Lunar Foundation Model
Description
Funded by the Office of the Chief Science Data Officer, the Lunar Foundation Model (LFM) is a joint effort between GSFC, MSFC, and IBM that will harness a large and diverse array of multi-modal datasets from recent missions with the goals to 1) Create a working example of a FM that demonstrates the construction and scientific use of FMs in planetary science, 2) Expand the community of machine learning practitioners within the lunar community and across planetary science, and 3) Pilot the use of FMs to lunar science and/or lunar exploration applications.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
1) Create a working example of a FM that demonstrates the construction and scientific use of FMs in planetary science, 2) Expand the community of machine learning practitioners within the lunar community and across planetary science, and 3) Pilot the use of FMs to lunar science and/or lunar exploration applications.
Controls / human review
ATO: Not reported; PIA: Not published