Real-Time Forest Fire Detection and Spread Prediction UsingTransformer-Based Vision Models on Edge Computing Nodes: A CaseStudy of the Aegean Pine Forest Zone, Greece (Aintelia Science Notes, 2025)
Increasing forest fire frequency in Eastern Mediterranean Pinus brutia ecosystems demands rapid automated detection and real-time spread prediction. This paper presents EgeoFireNet, an end-to-end edge-computing system deployed on NVIDIA Jetson Orin NX nodes within a 43-camera dual-channel monitoring network in Chios, Greece. The system integrates a fine-tuned Swin Transformer detection model with a physics-informed cellular automaton spread prediction module. Optimized via TensorRT INT8, the detection model processes 22.4 FPS, achieving a 0.889 mAP@0.5 and a 0.912 F1-score for fire and smoke. Initialized by real-time segmentation masks and sensor data, the prediction module generates 15-minute georeferenced forecasts in 4.2 seconds with a 0.76 Sorensen-Dice coefficient. EgeoFireNet reduces median end-to-end alert latency to 87 seconds a 12-to-24-fold improvement over conventional helicopter patrols while maintaining an operational false positive rate of 0.023 alerts/camera/hour.