Cyber-Resilient Multimodal Learning for Forest Fire Detection and Risk Prediction via Drone and Satellite Observations
TL;DR
An Adaptive Trust-Aware Hierarchical Multimodal Learning Network (ATHML-Net) that combines drone image features, satellite observations, meteorological factors, and terrain data in a single cyber-resilient learning system is introduced, providing a robust, accurate, and cyber-resilient solution for real-time forest fire detection and proactive wildfire risk prediction.
Abstract
Climate change, longer drought periods, and increased human activities have led to forests being burnt with greater frequency and intensity, with the consequences being a risk to ecosystems and to biodiversity as well as to public personnel and their health. Current monitoring systems using drones or satellites have enhanced wildfire monitoring, but there is still limited integration of features across modalities, insufficient cybersecurity resilience, and limited capacity to dynamically predict wildfire risk. To overcome these challenges, this paper introduces an Adaptive Trust-Aware Hierarchical Multimodal Learning Network (ATHML-Net) that combines drone image features, satellite observations, meteorological factors, and terrain data in a single cyber-resilient learning system. The proposed model aims to quantify the credibility of the heterogeneous data sources, design a physics-informed wildfire propagation learning mechanism to learn from fire and its propagation process for accurate spatiotemporal risk prediction, and propose a mechanism to estimate feature reliability based on trustworthiness to fuse features from different sources, which is robust against cheating attacks. The proposed framework has been shown to be effective through extensive experiments on publicly available multimodal wildfire datasets. The ATHML-Net detection achieves 99.21% accuracy, with precisions and recalls of 98.96% and 98.84%, respectively, yielding an F1 score of 98.90% and an AUC of 99.47%, reducing wildfire risk prediction error to an RMSE of 0.081. The framework maintained an average inference time of 23.8 ms/frame across all cases and maintained a detection accuracy of 96.83% in adversarial attack scenarios, representing an average 3.8% improvement over the best baseline framework. These results are highly effective for real-time forest fire detection and proactive wildfire risk prediction, providing a robust, accurate, and cyber-resilient solution.