R-SWTNet: A Context-Aware U-Net-Based Framework for Segmenting Rural Roads and Alleys in China with the SQVillages Dataset

Jianing Wu, Junqi Yang, Xiaoyu Xu, Ying Zeng, Yan Cheng, Xiaodong Liu and Hong Zhang ()
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Jianing Wu: School of Architecture, Tsinghua University, Beijing 210008, China
Junqi Yang: School of Architecture, Tsinghua University, Beijing 210008, China
Xiaoyu Xu: School of Architecture, Tsinghua University, Beijing 210008, China
Ying Zeng: School of Architecture and Civil Engineering, Xiamen University, Xiamen 361005, China
Yan Cheng: School of Architecture, Tsinghua University, Beijing 210008, China
Xiaodong Liu: School of Architecture, Tsinghua University, Beijing 210008, China
Hong Zhang: School of Architecture, Tsinghua University, Beijing 210008, China

Land, 2025, vol. 14, issue 10, 1-27

Abstract: Rural road networks are vital for rural development, yet narrow alleys and occluded segments remain underrepresented in digital maps due to irregular morphology, spectral ambiguity, and limited model generalization. Traditional segmentation models struggle to balance local detail preservation and long-range dependency modeling, prioritizing either local features or global context alone. Hypothesizing that integrating hierarchical local features and global context will mitigate these limitations, this study aims to accurately segment such rural roads by proposing R-SWTNet, a context-aware U-Net-based framework, and constructing the SQVillages dataset. R-SWTNet integrates ResNet34 for hierarchical feature extraction, Swin Transformer for long-range dependency modeling, ASPP for multi-scale context fusion, and CAM-Residual blocks for channel-wise attention. The SQVillages dataset, built from multi-source remote sensing imagery, includes 18 diverse villages with adaptive augmentation to mitigate class imbalance. Experimental results show R-SWTNet achieves a validation IoU of 54.88% and F1-score of 70.87%, outperforming U-Net and Swin-UNet, and with less overfitting than R-Net and D-LinkNet. Its lightweight variant supports edge deployment, enabling on-site road management. This work provides a data-driven tool for infrastructure planning under China’s Rural Revitalization Strategy, with potential scalability to global unstructured rural road scenes.

Keywords: rural road segmentation; U-Net; Swin Transformer; multi-scale context fusion; remote sensing imagery (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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