Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade
Abigail R. Azari, John B. Biersteker, Ryan M. Dewey, Gary Doran, Emily J. Forsberg, Camilla D. K. Harris, Hannah R. Kerner, Katherine A. Skinner, Andy W. Smith, Rashied Amini, Saverio Cambioni, Victoria Da Poian
arXiv:2007.15129v134 citations
Originality Synthesis-oriented
AI Analysis
This work targets planetary scientists by outlining a roadmap for integrating ML, but it is incremental as it focuses on recommendations rather than novel breakthroughs.
The paper addresses the underutilization of machine learning in planetary science despite growing data volumes, proposing ten recommendations to enhance its adoption for data analysis and insight generation.
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.