ChenHongruixuan/BRIGHT — Gitpedia

<div align="center"> <h1 align="center">☀️BRIGHT☀️</h1> <h3>BRIGHT: A globally distributed multimodal VHR dataset for all-weather disaster response</h3>

Hongruixuan Chen1,2, Jian Song1,2, Olivier Dietrich3, Clifford Broni-Bediako2, Weihao Xuan1,2, Junjue Wang1
Xinlei Shao1, Yimin Wei1,2, Junshi Xia3, Cuiling Lan4, Konrad Schindler3, Naoto Yokoya1,2 *

1 The University of Tokyo, 2 RIKEN AIP, 3 ETH Zurich, 4 Microsoft Research Asia

Overview | Start BRIGHT | Common Issues | Follow-Ups | Others

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🛎️Updates

Notice☀️☀️: BRIGHT has been accepted by ESSD!! The contents related to IEEE GRSS DFC 2025 have been transferred to here!!
May 18th, 2026: Bright has been selected as 🔥ESI Hot Paper and Highly Cited Paper🏆!!
Mar 25th, 2026: Bright challenge: advancing multimodal building damage mapping to instance level on CVPRW 2026 is now open. You can download the instance labels, run our baseline code and submit your results on Codabench page now!!
Nov 18th, 2025: BRIGHT has been accepted by ESSD and online available now!!
Aug 12th, 2025: BRIGHT has been integrated into TorChange. Many thanks for the effort of Dr. Zhuo Zheng!!
May 05th, 2025: All the data and benchmark code related to our paper has now released. You are warmly welcome to use them!!
Apr 28th, 2025: IEEE GRSS DFC 2025 Track II is over. Congratulations to winners!! You can now download the full version of DFC 2025 Track II data in Zenodo or HuggingFace!!
Jan 18th, 2025: BRIGHT has been integrated into TorchGeo. Many thanks for the effort of Nils Lehmann!!
Jan 13th, 2025: The arXiv paper of BRIGHT is now online. If you are interested in details of BRIGHT, do not hesitate to take a look!!

🔭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 disaster events in 23 regions worldwide, with a particular focus on developing countries.
It supports not only the development of supervised deep models, but also the testing of their performance on cross-event transfer setup, as well as unsupervised domain adaptation, semi-supervised learning, unsupervised change detection, and unsupervised image matching methods in multimodal and disaster scenarios.

🗝️Let's Get Started with BRIGHT!

`A. Installation`

Note that the code in this repo runs under Linux system. We have not tested whether it works under other OS.

Step 1: Clone the repository:

Clone this repository and navigate to the project directory:

bash
git clone https://github.com/ChenHongruixuan/BRIGHT.git
cd BRIGHT

Step 2: Environment Setup:

It is recommended to set up a conda environment and installing dependencies via pip. Use the following commands to set up your environment:

Create and activate a new conda environment

bash
conda create -n bright-benchmark
conda activate bright-benchmark

Install dependencies

bash
pip install -r requirements.txt

`B. Data Preparation`

Please download the BRIGHT from Zenodo or HuggingFace. Note that we cannot redistribute the optical data over Ukraine, Myanmar, and Mexico. Please follow our tutorial to download and preprocess them.

After the data has been prepared, please make them have the following folder/file structure:

${DATASET_ROOT}   # Dataset root directory, for example: /home/username/data/bright
│
├── pre-event
│    ├──bata-explosion_00000000_pre_disaster.tif
│    ├──bata-explosion_00000001_pre_disaster.tif
│    ├──bata-explosion_00000002_pre_disaster.tif
│   ...
│
├── post-event
│    ├──bata-explosion_00000000_post_disaster.tif
│    ... 
│
└── target
     ├──bata-explosion_00000000_building_damage.tif 
     ...

`C. Model Training & Tuning`

The following commands show how to train and evaluate UNet on the BRIGHT dataset using our standard ML split set in [bda_benchmark/dataset/splitname/standard_ML]:

bash
python script/standard_ML/train_UNet.py --dataset 'BRIGHT' \
                                        --train_batch_size 16 \
                                        --eval_batch_size 4 \
                                        --num_workers 16 \
                                        --crop_size 640 \
                                        --max_iters 800000 \
                                        --learning_rate 1e-4 \
                                        --model_type 'UNet' \
                                        --model_param_path '<your model checkpoint saved path>' \
                                        --train_dataset_path '<your dataset path>' \
                                        --train_data_list_path '<your project path>/bda_benchmark/dataset/splitname/standard_ML/train_set.txt' \
                                        --val_dataset_path '<your dataset path>' \
                                        --val_data_list_path '<your project path>/bda_benchmark/dataset/splitname/standard_ML/val_set.txt' \
                                        --test_dataset_path '<your dataset path>' \
                                        --test_data_list_path '<your project path>/bda_benchmark/dataset/splitname/standard_ML/test_set.txt'

`D. Inference & Evaluation`

Then, you can run the following code to generate raw & visualized prediction results and evaluate performance using the saved weight. You can also download our provided checkpoints from Zenodo.

bash
python script/standard_ML/infer_UNet.py --model_path  '<path of the checkpoint of model>' \
                                        --test_dataset_path '<your dataset path>' \
                                        --test_data_list_path '<your project path>/bda_benchmark/dataset/splitname/standard_ML/test_set.txt' \
                                        --output_dir '<your inference results saved path>'

`E. Other Benchmarks & Setup`

In addition to the above supervised deep models, BRIGHT also provides standardized evaluation setups for several important learning paradigms and multimodal EO tasks:

Cross-event transfer setup: Evaluate model generalization across disaster types and regions. This setup simulates real-world scenarios where no labeled data (zero-shot) or limited labeled data (one-shot) is available for the target event during training.
Unsupervised domain adaptation: Adapt models trained on source disaster events to unseen target events without any target labels, using UDA techniques under the zero-shot cross-event setting.
Semi-supervised learning: Leverage a small number of labeled samples and a larger set of unlabeled samples from the target event to improve performance under the one-shot cross-event setting.
Unsupervised multimodal change detection: Detect disaster-induced building changes without using any labels. This setup supports benchmarking of general-purpose change detection algorithms under realistic large-scale disaster scenarios.
Unsupervised multimodal image matching: Evaluate the performance of matching algorithms in aligning raw, large-scale optical and SAR images based on manual-control-point-based registration accuracy. This setup focuses on realistic multimodal alignment in disaster-affected areas.
IEEE GRSS DFC 2025 Track II: The Track II of IEEE GRSS DFC 2025 aims to develop robust and generalizable methods for assessing building damage using bi-temporal multimodal images on unseen disaster events.

🤔Common Issues

Based on peers' questions from issue section, here's a quick navigate list of solutions to some common issues.

Issue	Solution
Complete data of DFC25 for research	The labels for validation and test sets of DFC25 have been uploaded to Zenodo and HuggingFace.
Python package conflicts	The baseline code is not limited to a specific version, and participants do not need to match the version we provide.

🏢 Works Building on BRIGHT

We are delighted to see BRIGHT supporting various research directions. Below is a curated list of papers, benchmarks, and projects that build upon or integrate BRIGHT.

Work	Category	Venue	Link	Key Contribution
CDML	Algorithm & Benchmark	IEEE TPAMI 2026	Code	Proposed a first-order cross-domain meta-learning framework for few-shot remote sensing classification
SARCLIP	Algorithm & Benchmark	ISPRS J P&RS 2025	Data & Code	Proposed multimodal foundation model (SARCLIP) and 400k dataset for SAR analysis
DisasterM3	Benchmark	NeurIPS 2025	Data & Code	Constructed DisasterM3, a multi-sensor vision-language dataset (123k pairs) for VLM-based disaster response
SARLANG-1M	Benchmark	IEEE TGRS 2026	Data & Code	Constructed a large-scale SAR-text benchmark (1M+ pairs) for multimodal understanding
IM4CD	Algorithm	ISPRS J P&RS 2026	-	Proposed an unsupervised framework that unifies multimodal change detection and image matching to robustly identify changes across different modalities
FlowMamba	Algorithm	IEEE TCSVT 2026	Code	Proposed a Mamba-based network handling image misalignment for building damage assessment
DSTCD	Algorithm	JAG 2026	Code	Proposed a dual-stage framework transferring registration features for few-shot optical-SAR change detection in disaster monitoring
DDCL-GAN	Algorithm	ESWA 2026		Proposed a dual-domain contrastive learning framework for unsupervised multimodal change detection
CDPrompt	Algorithm	IEEE TGRS 2025	Data & Code	Proposed CDPrompt framework and Bright-Extended dataset for multimodal change detection
TSIG-GAN	Algorithm	GIS&RS 2025	-	Proposed TSIG-GAN for fine-grained multimodal change detection via texture-structure interaction
DCIBCD	Algorithm	ESWA 2025	Code	Proposed a dual-branch model mitigating bidirectional interference in change detection

📜Reference

If this dataset or code contributes to your research, please kindly consider citing our paper and give this repo ⭐️ :)

@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}
}