This paper presents an analysis of data derived from thousands of publicly available photographs showing life on the International Space Station (ISS) between 2000 and 2020. Our analysis uses crew and locational information from the photographs’ metadata to identify the distribution of different population groups—by gender, nationality, and spa…[Read more]

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,…[Read more]

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,…[Read more]

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,…[Read more]