DENet: Direction and Edge Co-Awareness Network for Road Extraction From High-Resolution Remote Sensing Imagery

Automatic road extraction from high-resolution remote sensing images has greatly facilitated the applications of high-precision road mapping in autonomous driving and intelligent transportation. However, challenges such as occlusions from buildings, trees, and complex road shapes bring great difficulties to precise road extraction. Also, existing methods often overlook the integrity of road direction and edge, leading to unsatisfactory extraction results. To alleviate the issue, this paper has presented a direction and edge co-awareness network (DENet). Firstly, the road edge detector (RED) is introduced to extract coarse road edges with abundant directional information. By leveraging the edge enhancement blocks, the edge structures of road can be efficiently refined, achieving the extraction of intricate narrow and elongated road shapes. Secondly, we incorporate the directional spatial attention (DSA) mechanism within the dual encoders and decoders to promote the extraction and fusion of road directional information and elongated features from different orientations, thus greatly mitigating the road occlusion issue. Finally, to fully interlace potential road information, a grouped local-global feature fusion (GLFF) is specifically designed to exchange multi-scale semantic information across different channels, simultaneously emphasizing road features and suppressing irrelevant background features. Numerous experimental results on three public datasets demonstrate the effectiveness and efficiency of the proposed DENet for road extraction, achieving F1 scores of 78.51% on the CHN6-CUG dataset, 79.35% on the Massachusetts road dataset, and 77.90% on the GF2-FC dataset, outperforming several existing state-of-the-art methods. The code is available at: https://github.com/gwy103/DENet.