Design and optimisation of remote sensing image road segmentation network integrating multi-scale features

In response to the problems of insufficient multi-scale feature extraction, difficulty in preserving edge details, and insufficient road connectivity in remote sensing image road segmentation, a multi-scale feature refinement network is proposed. The network adopts a two-stage design architecture. In the first stage, a multi-scale feature enhancement module is combined with a window transformer encoder, a global information enhancement module, and an attention enhancement module to effectively express multi-scale road targets. Finally, the complete segmentation result is obtained through weighted fusion of spatial and channel alignment. The results showed that the average intersection to union ratio index reached 92.1%, 89.7%, 88.3%, and 88.9% on the Massachusetts Road Dataset, DeepGlobe Road Extraction Dataset, Space Net-3 Dataset, and Urban Landscape Dataset, respectively. The boundary score index reached 0.91, 0.89, 0.88, and 0.88, respectively. The median road connectivity index reached 0.89, the number of road fractures decreased to 3.2 per kilometre, and the road accessibility rate reached 92.6%. Therefore, the multi-scale feature refinement network effectively solves the key technical problems in remote sensing image road segmentation through two-stage progressive optimisation, providing an effective technical solution for road information extraction in intelligent transportation systems.