Construction and Evaluation of Machine Learning Models for Near-Real-Time Fire Detection from MTG FCI Imagery
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 hig...
| Authors | Asaf Vanunu, Boaz Nadler, Arnon Karnieli |
|---|---|
| Primary category | cs.CV |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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