发表机构
University of California, Davis（加利福尼亚大学戴维斯分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究开发经空间验证的GeoAI框架，结合多源数据揭示2025年帕利塞兹火灾中建筑密度、植被湿度等对建筑损毁的作用，提出空间块验证为城市野火建模的最低标准。
AI 中文摘要
城市野火恢复力取决于建成环境、植被状况与极端火灾天气之间的相互作用，但城市尺度的风险模型常常忽略预测能力是否能跨社区转移。针对2025年1月的帕利塞兹火灾，我们开发了经空间验证的GeoAI工作流，将12081项加州消防局（CAL FIRE）的损毁检查数据与火灾前的哨兵2号（Sentinel-2）植被指数、陆地卫星（Landsat）地表温度、LANDFIRE燃料数据、地形数据，以及开放街道地图（OpenStreetMap）的建筑和道路数据关联起来。在9883处接受检查的住宅建筑中，5566处被损毁。随机交叉验证显示，集成XGBoost模型的ROC-AUC为0.92，但1公里空间块验证将性能降至0.75；逻辑回归表现相似且校准效果更好。100米范围内的建筑数量是最强预测因子，每增加一个标准差，损毁几率增加4.12倍。植被湿度和绿度呈现相反的条件关联：100-300米范围内的归一化差异湿度指数（NDMI）具有保护作用（比值比OR=0.52），而30-100米范围内的归一化差异植被指数（NDVI）在控制湿度后与损毁呈正相关（OR=1.74）。预测信息集中在100-300米的社区尺度。单独的火灾后轨迹图绘制了火烧烈度和植被恢复情况，无遗漏。研究结果支持社区尺度的易感性筛查、关注湿度的植被管理，以及将空间块验证作为单事件城市野火建模的最低标准。
英文摘要
Urban wildfire resilience depends on interactions among built form, vegetation condition, and extreme fire weather, yet city-scale risk models often overlook whether predictive skill transfers across neighborhoods. We developed a spatially validated GeoAI workflow for the January 2025 Palisades Fire, linking 12,081 CAL FIRE damage inspections to pre-fire Sentinel-2 vegetation indices, Landsat surface temperature, LANDFIRE fuels, terrain, and OpenStreetMap buildings and roads. Among 9,883 inspected residential structures, 5,566 were destroyed. Random cross-validation yielded ROC-AUC 0.92 for the integrated XGBoost model, but 1 km spatial block validation reduced performance to 0.75; logistic regression performed similarly and was better calibrated. Building count within 100 m was the strongest predictor, with destruction odds increasing 4.12-fold per standard deviation. Vegetation moisture and greenness showed opposing conditional associations: NDMI at 100-300 m was protective (OR 0.52), whereas NDVI at 30-100 m was positively associated with destruction after accounting for moisture (OR 1.74). Predictive information was concentrated at the 100-300 m neighborhood scale. A separate post-fire track mapped burn severity and vegetation recovery without leakage. The results support neighborhood-scale susceptibility screening, moisture-aware vegetation management, and spatial block validation as a minimum standard for single-event urban wildfire modeling.
Comments20 pages, 13 figures, 4 tables. Data and derived products: https://doi.org/10.5281/zenodo.22061862. Replication code: https://github.com/MohammadrezaNarimaniUCDavis/Palisades_Urban_Wildfire_GeoAI
