Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade
Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:ZSTMBBQCrecord.jsonopen to challenge →
classification
astro-ph.IM
astro-ph.EPstat.ML
keywords
planetarylearningmachinemethodsscienceabilityapplicationsbolstering
read the original 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.
This paper has not been read by Pith yet.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.