MONITRS：通过遥感对自然事件的多模态观测
MONITRS: Multimodal Observations of Natural Incidents Through Remote Sensing
AI总结：
本文提出MONITRS多模态数据集，包含超1万个FEMA灾害事件的时间序列卫星图像与自然语言标注，并证明微调现有多模态大语言模型可显著提升灾害监测性能，为ML辅助灾害响应建立新基准。
摘要：
自然灾害每年对社区和基础设施造成毁灭性破坏。由于在灾害发生期间及之后难以进入受灾区域，有效的灾害响应受到阻碍。遥感使我们能够以远程方式监测自然灾害。近年来，计算机视觉和深度学习取得了进展，有助于自动化卫星图像分析，然而，这些方法仍然受到以下限制：它们对特定灾害类型的关注范围狭窄、依赖人工专家判读，以及缺乏具有足够时间粒度或自然语言标注的数据集来追踪灾害进展。我们提出了MONITRS，这是一个新颖的多模态数据集，包含超过10,000个FEMA灾害事件，具有时间序列卫星图像和来自新闻文章的自然语言标注，并附带地理标记位置和问答对。我们证明，在我们的数据集上微调现有的多模态大语言模型（MLLMs）可以在灾害监测任务上带来显著的性能提升，为机器学习辅助的灾害响应系统建立了新的基准。代码可在以下地址获取：https://github.com/ShreelekhaR/MONITRS
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
