MURAL - Maynooth University Research Archive Library



    Exploiting the Temporal Dimension of Remotely Sensed Imagery with Deep Learning Models


    Pereira, Agustín García and Porwol, Lukasz and Ojo, Adegboyega and Curry, Edward (2021) Exploiting the Temporal Dimension of Remotely Sensed Imagery with Deep Learning Models. In: Proceedings of the 54th Hawaii International Conference on System Sciences, January 2021.

    [img]
    Preview
    Download (640kB) | Preview


    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...



    Add this article to your Mendeley library


    Abstract

    The rapid rise of artificial intelligence and the increasing availability of open Earth Observation (EO) data present new opportunities to address important global problems such as the proliferation of agricultural systems which endanger ecological sustainability. Despite the plethora of satellite images describing a given location on earth every year, very few deep learning-based solutions have harnessed the temporal and sequential dynamics of land use to map agricultural practices. This paper compares different approaches to classify agricultural land use exploiting the temporal and spectral dimensions of EO data. The results show greater efficiency of the presented deep learning-based algorithms compared to state-of-the-art approaches when mapping agricultural classes.

    Item Type: Conference or Workshop Item (Paper)
    Keywords: Exploiting; Temporal Dimension; Remotely Sensed Imagery; Deep Learning Models;
    Academic Unit: Faculty of Social Sciences > Research Institutes > Innovation Value Institute, IVI
    Faculty of Social Sciences > School of Business
    Item ID: 15784
    Depositing User: Adegboyega Ojo
    Date Deposited: 06 Apr 2022 09:17
    Journal or Publication Title: Proceedings of the 54th Hawaii International Conference on System Sciences
    Refereed: Yes
    URI:

      Repository Staff Only(login required)

      View Item Item control page

      Downloads

      Downloads per month over past year