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This paper proposes a novel method of extracting roads and bridges from high-resolution remote sensing images based on deep learning. Edge detection is performed on the images in the road area along with the road skeleton line, and the result of the detected binary edge is vectorized. The interference of protective belts on both sides of the road, road vehicles, road green belts, traffic signs, etc. and the shadow interference of the bridge itself are eliminated to determine the parallel sides of the road. The bridge features on the road are used to locate the detected bridge and obtain information such as the location, length, width, and direction of the bridge, verifying the experimental results of the Shaoguan Le point images. In addition, in order to learn higher-level road feature information, the algorithm in this paper introduces the hollow convolution and multicore pooling modules. Secondly, the residual refinement network further refines the output of the prediction network to improve the ambiguity of the prediction network results. In addition, in view of the small proportion of road pixels in remote sensing images, the network also integrates binary cross entropy, structural similarity, and intersection ratio loss function to reduce road information loss. The applicability of the proposed study was tested, and the results show that the algorithm is very effective for the extraction of road and bridge targets.

Bridge Extraction Algorithm Based on Deep Learning and High-Resolution Satellite Image