融合社交媒体与卫星数据，动态预测林火蔓延，提升救灾响应精度。
MFiSP: A Multimodal Fire Spread Prediction Framework
- 结合卫星影像与社交媒体数据，动态调整火势预测模型。
- 在多场景测试中，融合数据的预测边界误差显著低于传统方法。
- 适合应急决策、灾害预警系统开发者参考。
2019-2020年澳大利亚“黑色夏季”山火摧毁了1900万公顷土地，烧毁3000栋房屋，持续七个月，凸显了野火威胁日益加剧，亟需更精准的预测以支持有效应对。传统火情建模依赖火行为分析师（FBAns）人工解读和静态环境数据，常导致误判与操作局限。新兴数据源如NASA FIRMS卫星影像和志愿地理信息，为动态火势预测提供了可能。本文提出多模态火势蔓延预测框架（MFiSP），整合社交媒体数据与遥感观测，通过在同化周期间调整燃料地图策略，使火势行为预测动态匹配实际蔓延速率。我们在多个情景下使用合成生成的火情事件多边形评估MFiSP效能，分析单模态与多模态数据对预测边界的独立及联合影响。结果表明，融合多源数据的MFiSP可显著提升火势蔓延预测精度，优于依赖FBAns经验与静态输入的传统方法。
原文摘要 · Abstract (English)
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.
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