Document Type
Article
Publication Date
8-12-2022
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.
Recommended Citation
R.H. Ali, A.K. Kashefi, A.C. Gorman, J.S.P. Walsh, E.J. Linstead, Automated identification of astronauts on board the International Space Station: A case study in space archaeology, Acta Astronautica 200 (2022), pp. 262-269. https://doi.org/10.1016/j.actaastro.2022.08.017
Peer Reviewed
1
Copyright
The authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Included in
Archaeological Anthropology Commons, Databases and Information Systems Commons, Data Science Commons, Other Computer Engineering Commons, Other Computer Sciences Commons, Photography Commons
Comments
This article was originally published in Acta Astronautica, volume 200, in 2022. https://doi.org/10.1016/j.actaastro.2022.08.017