StripUnet: A Method for Dense Road Extraction From Remote Sensing Images

Road extraction from high-resolution remote sensing images can provide vital data support for applications in urban and rural planning, traffic control, and environmental protection. However, roads in many remote sensing images are densely distributed with a very small proportion of road information against a complex background, significantly impacting the integrity and connectivity of the extracted road network structure. To address this issue, we propose a method named StripUnet for dense road extraction from remote sensing images. The designed Strip Attention Learning Module (SALM) enables the model to focus on strip-shaped roads; the designed Multi-Scale Feature Fusion Module (MSFF) is used for extracting global and contextual information from deep feature maps; the designed Strip Feature Enhancement Module (SFEM) enhances the strip features in feature maps transmitted through skip connections; and the designed Multi-Scale Snake Decoder (MSSD) utilizes dynamic snake convolution to aid the model in better reconstructing roads. The designed model is tested on the public datasets DeepGlobe and Massachusetts, achieving F1 scores of 83.75% and 80.65%, and IoUs of 73.04% and 67.96%, respectively. Compared to the latest state-of-the-art models, F1 scores improve by 1.07% and 1.11%, and IoUs increase by 1.28% and 1.07%, respectively. Experiments demonstrate that StripUnet is highly effective in dense road network extraction.