Multi-scale resolution and strip feature fusion network (MSRSF-Net) was proposed in order to address the issue of fragmentation and loss of fine details in extracting long-range topological road feature from remote sensing imagery. The network was designed with a strip-shaped attention mechanism in order to enhance the feature representation of elongated road. The encoder integrated dual channel-spatial attention mechanism with multi-resolution residual branch in order to achieve collaborative cross-scale feature extraction. The decoder adopted a feature fusion architecture combining strip and square convolution, improving the topological continuity of road extraction. The experimental results on the Massachusetts, DeepGlobe and SpaceNet datasets demonstrated that MSRSF-Net achieved IoU scores of 73.76%, 68.57% and 59.98%, with APLS metrics of 69.78%, 60.27% and 62.17%, respectively, demonstrating superior performance in preserving road connectivity compared with mainstream segmentation models.