发表机构
Ben-Gurion University of the Negev; Weizmann Institute of Science（内盖夫本-古里安大学; 魏茨曼科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究评估了基于MTG FCI影像的机器学习模型在近实时火灾探测中的性能，发现1公里模型显著优于2公里变体和阈值算法，F1分数最高提升0.36，且能提前260分钟探测火灾，模型已开源。
AI 中文摘要
地球静止卫星观测对于野火探测与监测具有重要意义。本研究评估了在1公里和2公里空间配置下用于MTG FCI近实时火灾探测的机器学习模型，并将其与基于阈值的算法进行比较。模型使用VIIRS火灾参考数据在欧洲、非洲和中东的多样生态区域进行训练和评估。关键结果表明，1公里模型显著优于其2公里变体及业务化阈值产品。所构建的1公里模型相较于基线产品，F1分数最高提升了0.36。重要的是，1公里模型检测小规模火灾的概率高于竞争模型。此外，模型比基线产品提前多达260分钟稳健地探测到火灾。为支持开源应用，我们训练好的模型已公开提供。
英文摘要
Geostationary satellite observations are important for wildfire detection and monitoring. The current study evaluates machine learning models for MTG FCI near-real-time fire detection in 1- and 2-km spatial configurations and compares them with threshold-based algorithms. The models were trained and evaluated using VIIRS fire reference data across diverse ecological regions in Europe, Africa, and the Middle East. The key results are that 1-km models significantly outperform both their 2-km variants and operational threshold products. The constructed 1-km models achieved F1 scores higher by up to 0.36 compared to baseline products. Importantly, the 1-km models detected small fires with higher probability compared to competing models. Finally, the models robustly detected fires up to 260 min earlier than baseline products. To support opensource applications, our trained models are publicly available.
