NASA-IBM Lunar Foundation Model Goes Open Source With a 2M-Tile Dataset and 22% Lower Ice-Mapping Error

IBM and NASA have released the NASA-IBM Lunar Foundation Model as open source, one of the first publicly available foundation models built for scientific study of the Moon. The weights, a technical report, and the machine-learning-ready dataset it was trained on are up on Hugging Face under the Prithvi family, which already covers Earth observation, weather, and heliophysics. The pitch is that decades of multi-instrument lunar data have outgrown hand-sifted maps and narrow, task-specific models, and a single pretrained backbone can be adapted to crater mapping, volcanic-feature detection, and ice prospecting without starting over each time. In IBM and NASA’s own benchmarks, the model cut root-mean-square error in flagging likely ice deposits by up to 22% against a SwinV2-B baseline.

A TerraMind Backbone on 30 Layers From Nine Instruments

The model is built on a version of TerraMind, the Earth-observation model IBM developed with the European Space Agency, chosen for how it…