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

Asaf Vanunu; Boaz Nadler; Arnon Karnieli

cs.CVOct 1, 2026

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

Authors: Asaf Vanunu, Boaz Nadler, Arnon Karnieli

Organizations: The Albert Katz International School for Desert Studies, The Jacob Blaustein Institutes for Desert Research, Sde Boker Campus, Ben-Gurion University of the Negev, Midreshet Ben-Gurion 84990, Israel · The Goldman Sonnenfeldt School of Sustainability and Climate Change, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel · The Remote Sensing Laboratory, French Associates Institute for Agriculture and Biotechnology of Drylands, The Jacob Blaustein Institutes for Desert Research, Sde Boker Campus, Ben-Gurion University of the Negev, 84990, Israel · Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel

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.

Figures & tables

Figure 1: Study area maps. The training states are marked in black, evaluation states in red, and the states used for both are marked in light blue.

Region	Months (2024)	GOES ABI Number of images	VIIRS Number of detected fires
Georgia	February, March, April	185	8211
Texas	March, April, May	325	9005
California-Oregon	July, August, September	363	53,259
Arizona-Utah	September, October, November	285	5309
British Columbia	July, August, September	115	1523

Table 1: Evaluation dataset

Figure 2: Map of the locations of 51 fire events with their identification numbers. For example, fire number 19 is the Bobcat Fire in Florida that occurred on 29 May 2021.

Figure 3: Rasterizing VIIRS fire detections onto the GOES ABI grid.

Figure 4: Illustration of the fire labeling process. Left: VIIRS fire detection at pixel $(i,j)$ in the rasterized VIIRS matrix $V$ . Right: A $3\times 3$ window is applied in the GOES MCMI matrix $G$ . The pixel $G(i^{\prime},j^{\prime})$ with the highest FI value is labeled as fire.

Figure 5: Illustration of accuracy evaluation of a GEO-based fire detection algorithm using a $3\times 3$ window. (left) Red pixels are GEO-based fire detections; (center) Pink pixels are rasterized VIIRS fire detections; (right) FN, TP, and FP are false negative, true positive, and false positive, respectively.

Figure 6: Comparison of performance metrics in terms of recall, precision, and F1 between the CatBoost model and the GOES FDC product in five different evaluation regions.

Figure 7: Example of CatBoost model predictions on the background of GOES-16 ABI FI images in Texas (left) and Georgia (right). VIIRS 375 m fire detections are shown as green squares. CatBoost prediction pixels are color-coded in red, purple, and blue for TP, FP, and FN, respectively.

Region	Daytime images	Nighttime images
Georgia	101	84
Texas	153	172
California-Oregon	180	183
Arizona-Utah	135	150
British Columbia	38	77

Table 2: Number of daytime and nighttime GOES ABI images for all study regions.

Figure 8: Recall, precision, and F1 scores of the CatBoost model and GOES FDC across all study regions. Panel A (upper) nighttime and Panel B (lower) daytime.

Figure 9: Comparison of performance metrics between CatBoost models using different number of fire pixels for training in five different evaluation regions.

Figure 10: First detection time difference between CatBoost and GOES FDC across fire events. Panel A shows the analysis using only the processed and saturated FDC categories, and Panel B includes the high category. Fire events that were detected simultaneously by CatBoost and GOES FDC have a time delta label of "0".

Fire event	First fire detection	Time difference (minutes)
Beech	CatBoost	300
Bentley		480
Davis		445
Smokehouse Creek		245
Airport	VIIRS	No CatBoost detection
Blue Mountain

Table 3: Summary of fire events detected (1) Only by CatBoost and VIIRS; (2) Only by VIIRS.

Figure 11: Probability of a fire pixel detected by the CatBoost and GOES FDC as a function of the minimum number of VIIRS fire detections inside a GOES ABI pixel ( $k$ ). Evaluation regions: (A) Georgia, (B) Texas, (C) California-Oregon, (D) Arizona-Utah and (E) British Columbia.

Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

Figure B.1: Ten most important features.

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Construction and Evaluation of Machine Learning Models for Near-Real-Time Fire Detection from MTG FCI Imagery

Asaf Vanunu, Boaz Nadler, Arnon Karnieli

Wildfire Detection Satellite Imagery

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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.

Abstract

Explore similar work

Construction and Evaluation of Machine Learning Models for Near-Real-Time Fire Detection from MTG FCI Imagery

On-Orbit Real-Time Wildfire Detection Under On-Board Constraints

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