Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade

In one of the most profound examples, the first image of a black hole was captured by applying a machine learning algorithm to petabytes of data collected from eight telescopes [1]. Since planetary science's last decadal survey, the use of machine learning has increased in each division of NASA's Science Mission Directorate (SMD). However, even though the number of planetary science publications involving machine learning has grown exponentially over the last ten years, it lags in both percent share and growth rate compared to heliophysics, astrophysics, and Earth science (Figure 1). In this white paper, we assert that planetary science, similar to related disciplines, has the opportunity to leverage machine learning methods for scientific advancement in our field.