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.

中文翻译：

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国际空间站上宇航员的自动识别：空间考古学案例研究

我们开发并应用了基于深度学习的计算机视觉管道，以自动识别国际空间站上拍摄的档案摄影图像中的机组人员。我们的方法能够在没有人工监督的情况下以高精度快速标记来自公共和私人照片存储库的数千张图像，包括船员面部被部分遮挡的照片。利用我们管道的结果，我们对机组人员进行了大规模的网络分析，使用图像数据为机组人员在执行任务期间的社交互动提供新的见解。