Over the past decades of NASA’s inner solar system exploration, data obtained from the Moon alone accounts for ~76%. Most of the lunar orbital spacecraft of the past and present carried imaging cameras and spectrometers (including multispectral and hyperspectral payloads), as well as a large variety of other passive and active instruments. For example, NASA’s Lunar Reconnaissance Orbiter (LRO) has been operating for more than 10 years, providing us with ~1206 TB of lunar data which amounts to ~99.5% of the total data contributed by NASA built instruments. Given recent advances in instrument and communication capabilities, the amount of data returned from spacecraft is expected to keep rising quickly. The white paper focus on potential components of AI and ML that could help to accelerate the future exploration of the Moon and other planetary bodies. The white paper highlights on selected AI/ML-based approaches for lunar and planetary surface science and exploration, the need for open-source availability of training, validation, and testing datasets for AI-ML based approaches, and need for opportunities to further bridge the gap between industry and academia for advancing AI-ML based research in lunar and planetary science and exploration.

Multiscale Dynamics

Varatharajan, I., Bickel, V., Angerhausen, D., Antoniadou, E., Shukla, S., Maiti, A., … D' Amore, M. (2020). Artificial Intelligence for the Advancement of Lunar and Planetary Science and