BRPE-UNet: A Multi-Scale Feature Enhancement Network with Polarized Self-Attention and Adaptive Edge Refinement for Farmland Road Extraction

Abstract

The precise extraction of field roads in hilly and terrace farmlands poses great challenges for remote sensing analysis. Farm roads are narrow and irregular, with spectra close to crop ridges, and frequently covered by tree and building shadows. Traditional segmentation methods easily produce missed detection, broken and distorted road outlines. Most existing deep learning networks are optimized for cities and lack generalization to complex rural scenes. This work presents a multi-scale feature enhancement and edge-aware network (BRPE-Unet), an improved U-Net for accurate rural road extraction from high-resolution remote sensing images. An optimized DenseASPP block is inserted into encoders to fix U-Net’s weak multi-scale feature capacity and fuse tiny road details with global context. Parallel Polarized Self-Attention is added to skip links to highlight road areas and suppress crop and shadow noise. A new Adaptive Edge Refinement module is placed at the decoder output, using image-dependent threshold generation and multi-directional gradient information to refine blurred and fragmented road boundaries and improve segmentation continuity. Tests on DeepGlobe Road and WHU-RuR+ datasets yield IoUs of 67.54% and 52.30%, respectively, and the proposed BRPE-UNet demonstrates competitive road extraction performance among the representative methods evaluated in this study. BRPE-UNet can reliably extract rural roads and offers technical support for farm modernization and rural revitalization.