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NASA, in collaboration with IBM, has unveiled a new open-source AI model designed to help researchers analyze years’ worth of data on Earth’s moon and advance plans for long-term human presence there.
The NASA-IBM Lunar Foundation application is a publicly available tool designed for the detailed study of the lunar surface. The platform was trained on more than 30 layers of data collected by nine instruments during four NASA space missions, including those conducted by the Lunar Reconnaissance Orbiter. The new project expands the lineup of open-source Prithvi base models from IBM and NASA. These platforms cover geospatial, meteorological, and other related applications, providing scientists with powerful open-source tools for processing large datasets.
The system can help experts identify potential ice deposits in permanently shadowed regions of the Moon, create crater maps to identify safe landing sites, and study volcanic features – that is, to perform tasks that previously required scientists to manually analyze maps and images or use machine learning algorithms with lower resolution.
During comparative tests, the system demonstrated the ability to identify key terrain features with 23% greater accuracy compared to common methods, according to representatives from NASA and IBM. Lunar ice is of particular interest to space agencies because it indicates the presence of water and oxygen – valuable resources considered essential for a future lunar base and for producing fuel for missions to Mars.
As part of NASA’s Artemis lunar mission, astronauts are scheduled to travel to the Moon in 2028 to test the latest technologies needed for long-term exploration of the Moon and future expeditions to Mars.
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