Roads are one of the most important earth features in the satellite imagery. Its analysis plays a great role in urban/rural planning and its development. Several methodologies have been developed earlier towards this direction but most of them have used high-resolution satellite images. This research uses the freely available medium spatial resolution multi-spectral satellite images, i.e., Sentinel-2A from European Space Agency (ESA) and implemented & analyzed various state-of-art deep learning models, and architectures which are specifically tuned for road extraction, i.e., ResUnet, ResUnet-a, U-Net, Attention U-Net, LinkNet, & Dilated-LinkNet. It has been identified that the Attention U-Net based model has out-performed the other models in road extraction from medium spatial resolution satellite. Also, the work analyzed the optimum loss function suitable for road extraction in such resolution. The work can be utilized for urban road extraction for societal benefits.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Road Extraction from Medium Resolution Satellite Images using Deep Learning Techniques


    Contributors:
    Gupta, Ayan (author) / Dixit, Mayank (author) / Sharma, Vishnu (author) / Nand, Parma (author)


    Publication date :

    2022-12-16


    Size :

    2582387 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Road Extraction from Satellite Images Using Maximum Entropy Threshold Method

    Zhou, Miaolan / Chen, Nuo / Hu, Wenjing et al. | IEEE | 2023


    Automated extraction of road network from medium-and high-resolution images

    Poz, A. P. / Zanin, R. B. / Vale, G. M. | British Library Online Contents | 2006


    Energy-based Method for Road Extraction from Satellite Images

    Ke, Q. / Xiao, J. / Yang, Z. et al. | British Library Conference Proceedings | 1996


    Extraction of Main Urban Roads from High Resolution Satellite Images by Machine Learning

    Wang, Y. / Tian, Y. / Tai, X. et al. | British Library Conference Proceedings | 2006