DisasterM3: A Remote Sensing Vision Language Dataset for Disaster Damage Assessment and Response Junjue Wang , Weihao Xuan , Heli Qi, Zhihao Liu, Kunyi Liu, Yuhan Wu, Hongruixuan Chen , Jian Song Junshi Xia, Zhuo Zheng , Naoto Yokoya† Equal Contributions † Corresponding Author Paper : https://arxiv.org/abs/2505.21089 Code : https://github.com/Junjue Wang/DisasterM3 Highlights DisasterM3 includes 26,988 bi temporal satellite images and 123k instruction pairs across 5 continents, with three characteristics: 1. Multi hazard: 36 historical disaster events with significant impacts, which are categorized into 10 common natural and man made disasters 2. Multi sensor: Extreme weather during disasters often hinders optical sensor imaging, making it necessary to combine Synthetic Aperture Radar (SAR) imagery for post disaster scenes 3. Multi task: 9 disaster related visual perception and reasoning tasks, harnessing the full potential of VLM's reasoning ability News 2025/10/23, We released the DisasterM3 instruct set. 2025/10/17, We released the benchmark set of DisasterM3. 2025/09/22, We are preparing the dataset and code. 2025/09/22, Our paper got accepted by NeurIPS 2025. Benchmark Please…
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