发表机构
UNSW Sydney; ARC Centre of Excellence for the Weather of the 21st Century; Climate Change Research Centre, University of New South Wales; UNSW AI Institute; Wisconsin School of Business, University of Wisconsin–Madison（新南威尔士大学悉尼分校; 21世纪天气ARC卓越中心; 新南威尔士大学气候变化研究中心; 新南威尔士大学人工智能研究院; 威斯康星大学麦迪逊分校威斯康星商学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究以澳大利亚维多利亚州为案例，评估AlphaEarth Foundations嵌入在野火易感性制图中的价值，发现其可高精度重建常用变量，模型ROC-AUC超0.92，近区域迁移性优于物理变量模型，为相关应用提供指导。
AI 中文摘要
野火易感性制图通常依赖于从多个遥感、气候和地理空间产品中整合的物理变量。AlphaEarth Foundations（AEF）提供可直接用于分析的地理空间嵌入，这可能会减少对繁重协调工作和特定任务特征工程的依赖，但其在野火易感性制图中的价值尚未得到系统评估。以2017-2025年的澳大利亚维多利亚州为案例研究，我们发现AEF嵌入能够高精度地重建野火易感性分析中常用的变量。在基于卫星衍生火灾发生数据训练的下游易感性模型中，基于嵌入的易感性模型的ROC-AUC值超过0.92，且能持续识别维多利亚州东部的高野火易感性区域，特别是吉普斯兰和东北部高地，以及维多利亚州中部和西北部的额外局部热点。AEF嵌入的一个关键特性是在气候相似区域内具有很强的近区域可迁移性：当在维多利亚州训练的基于嵌入的模型应用于堪培拉和悉尼西部-蓝山地区时，与基于物理变量的模型平均下降约25%相比，ROC-AUC在堪培拉提高了约4%，在悉尼西部-蓝山地区仅下降约2%。这些发现为使用AEF嵌入提供了实用指导，并为政府机构和再保险公司等下游用户构建可扩展的野火易感性制图工作流程奠定了基础。
英文摘要
Wildfire susceptibility mapping typically relies on physical variables assembled from multiple remote-sensing, climate, and geospatial products. AlphaEarth Foundations (AEF) provides analysis-ready geospatial embeddings that may reduce this dependence on heavy harmonisation and task-specific feature engineering, but their value for wildfire susceptibility mapping has not been systematically evaluated. Using Victoria, Australia (2017-2025), as a case study, we show that AEF embeddings can reconstruct commonly used variables in wildfire susceptibility analysis with high accuracy. In downstream susceptibility models trained on satellite-derived fire occurrence data, embedding-based susceptibility models achieve ROC-AUC values above 0.92 and consistently identify high wildfire susceptibility across eastern Victoria, particularly Gippsland and the north-eastern uplands, with additional localized hotspots in central and northwestern Victoria. A key feature of AEF embeddings is their strong near-region transferability within climatically similar regions. When embedding-based models trained in Victoria are applied to Canberra and Western Sydney-Blue Mountains, ROC-AUC improves by around 4% at Canberra and declines by around 2% at Western Sydney-Blue Mountains, compared with a mean decrease of approximately 25% for physical-variable models. These findings provide practical guidance for using AEF embeddings and lay a foundation for scalable wildfire susceptibility mapping workflows for downstream users such as government agencies and (re)insurers.
