一种用于细粒度众包地震灾害评估的多模态、多语言与多维流水线
A Multimodal, Multilingual, and Multidimensional Pipeline for Fine-grained Crowdsourcing Earthquake Damage Evaluation
AI总结：
本文提出多模态、多语言与多维（3M）流水线，利用多模态大语言模型融合社交媒体图像与文本进行细粒度地震灾害评估，并在两次地震事件中验证了其与真实震害数据的强相关性。
摘要：
快速、细粒度的灾害损失评估对于有效应急响应至关重要，但由于地面传感器有限以及官方报告延迟，这一任务仍面临挑战。社交媒体提供了丰富、实时且以人为中心的观测信息，但其多模态和非结构化的特性给传统分析方法带来了困难。在本研究中，我们提出了一种结构化的多模态、多语言与多维（3M）流水线，利用多模态大语言模型（MLLMs）来评估灾害影响。我们在两次重大地震事件上，通过宏观和微观层面的分析，对三个基础模型进行了评估。结果表明，MLLMs能够有效整合图像-文本信号，并与真实地震数据表现出强相关性。然而，模型性能会随语言、震中距离和输入模态的不同而变化。本研究凸显了MLLMs在灾害评估中的潜力，并为未来将MLLMs应用于实时危机情境的研究奠定了基础。代码和数据已发布在：https://github.com/missa7481/EMNLP25_earthquake
英文摘要：
Rapid, fine-grained disaster damage assessment is essential for effective emergency response, yet remains challenging due to limited ground sensors and delays in official reporting. Social media provides a rich, real-time source of human-centric observations, but its multimodal and unstructured nature presents challenges for traditional analytical methods. In this study, we propose a structured Multimodal, Multilingual, and Multidimensional (3M) pipeline that leverages multimodal large language models (MLLMs) to assess disaster impacts. We evaluate three foundation models across two major earthquake events using both macro- and micro-level analyses. Results show that MLLMs effectively integrate image-text signals and demonstrate a strong correlation with ground-truth seismic data. However, performance varies with language, epicentral distance, and input modality. This work highlights the potential of MLLMs for disaster assessment and provides a foundation for future research in applying MLLMs to real-time crisis contexts. The code and data are released at: https://github.com/missa7481/EMNLP25_earthquake
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。
