NASA and IBM released an open lunar AI model trained on two million data bundles
Rachel Slank aligned 11 data types and labeled 49,000 craters; the GitHub release includes downstream code but omits the pretraining code.
By Ryan Merket · Published
Primary source: USRA
Why it matters
The project shows how domain experts can turn fragmented public science data into reusable AI infrastructure, while the missing pretraining code limits full reproducibility.
Planetary scientist Rachel Slank helped NASA and IBM turn decades of mismatched Moon observations into the NASA-IBM Lunar Foundation Model, an open model released on September 10th for crater mapping, volcanic-feature analysis and research into possible polar ice.…
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