Extracting rural road networks from remote sensing images is crucial for data-driven precision agriculture planning. However, traditional semantic segmentation methods often struggle to achieve both high-precision boundary delineation and topological integrity, especially in heterogeneous rural landscapes. To address these issues, this study proposes MACER-UNet, a novel connectivity-aware road extraction model that integrates multi-scale perception and edge enhancement capabilities. Specifically, MACER-UNet employs ResNet-50 as the backbone network to extract robust deep semantic features. Within the encoder–decoder framework, an atrous spatial pyramid pooling module (ASPP) is embedded to capture rich multi-scale context cues, thereby enhancing robustness to varying road widths and inconsistent imaging conditions. During the decoding process, the convolutional block attention module (CBAM) recalibrates features to reduce noise from the agricultural background. The edge enhancement module (EEM) extracts high-frequency gradient cues for geometric correction and boundary sharpening. This architecture combines spatial attention and edge constraints to balance recognition accuracy and topological connectivity. On the public WHU-CR dataset, MACER-UNet achieved an intersection over union (IoU) of 50.37% and an F1 score of 67.02%, outperforming U-Net (44.27%), DeepLabv3+ (49.43%), and D-LinkNet (49.54%), and its connectivity was comparable to recent state-of-the-art road extraction methods such as C2Net (49.37%) and CGCNet (50.34%). On a self-built dataset with a 3 m resolution in Suihua, the model achieved an IoU of 42.56% and an F1 score of 59.71%. The evaluation results confirm that MACER-UNet provides a road network with geometric consistency and topological integrity for spatial analysis in rural environments.
Tang et al. (2026) studied this question.