote-sensing | |
| - disaster-response | |
| **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. | |
| **IEEE GRSS Data Fusion Contest 2025** | |
| * 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/). | |
| * 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. | |
| * 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://huggingface.co/papers/2501.06019). | |
| 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](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. |