Comparing a gradient boosting algorithm to the GOES FDC for wildfire detection

2026-10-01

AI Summary

This study addresses the limited accuracy, nighttime omission errors, and delayed warnings associated with GOES fire detection products by proposing a satellite-based fire detection method utilizing the CatBoost gradient boosting algorithm. The approach leverages GOES Advanced Baseline Imager (ABI) multispectral imagery for model training and comparative evaluation, enabling high-precision, all-weather fire monitoring. Experimental results demonstrate that the proposed model improves the F1 score by 0.16 to 0.38 over the conventional GOES Fire Detection and Characterization (FDC) product while significantly enhancing nighttime recall rates. Furthermore, across 51 historical wildfire events, the model detected 26 fires earlier than both VIIRS and FDC, substantially advancing initial detection times. These findings indicate that the proposed method provides effective technical support for early wildfire warning systems.

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.

Problem

• wildfire detection
• GOES ABI imagery
• satellite remote sensing
• fire detection accuracy

Innovation

• CatBoost
• Wildfire detection
• Gradient boosting
• GOES ABI imagery
• Machine learning