用多源卫星数据生成高分辨率林火燃料湿度图，提升预警能力
High-Resolution Live Fuel Moisture Content (LFMC) Maps for Wildfire Risk from Multimodal Earth Observation Data
- 基于预训练多模态地球观测模型，自动生成全覆盖燃料湿度图
- 相比随机初始化模型，均方根误差降低20%
- 适用于美国多地实时火险监测，尤其适合灾后评估
野火正以惊人速度加剧。近年来，人工智能与公开卫星数据的进步使得全球范围内高分辨率、低延迟的火灾风险因子监测成为可能。活燃料含水量（LFMC）是关键火险指标，对科研与应急响应均有价值。但地面采样成本高、耗时长，导致数据稀疏且更新频率低。本文探索使用预训练的多模态地球观测模型，生成大范围、空间连续（全覆盖）的LFMC地图。方法在随机初始化模型基础上显著提升性能，均方根误差（RMSE）降低20%。我们提供自动化流程，可在全美范围内快速生成该类地图，并在近期受火灾影响的埃顿（Eaton）与帕利萨德斯（Palisades）地区验证其有效性。
原文摘要 · Abstract (English)
Wildfires are increasing in intensity and severity at an alarming rate. Recent advances in AI and publicly available satellite data enable monitoring critical wildfire risk factors globally, at high resolution and low latency. Live Fuel Moisture Content (LFMC) is a critical wildfire risk factor and is valuable for both wildfire research and operational response. However, ground-based LFMC samples are both labor intensive and costly to acquire, resulting in sparse and infrequent updates. In this work, we explore the use of a pretrained, highly-multimodal earth-observation model for generating large-scale spatially complete (wall-to-wall) LFMC maps. Our approach achieves significant improvements over previous methods using randomly initialized models (20 reduction in RMSE). We provide an automated pipeline that enables rapid generation of these LFMC maps across the United States, and demonstrate its effectiveness in two regions recently impacted by wildfire (Eaton and Palisades).
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