RCDA-Net: a residual contextual dual attention network for wildfire spread region prediction

Wildfire spread prediction is a critical task in remote sensing image analysis, where accurately identifying newly expanded wildfire regions is essential for real-time monitoring and emergency response. In this study, we propose a semantic segmentation network, termed the Residual Contextual Dual Attention Network (RCDA-Net), for predicting incremental wildfire spread regions from multi-source remote sensing data. RCDA-Net integrates two attention modules, Contextual Anchor Attention (CAA) and Adaptive Graph Channel Attention (AGCA), to enhance spatial structure modelling and inter-channel dependency learning across spectral, topographic, and meteorological inputs. We construct and release a large-scale wildfire dataset covering North America, which serves as a benchmark for incremental fire spread prediction. Experimental results on this dataset show that RCDA-Net achieves an F1-score of 0.471 and an IoU of 0.308, outperforming established models such as U-Net, AttU-Net, WPN, and FU-NetCast. With 8.9 million parameters and an inference speed of 131 frames per second (fps), RCDA-Net provides a favourable balance between segmentation accuracy and computational efficiency. Ablation studies validate the complementary effects of CAA and AGCA, while additional analyses demonstrate that Dice Loss effectively mitigates class imbalance and boundary ambiguity. Robustness evaluations further indicate that wildfire masks and meteorological factors play a dominant role in predictive performance under partial input degradation. The source code is publicly available at: https://github.com/hxxAlways/RCDA-Net.