Foundation Model for Lunar Science

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The purpose of Lunar FM is to overcome the limitations of traditional, task-specific Machine Learning (ML) models in analyzing the vast, diverse, and long-term datasets collected by the Lunar Reconnaissance Orbiter (LRO) mission. This includes addressing data challenges such as heterogeneity in spatial and temporal resolution, differences in data formats and calibration standards, and the significant lack of large, accurately labeled datasets (sparse ground truth) for supervised learning.