IBM, NASA Open-Source Lunar AI Model Beating Rivals by 23%
Yorktown Heights, N.Y. – September 15, 2026 -- IBM and NASA released the open-source NASA-IBM Lunar Foundation Model, one of the first publicly available foundation models built for scientific exploration of the Moon, outperforming widely used detection methods by up to 23% in identifying craters, ice deposits and volcanic formations.
Model cuts ice-detection error by up to 22% versus baseline
The model reduced root-mean-square error in identifying areas with high potential for lunar ice by up to 22% compared with the SwinV2-B (ImageNet) benchmark, according to a technical paper authored by IBM and NASA researchers. Permanently shadowed lunar regions -- among the hardest environments to observe -- may hold subsurface ice, a resource NASA considers essential for a future Moon base and for producing rocket fuel for Mars missions.