NASA and IBM have released an open-source AI model trained on a large collection of lunar observations to help scientists analyze the Moon at scale. "The NASA-IBM Lunar Foundation Model gives scientists a foundation to explore the Moon at scale, connecting observations across instruments, revealing patterns that are difficult to see in isolation, and providing an open platform the global research community can build on," said IBM director of research for Europe, Juan Bernabe-Moreno. The Register reports: It is claimed as the first AI model to integrate observations captured in a range of modalities (data formats), and at different viewing angles and spatial scales. Instead of sifting through maps and images by hand or using low resolution machine learning models, scientists can use this to analyze geographic features, the pair say. In particular, NASA and IBM hope researchers will be able to discover previously unidentified lunar ice deposits, analyze volcanic features called Irregular Mare Patches, and identify and classify craters.

Lunar ice indicates the presence of water and oxygen, which may be useful for future manned missions. It is found in permanently shadowed regions, which are among the most difficult areas to observe. The NASA-IBM model combines multimodal and multi-resolution observations to better predict where ice may be present on the lunar surface. Alongside the model, IBM and NASA scientists compiled an open-source lunar dataset from over 30 spatially-aligned layers, using data from nine instruments across four missions. It combines tens of thousands of images and maps showing various geophysical properties of the lunar surface.

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