arXiv：cs.LG（机器学习，全量分类）· Asaf Vanunu, Boaz Nadler, Arnon Karnieli·· 15 小时前AI 评分42
CatBoost 与 GOES FDC 野火检测对比：机器学习模型精度更高、夜间检测更可靠
Comparing a gradient boosting algorithm to the GOES FDC for wildfire detection
AI 导读
一项研究用数千张 GOES ABI 图像和超 30 万条 VIIRS 火点数据训练 CatBoost 模型，在五个区域的独立数据集上其 precision、recall 和 F1 均超过业务化运行的 GOES FDC，F1 高出 0.16 至 0.38。
正文
Abstract: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.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.01994 [cs.CV] |
| (or arXiv:2610.01994v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.01994 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Asaf Vanunu [view email]
[v1]
Thu, 1 Oct 2026 16:29:13 UTC (7,423 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org