A Multimodal Deep Learning Framework for Accurate Wildfire Segmentation Using RGB and Thermal Imagery

Wildfires pose serious threats to ecosystems, human life, and climate stability, underscoring the urgent need for accurate monitoring. Traditional approaches based on either optical or thermal imagery often fail under challenging conditions such as lighting interference, varying data sources, or small-scale flames, as they do not account for the hierarchical nature of feature representations. To overcome these limitations, we propose a multimodal deep learning framework that integrates visible (RGB) and thermal infrared (TIR) imagery for accurate wildfire segmentation. The framework incorporates edge-guided supervision and multilevel fusion to capture fine fire boundaries while exploiting complementary information from both modalities. To assess its effectiveness, we constructed a multi-scale flame segmentation dataset and validated the method across diverse conditions, including different data sources, lighting environments, and five flame size categories ranging from small to large. Experimental results show that BFCNet achieves an IoU of 88.25% and an F1 score of 93.76%, outperforming both single-modality and existing multimodal approaches across all evaluation tasks. These results demonstrate the potential of multimodal deep learning to enhance wildfire monitoring, offering practical value for disaster management, ecological protection, and the deployment of autonomous aerial surveillance systems.