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

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 quickly tag thousands of images from public private photo repositories without human supervision with high degrees accuracy, including photographs where faces are partially obscured. Using results our pipeline, we carry out large-scale network analysis crew, using data provide novel insights into social interactions among during their missions.