HSN-Net: A Hybrid Segmentation Neural Network for High-Resolution Road Extraction

Abstract

Road network information is a core component of online maps and plays a crucial role in navigation, urban planning, and traffic management. Convolutional neural networks (CNNs) have demonstrated remarkable performance in road extraction tasks. However, their limited ability to capture global information often leads to fragmented road segments when roads are occluded by other terrains in satellite images, ultimately undermining the accuracy and continuity of the segmentation results. Given the strengths of transformers in capturing global contextual information and CNNs in extracting local detailed features, this letter introduces a novel deep network called hybrid segmentation neural network (HSN-Net), which seamlessly integrates transformers with CNNs to leverage the advantages of both the architectures. To further enhance road continuity, we propose the road continuity perception module (RCPM). Experiments on the DeepGlobe and CHN6-CUG datasets demonstrate that our HSN-Net achieves state-of-the-art segmentation performance in road extraction tasks, validating the effectiveness of our design choices. The source code is available at https://github.com/hb281/HSN-Net