Meta-Optimized Continual Adaptation for planetary geology survey missions for extreme data sparsity scenarios

Meta-Optimized Continual Adaptation for planetary geology survey missions for extreme data sparsity scenarios Introduction: The Martian Conundrum That Changed My Approach to AI I remember the exact moment when the limitations of conventional machine learning became painfully clear. I was working with a team analyzing data from the Perseverance rover, trying to train a model to identify rare mineral formations in Jezero Crater. We had terabytes of data from Earth-based analogs, but only a handful of validated samples from Mars itself. The model performed beautifully on our test sets—until we deployed it on actual Martian data. The accuracy plummeted from 94% to 37% overnight. This experience fundamentally shifted my perspective on AI for space exploration. While studying reinforcement learning papers late one night, I realized we were approaching the problem backward. We were trying to cram Earth knowledge into Martian applications, rather than building systems that could learn and adap