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.

翻译：2019-2020年澳大利亚的“黑色夏季”丛林大火肆虐了1900万公顷土地，摧毁了3000所房屋，持续了七个月之久，凸显了野火威胁规模不断扩大、应对日益紧迫，亟需更精准的预测以实现有效响应。传统的火灾建模依赖火灾行为分析师（FBAns）的人工判读和静态环境数据，常导致预测不准和操作局限。新兴数据源（如NASA的FIRMS卫星影像和公众地理信息）通过实现动态火势蔓延预测，为改进提供了可能。本研究提出一种多模态火势蔓延预测框架（MFiSP），通过整合社交媒体数据和遥感观测来提升预测精度。该框架通过在数据同化周期间调整可燃物地图处理策略，动态修正火行为预测以匹配观测到的蔓延速率。我们利用多种情景下合成生成的火场多边形评估MFiSP的效能，分析其对预测火场边界的单独及综合影响。结果表明，相较于依赖FBAn专业知识和静态输入的传统方法，我们整合多模态数据的MFiSP能够提升火势蔓延预测能力。