  Images of spacecraft photographed from other spacecraft operating in outer
space are difficult to come by, especially at a scale typically required for
deep learning tasks. Semantic image segmentation, object detection and
localization, and pose estimation are well researched areas with powerful
results for many applications, and would be very useful in autonomous
spacecraft operation and rendezvous. However, recent studies show that these
strong results in broad and common domains may generalize poorly even to
specific industrial applications on earth. To address this, we propose a method
for generating synthetic image data that are labelled for semantic
segmentation, generalizable to other tasks, and provide a prototype synthetic
image dataset consisting of 2D monocular images of unmanned spacecraft, in
order to enable further research in the area of autonomous spacecraft
rendezvous. We also present a strong benchmark result (S{\o}rensen-Dice
coefficient 0.8723) on these synthetic data, suggesting that it is feasible to
train well-performing image segmentation models for this task, especially if
the target spacecraft and its configuration are known.
