• Automated identification of astronauts on board the International Space Station: A case study in space archaeology

    Author(s):
    Rao Hamza Ali, Alice Gorman, Erik Linstead, Justin Walsh (see profile)
    Contributor(s):
    Amir Kanan Kashefi
    Date:
    2022
    Group(s):
    Anthropology, Archaeology, Digital Archaeology
    Subject(s):
    International Space Station
    Item Type:
    Article
    Permanent URL:
    https://doi.org/10.17613/xz05-ar06
    Abstract:
    We develop and apply a deep learning-based computer vision pipeline to automatically identify crew members in archival photographic imagery taken on-board the International Space Station. Our approach is able to quickly tag thousands of images from public and private photo repositories without human supervision with high degrees of accuracy, including photographs where crew faces are partially obscured. Using the results of our pipeline, we carry out a large-scale network analysis of the crew, using the imagery data to provide novel insights into the social interactions among crew during their missions.
    Metadata:
    xml
    Published as:
    Journal article     Show details
    Pub. DOI:
    https://doi.org/10.1016/j.actaastro.2022.08.017
    Publisher:
    Elsevier BV
    Pub. Date:
    2022-8-12
    Journal:
    Acta Astronautica
    Volume:
    200
    Page Range:
    262 - 269
    ISSN:
    0094-5765
    Status:
    Published
    Last Updated:
    1 year ago
    License:
    Attribution
    Share this:

    Downloads

    Item Name: pdf 1-s2.0-s0094576522004210-main.pdf
      Download View in browser
    Activity: Downloads: 397

    Back to Deposits