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The use of Deep Learning (DL) algorithms has improved the performance ofvision-based space applications in recent years. However, generating largeamounts of annotated data for training these DL algorithms has provenchallenging. While synthetically generated images can be used, the DL modelstrained on synthetic data are often susceptible to performance degradation,when tested in real-world environments. In this context, the InterdisciplinaryCenter of Security, Reliability and Trust (SnT) at the University of Luxembourghas developed the 'SnT Zero-G Lab', for training and validating vision-basedspace algorithms in conditions emulating real-world space environments. Animportant aspect of the SnT Zero-G Lab development was the equipment selection.From the lessons learned during the lab development, this article presents asystematic approach combining market survey and experimental analyses forequipment selection. In particular, the article focus on the image acquisitionequipment in a space lab: background materials, cameras and illumination lamps.The results from the experiment analyses show that the market surveycomplimented by experimental analyses is required for effective equipmentselection in a space lab development project.

Lessons from a Space Lab -- An Image Acquisition Perspective