LAUSR

Automated road extraction from very high-resolution (VHR) remote sensing imagery is important in many practical applications and has a long research history. Due to the diversity, narrowness, and sparsity of the road nature, extracting a full detailed road network remains a challenge, especially in the presence of interference. When applying semantic segmentation to deal with road extraction, U-Net-based architectures have achieved great progress through the use of dilated convolution or residual structure. However, the existing methods rarely focus on shape completeness and road continuity, and in fact, these are essential for road extraction. Inspirit by the multitask learning, in this letter, we present a novel road extraction architecture called gated auxiliary edge (GAE)-LinkNet with semantic segmentation as the main task and edge detection as the auxiliary task. With the proposed GatedBlocks, redundant features are filtered out and shape-relevant features stand out. Through the task loss weighing mechanism, these two tasks can work together seamlessly to make better use of the shape features. Experiments on a public road data set show that the proposed method is superior to state-of-the-art road extraction methods.