Attention-Driven Separable Convolutional Networks for Efficient and Explainable Forest Fire Detection: ForestFlameNet

Forest fires present a significant challenge, which requires advanced detection systems to mitigate their impact on ecosystems and human settlements. The evolving landscape of remote sensing and machine learning offers new avenues for enhancing the speed and accuracy of fire detection methods. This paper introduces ForestFlameNet, an innovative deep learning model that incorporates separable convolutions and attention mechanisms tailored to efficiently analyze satellite and aerial imagery to detect early signs of wildfires. ForestFlameNet stands out by integrating a lightweight architecture that maintains high accuracy while being computationally efficient while offering comparable performance with state of the art models. With a model size of only $\mathbf{0. 7 5 4 M}$ parameters, ForestFlameNet ensures efficiency without compromising performance, making it highly suitable for deployment on resource-limited devices. The Local Interpretable Model-Agnostic Explanations (LIME) bolstered the model’s efficacy, illustrating its predictive decisions, and increasing transparency and trust in its outputs. t-Distributed Stochastic Neighbor Embedding (t-SNE) and Gradient-weighted Class Activation Mapping (Grad-CAM) also help to understand how the model recognizes features by showing how different parts of the input data affect the outcome of the detection.