比较梯度提升算法与GOES FDC在野火检测中的表现
Comparing a gradient boosting algorithm to the GOES FDC for wildfire detection
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中文总结 AI 辅助
本研究提出基于CatBoost的机器学习方法用于GOES ABI影像野火检测，在多个区域上相比GOES FDC实现了更高的精度、召回率和F1分数，且夜间检测能力显著更优，并能更早发现火灾。
中文摘要 AI 辅助
野火对人类生命、生态系统和财产构成严重风险。本研究提出了一种利用GOES ABI影像进行野火检测的机器学习方法。在一个包含数千张ABI图像和超过30万次匹配的VIIRS火点检测的大型数据集上训练了CatBoost模型。在覆盖五个区域的独立数据集上的评估显示，训练得到的CatBoost模型优于业务化的GOES火灾检测与特征描述（FDC）产品。在训练区域内外的精度、召回率和F1分数均更高。在所有区域中，CatBoost模型的F1分数比GOES FDC高出0.16至0.38。此外，在51起历史火灾事件中，CatBoost在VIIRS和GOES FDC之前检测到26起火灾，而GOES FDC仅提前检测到6起。重要的是，CatBoost模型在夜间也能实现准确的野火检测，而GOES FDC的召回率非常低，约为0.03。这项研究表明，机器学习模型可能对现有的静止轨道卫星火灾产品带来显著改进，包括更高的准确性、更少的误报和更早的检测。
英文摘要
Wildfires pose severe risks to human life, ecosystems, and property. This study presents a machine learning approach for wildfire detection from GOES ABI imagery. A CatBoost model was trained on a large dataset with thousands of ABI images and over 300,000 matching VIIRS fire detections. An evaluation on a separate dataset across five regions showed that the learned CatBoost model outperformed the operational GOES Fire Detection and Characterization (FDC) product. It achieved higher precision, recall, and F1 scores both within and outside the training area. The CatBoost model achieved F1 scores that were 0.16 to 0.38 higher than the GOES FDC in all regions. In addition, out of 51 historical fire events, the CatBoost detected 26 fires before both VIIRS and GOES FDC, compared to only six earlier detections by the GOES FDC. Importantly, the CatBoost model achieved accurate wildfire detection also during nighttime, whereas the GOES FDC obtained very low recall values, around 0.03. This study demonstrates that machine learning models may offer significant improvements over existing geostationary fire products, including higher accuracy, fewer false alarms, and earlier detection.
发表机构
- Ben-Gurion University of the Negev（内盖夫本-古里安大学）
- Weizmann Institute of Science（魏茨曼科学研究所）
机构由 AI 辅助整理，请以论文原文为准。
