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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:
    Guo, Tan (author) / Gao, Ying (author) / Luo, Fulin (author) / Zhang, Lei (author) / Du, Bo (author) / Gao, Xinbo (author)


    Publication date :

    2025-07-01


    Size :

    6374656 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Denet -- Fastening new markets

    Online Contents | 2007


    Road-following and traffic analysis using high-resolution remote sensing imagery

    Kahaki, Seyed Moslafa Mousavi / Fathy, Mahmood / Ganj, Mohsen | Tema Archive | 2009



    Knowledge-Based Road Extraction from High Resolution Remotely Sensed Imagery

    Shen, Jing / Lin, Xiangguo / Shi, Yunfei et al. | IEEE | 2008