MFiSP：一种多模态火灾蔓延预测框架
MFiSP: A Multimodal Fire Spread Prediction Framework
AI总结：
针对传统火灾预测依赖人工与静态数据、精度不足的问题，提出整合社交媒体与遥感数据的MFiSP多模态框架，通过动态调整可燃物图策略匹配观测蔓延速率，实验验证其预测效果优于传统方法。
摘要：
2019-2020年澳大利亚“黑色夏季”森林大火烧毁1900万公顷土地、3000栋房屋，持续七个月，表明野火威胁规模不断升级、紧迫性日益凸显，需要更优的预测以实现有效响应。传统火灾建模依赖火灾行为分析师（FBAns）的人工解读与静态环境数据，常导致结果不准确且存在操作局限性。新兴数据源，如NASA的FIRMS卫星影像与志愿地理信息，通过实现动态火灾蔓延预测提供了改进潜力。本研究提出多模态火灾蔓延预测框架MFiSP，该框架整合社交媒体数据与遥感观测以提升预测精度。通过在同化周期之间调整可燃物图操作策略，该框架可动态调整火灾行为预测，使其与观测到的蔓延速率保持一致。我们利用多场景下人工合成的火灾事件多边形评估MFiSP的效能，分析各模态单独及组合作用对预测 perimeter 的影响。结果表明，我们的整合多模态数据的MFiSP，相比依赖FBAn专业知识与静态输入的传统方法，能够改进火灾蔓延预测效果。
英文摘要：
The 2019-2020 Black Summer bushfires in Australia devastated 19 million hectares, destroyed 3,000 homes, and lasted seven months, demonstrating the escalating scale and urgency of wildfire threats requiring better forecasting for effective response. Traditional fire modeling relies on manual interpretation by Fire Behaviour Analysts (FBAns) and static environmental data, often leading to inaccuracies and operational limitations. Emerging data sources, such as NASA's FIRMS satellite imagery and Volunteered Geographic Information, offer potential improvements by enabling dynamic fire spread prediction. This study proposes a Multimodal Fire Spread Prediction Framework (MFiSP) that integrates social media data and remote sensing observations to enhance forecast accuracy. By adapting fuel map manipulation strategies between assimilation cycles, the framework dynamically adjusts fire behavior predictions to align with the observed rate of spread. We evaluate the efficacy of MFiSP using synthetically generated fire event polygons across multiple scenarios, analyzing individual and combined impacts on forecast perimeters. Results suggest that our MFiSP integrating multimodal data can improve fire spread prediction beyond conventional methods reliant on FBAn expertise and static inputs.
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。
