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.

    Highlights Photographs taken on the ISS give rich insight into the social structures onboard. Astronauts can be identified with high accuracy using pre-trained face detection models. Multi-expedition astronauts socialize more with crew from their first expedition. Astronauts are photographed with crew from the same expedition 64% of the time.


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    Titel :

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


    Beteiligte:

    Erschienen in:

    Acta Astronautica ; 200 ; 262-269


    Erscheinungsdatum :

    2022-08-08


    Format / Umfang :

    8 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch