AI Insight
Researchers developed a CatBoost machine learning model to detect wildfires using satellite imagery from GOES ABI, training it on thousands of images and over 300,000 fire detections from VIIRS. The model substantially outperformed the current operational GOES Fire Detection and Characterization product, achieving F1 scores 0.16 to 0.38 higher across five tested regions and detecting 26 historical fires earlier than both existing systems combined. The machine learning approach showed particularly strong performance during nighttime detection, where the operational system achieved very low recall rates of approximately 0.03.
Why it matters
Earlier and more accurate wildfire detection can provide critical additional time for evacuation, firefighting response, and resource deployment, potentially saving lives and reducing property damage. The improved nighttime detection capability addresses a significant gap in current operational systems, as many wildfires intensify or spread rapidly during overnight hours.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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.
Source: Comparing a gradient boosting algorithm to the GOES FDC for wildfire detection