IBM and NASA release open-source AI model for lunar ice, crater mapping

IBM and NASA have released an open-source AI model to help scientists analyse decades of lunar data. The model, trained on data from nine instruments across four NASA missions, can identify ice deposits and map craters with up to 23 per cent greater accuracy than existing methods.

IBM and NASA on Thursday released an open-source AI model designed to help scientists analyse decades of lunar observation data and support plans for a sustained human presence on the Moon.

The NASA-IBM Lunar Foundation Model is a publicly available AI tool trained on more than 30 layers of data collected by nine instruments on four NASA missions, including the Lunar Reconnaissance Orbiter. It joins the Prithvi family of open foundation models developed by IBM and NASA, which also cover geospatial, weather and other applications.

The model can help researchers identify potential ice deposits on the Moon's permanently shadowed regions, map craters to select safe landing sites and study volcanic features. These tasks have traditionally required scientists to manually sift through maps and images 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 program plans to return astronauts to the Moon in 2028, testing new technology for a sustained lunar presence and future Mars missions.