IBM and NASA released an open-source AI model on 10 September designed to help scientists analyse decades of lunar observations and support plans for a sustained human presence on the Moon.
The NASA-IBM Lunar Foundation Model is a publicly available tool for studying the Moon. It was trained on more than 30 layers of data collected by nine instruments on four NASA missions, including the Lunar Reconnaissance Orbiter.
It joins IBM and NASA's Prithvi family of open foundation models, which spans geospatial, weather and other applications. The model can help researchers identify potential ice deposits in the Moon's permanently shadowed regions, map craters to select safe landing sites and study volcanic features — tasks that traditionally required scientists to sift through maps and images by hand or rely on lower-resolution machine-learning tools.
In benchmark tests, the model identified key features on the lunar surface up to 23 per cent more accurately than widely used methods, NASA and IBM said.
Lunar ice is of particular interest to space agencies because it indicates the presence of water and oxygen, resources considered essential for a future Moon base and for producing rocket fuel for missions to Mars. NASA's Artemis programme plans to return astronauts to the Moon in 2028, testing technology for a sustained lunar presence and future Mars missions.
