构建1万+灾害事件的多模态数据集，助力智能灾情监测
MONITRS: Multimodal Observations of Natural Incidents Through Remote Sensing
- 融合卫星影像与新闻文本，构建多模态灾情数据集
- 在灾害追踪任务上显著提升模型性能，建立新基准
- 适合灾害响应、遥感分析与多模态学习研究者
自然灾害每年对社区和基础设施造成严重破坏。灾后救援常因难以进入灾区而受阻。遥感技术使远程监测成为可能，但现有计算机视觉与深度学习方法受限于仅针对特定灾害类型、依赖人工专家解读，且缺乏具备高时间粒度或自然语言标注的数据集。本文提出 MONITRS，一个包含超过 10,000 个 FEMA 灾害事件的新型多模态数据集，整合了时间序列卫星影像、来自新闻报道的自然语言注释、地理标签及问答对。实验表明，在该数据集上微调现有多模态大模型（MLLMs）可显著提升灾害监测任务表现，为机器学习辅助的灾情响应系统树立新基准。代码已开源。
原文摘要 · Abstract (English)
Natural disasters cause devastating damage to communities and infrastructure every year. Effective disaster response is hampered by the difficulty of accessing affected areas during and after events. Remote sensing has allowed us to monitor natural disasters in a remote way. More recently there have been advances in computer vision and deep learning that help automate satellite imagery analysis, However, they remain limited by their narrow focus on specific disaster types, reliance on manual expert interpretation, and lack of datasets with sufficient temporal granularity or natural language annotations for tracking disaster progression. We present MONITRS, a novel multimodal dataset of more than 10,000 FEMA disaster events with temporal satellite imagery and natural language annotations from news articles, accompanied by geotagged locations, and question-answer pairs. We demonstrate that fine-tuning existing MLLMs on our dataset yields significant performance improvements for disaster monitoring tasks, establishing a new benchmark for machine learning-assisted disaster response systems. Code can be found at: https://github.com/ShreelekhaR/MONITRS
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