CA-MTransUNet: Cloud-Aware Mixture-of-Experts Linear Transformer U-Net for forest burned area (FBA) mapping using Sentinel-1 and Sentinel-2 images

Wildfires are becoming more frequent and intense, which highlights the need for precise and effective forest burned area (FBA) detection. Current burn mapping approaches are hindered by challenges including the integration of multimodal datasets, high computational complexity of traditional attention mechanisms in segmentation models, and cloud contamination in optical satellite imagery. To address these issues, we proposed the Cloud-Aware Mixture-of-Experts Linear Transformer U-Net (CA-MTransU-Net). Our model integrates Sentinel-1 SAR and Sentinel-2 optical satellite data using a novel dynamic weighting approach, employs a computationally efficient Mixture-of-Experts (MoE) linear attention mechanism to effectively capture global feature dependencies, and incorporates a cloud-weighting method specifically designed to reduce the adverse impacts of cloud cover in optical satellite data. The developed architecture significantly outperformed several well-known segmentation algorithms, including U-Net, ResNet, SegFormer, TransU-Net, PSPNet, and DeepLabv3+, achieving the highest mean Intersection-over-Union (mIoU) of 87.00%, surpassing baseline models by an average of +6.29%. It also demonstrated superior computational efficiency with faster inference speeds (6.26 ms) compared to conventional transformer-based models like SegFormer (7.81 ms) and TransU-Net (13.17 ms). Despite its achievements, the model exhibits higher peak memory usage, which may limit deployment in resource-constrained environments. Additionally, like other tested models, it occasionally misclassified water bodies as burned areas.