RoadFocusNet: road extraction from remote sensing imagery using focused transformer and focused masked image modeling

Road extraction from remote sensing (RS) imagery is crucial for urban management, traffic planning, and autonomous driving. However, extracting accurate and complete roads remains challenging due to occlusions and severe class imbalance, where non-road regions dominate. To address these challenges, we propose a novel road extraction method incorporating two key components. The first is a Focused Masked Image Modeling (FocusMIM) strategy for data augmentation, which randomly masks road-related regions to efficiently model the latent dependency between occluded and non-occluded road parts. With FocusMIM, the model's ability to infer occluded roads is obviously improved. The second is a Focused Transformer (FocusFormer), which enhances road-related feature interactions through a Transformer-based encoder with Channel Self-Attention (CSA) modules and a Transformer decoder that leverages masked attention. The CSA modules aggregate global features of RS images to enhance contextual inference and mitigate occlusions. Meanwhile, the Transformer decoder employs a single road query that attends exclusively to road features, alleviating the class imbalance issue. Comprehensive experiments on the DeepGlobe Road, Massachusetts Road, and CHN6-CUG datasets demonstrate that our method outperforms several state-of-the-art methods, achieving an IoU increase of 0.96–5.38%. These results confirm the effectiveness of FocusMIM and FocusFormer in improving road continuity and reducing background interference.