首个全球分布的多模态灾损评估数据集，支持全天候灾后响应。
BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response
- 融合高分辨率光学与雷达影像，实现全天候灾损检测。
- 覆盖14个地区、7类灾害，光学/雷达分辨率0.3-1米。
- 适配AI模型训练，助力发展中国家灾后救援决策。
全球范围内灾害频发，造成重大人员伤亡和财产损失。遥感（EO）数据可实现快速全面的建筑损伤评估（BDA），对减少伤亡、指导救援至关重要。现有研究多基于光学遥感数据，但受限于晴天和白天条件，难以及时响应灾害。融合光学与合成孔径雷达（SAR）等多模态数据，可实现全天候、昼夜不间断的灾后响应。然而，鲁棒多模态AI模型的发展受限于缺乏合适的基准数据集。本文提出BRIGHT数据集，包含超高分辨率光学与雷达影像，支持基于AI的全天候灾后响应。BRIGHT是目前首个公开、全球分布、事件多样、专为灾后响应设计的多模态数据集，涵盖5类自然灾害与2类人为灾害，覆盖全球14个地区，尤其聚焦外部援助需求迫切的发展中国家。其光学与雷达影像空间分辨率在0.3-1米之间，能精细呈现单体建筑，适合精准损伤评估。实验中我们测试了7种先进AI模型在该数据集上的迁移性与鲁棒性。数据集与代码已开源（https://github.com/ChenHongruixuan/BRIGHT），并作为2025年IEEE GRSS数据融合竞赛官方数据集。
原文摘要 · Abstract (English)
Disaster events occur around the world and cause significant damage to human life and property. Earth observation (EO) data enables rapid and comprehensive building damage assessment (BDA), an essential capability in the aftermath of a disaster to reduce human casualties and to inform disaster relief efforts. Recent research focuses on the development of AI models to achieve accurate mapping of unseen disaster events, mostly using optical EO data. However, solutions based on optical data are limited to clear skies and daylight hours, preventing a prompt response to disasters. Integrating multimodal (MM) EO data, particularly the combination of optical and SAR imagery, makes it possible to provide all-weather, day-and-night disaster responses. Despite this potential, the development of robust multimodal AI models has been constrained by the lack of suitable benchmark datasets. In this paper, we present a BDA dataset using veRy-hIGH-resoluTion optical and SAR imagery (BRIGHT) to support AI-based all-weather disaster response. To the best of our knowledge, BRIGHT is the first open-access, globally distributed, event-diverse MM 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 where external assistance is most needed. The optical and SAR imagery in BRIGHT, with a spatial resolution between 0.3-1 meters, provides detailed representations of individual buildings, making it ideal for precise BDA. In our experiments, we have tested seven advanced AI models trained with our BRIGHT to validate the transferability and robustness. The dataset and code are available at https://github.com/ChenHongruixuan/BRIGHT. BRIGHT also serves as the official dataset for the 2025 IEEE GRSS Data Fusion Contest.
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