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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:
- 9 months ago
- License:
- Attribution
- Share this:
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Automated identification of astronauts on board the International Space Station: A case study in space archaeology