HPLNet: a hierarchical perception lightweight network for road extraction

With the progression of remote sensing technologies, extracting road networks from satellite imagery has emerged as a pivotal research domain in both Geographic Information Systems and Intelligent Transportation Systems. Recognizing the difficulty in balancing lightweight network design with extraction accuracy, the challenge of synergistically preserving global road connectivity and local details, and the hardship in effectively integrating low-level features with high-level representations to achieve full coupling between road details and semantic understanding in road extraction from remote sensing images, this study introduces a Hierarchical Perception Lightweight Network for road extraction (HPLNet). This innovative network integrates shallow perception part and deep perception part, aiming to optimize the trade-off between inference efficiency and extraction accuracy. In shallow perception, directional stripe convolutions capture road details, while deep perception integrates a spatial-channel semantic awareness network to bridge local and global information, boosting road semantic feature extraction. Moreover, to extend the model’s reception at both pixel and semantic levels, each network component strategically introduces parameter-free channel shift operations. HPLNet attains state-of-the-art efficiency in balancing parameter footprint and inference latency: its parameter count is merely 22% of that of U-Net, while its inference speed is 18% faster than FCN. Concurrently, it delivers competitive segmentation metrics on the Massachusetts dataset, achieving an IoU of 64.32% and an F1 score of 79.96%. Experimental results demonstrate that the proposed network achieves superior performance in both segmentation accuracy and model complexity, thereby offering an efficient solution for real-time deployment on edge devices.