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

Rao Hamza Ali, Amir Kanan Kashefi, Alice C. Gorman, Justin St P. Walsh, Erik J. Linstead

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
59 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)262-269
Number of pages8
JournalACTA ASTRONAUTICA
Volume200
Early online date12 Aug 2022
DOIs
Publication statusPublished - Nov 2022

Keywords

  • Amazon rekognition API
  • Computer vision
  • Deep neural networks
  • Face detection and identification
  • International Space Station
  • Social analysis

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