Road extraction networks fusing multiscale and edge features

Extracting roads using remote sensing images is of great significance to urban development. However, due to factors such as variable scale of roads and easy to be obscured, it leads to problems such as road miss detection and incomplete edges. To address the above problems, this paper proposes a network (MeD-Net) for road extraction from remote sensing images integrating multi-scale features and focusing on edge detail features. MeD-Net consists of two parts: road segmentation and edge extraction. The road segmentation network uses multi-scale deep feature processing (MDFP) module to extract multiscale features taking into account global and local information, and is trained using group normalization optimization model after convolution. The edge extraction network uses detail-guided fusion algorithms to enhance the detail information of deep edge features and uses attention mechanisms for feature fusion. To verify the algorithm performance, this paper conducts experiments using the Massachusetts road dataset and the GF-2 road dataset in Qingdao area. The experiments show that MeD-Net achieves the highest accuracy in both datasets in terms of intersection-over-union ratio and Fl value, and is able to extract roads at different scales and maintain road edges more completely. © 2025 SinoMaps Press. All rights reserved.