Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade
A. Azari
J. Biersteker
R. Dewey
Gary Doran
Emily J. Forsberg
C. Harris
Hannah Kerner
Katherine A. Skinner
Andy W. Smith
R. Amini
S. Cambioni
V. D. Poian
T. Garton
Michael D. Himes
S. Millholland
S. Ruhunusiri

Abstract
Machine learning (ML) methods can expand our ability to construct, and draw insight from large datasets. Despite the increasing volume of planetary observations, our field has seen few applications of ML in comparison to other sciences. To support these methods, we propose ten recommendations for bolstering a data-rich future in planetary science.
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