TransUV: A TransNeXt-Based Model with Multi-Scale and Attention Fusion for Fine-Grained Urban Village Extraction.
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| Title: | TransUV: A TransNeXt-Based Model with Multi-Scale and Attention Fusion for Fine-Grained Urban Village Extraction. |
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| Authors: | Lin, Xiaobao1 (AUTHOR), Wang, Yu2 (AUTHOR) wangy@secmep.cn, Zhou, Yaming1,2 (AUTHOR), Wang, Guangjun1,2 (AUTHOR), Chen, Sai1 (AUTHOR) |
| Source: | Remote Sensing. Jan2026, Vol. 18 Issue 2, p223. 24p. |
| Subjects: | Remote sensing, Statistical accuracy, Urban research, Urban growth, Mathematical models |
| Abstract: | Highlights: What are the main findings? The novel model introduced leverages a modified TransNeXt backbone with multi-scale attention fusion for the fine-grained extraction of urban villages (UVs) from high-resolution remote sensing imagery. The model comprises a Multi-level Feature Enhancement Module (MFEM) and an Advanced Attention Fusion Module (AAFM) to enhance boundary clarity and texture representation. It achieves an mIoU of 86.67% and an overall accuracy of 92.98% and outperforms several mainstream models. What are the implications of the main findings? TransUV demonstrates strong generalization capability across complex urban environments, which facilitates the precise delineation of irregular and densely structured UVs. The proposed approach achieves a balance between efficiency and accuracy, making it a robust solution for urban village extraction in urban renewal and monitoring applications. 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? The novel model introduced leverages a modified TransNeXt backbone with multi-scale attention fusion for the fine-grained extraction of urban villages (UVs) from high-resolution remote sensing imagery. The model comprises a Multi-level Feature Enhancement Module (MFEM) and an Advanced Attention Fusion Module (AAFM) to enhance boundary clarity and texture representation. It achieves an mIoU of 86.67% and an overall accuracy of 92.98% and outperforms several mainstream models. What are the implications of the main findings? TransUV demonstrates strong generalization capability across complex urban environments, which facilitates the precise delineation of irregular and densely structured UVs. The proposed approach achieves a balance between efficiency and accuracy, making it a robust solution for urban village extraction in urban renewal and monitoring applications. 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18020223 |
