TransUV: A TransNeXt-Based Model with Multi-Scale and Attention Fusion for Fine-Grained Urban Village Extraction

Urban villages (UVs) are widespread in rapidly urbanizing regions, but their fine-grained delineation from high-resolution remote sensing imagery remains a challenge due to complex spatial textures and ambiguous boundaries. To address this issue, this paper proposes TransUV, a TransNeXt-based encoder–decoder segmentation framework tailored to UV extraction. At the encoder front end, a Multi-level Feature Enhancement Module (MFEM) injects boundary- and texture-aware inductive bias by combining Laplacian-of-Gaussian (LoG) filtering with Gaussian smoothing, which strengthens edge responses while suppressing noise. At the decoder stage, we design a lightweight SegUV decoder equipped with an Advanced Attention Fusion Module (AAFM) that adaptively fuses multi-scale features using complementary channel, spatial, and directional attention. Experiments on 0.5 m imagery from two Chinese cities demonstrate that TransUV achieves an mIoU of 86.67% and an overall accuracy of 92.98%, significantly outperforming other mainstream models.