USRA Contributes to Open-Source AI Model for Lunar Research

The Universities Space Research Association (USRA) contributed planetary science expertise to the development of the NASA-IBM Lunar Foundation Model, an open-source artificial intelligence model designed for lunar research. The model combines diverse datasets collected by lunar missions and allows researchers to analyze large amounts of data quickly.

The NASA-IBM Lunar Foundation Model was pretrained on SomBench, a multimodal dataset containing nearly two million co-registered data bundles spanning 11 modalities and two spatial scales. This allowed the model to learn relationships among different types of lunar observations rather than treating each dataset independently.

USRA's contribution to the project was provided by Dr. Rachel Slank, an associate scientist with USRA's Science and Technology Institute. She served as a planetary science subject-matter expert on the NASA-IBM Lunar Foundation Model team and helped connect lunar science priorities with model development and evaluation.