AI, ML, and CV are widely applicable and in use in many aspects of the space enterprise. Automating ground-based analysis of immense astronomical datasets dates back to the 1990's and continues for both visual and radio astronomy with the Intermediate Palomar Transient Factory, the Zwicky Transient Factory, and Very Long Baseline Array Fast Radio Transients Experiment (V-FASTR) and many others. AI/ML/CV techniques are being pushed to the edge onboard rovers at Mars such as WATCH on the MER rovers and AEGIS autonomous targeting on the MER, MSL, and M2020 rovers. Closer to Earth, such techniques can be used to enable Earth Observing assets to avoid cloudy data and hunt for specific science phenomena such as deep convective storms. In some cases large numbers of assets are networked together to track volcanoes, flooding, wildfires, and other dynamic phenomena. We discuss these use cases, challenges, and lessons learned from decades of deploying AI/ML/CV for space applications.
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