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, Tadhg M., Garton, Michael D. Himes, Sarah Millholland
TL;DR
This paper discusses how machine learning can significantly enhance planetary science by analyzing large datasets, and offers ten strategic recommendations to foster ML integration in the field over the next decade.
Contribution
It provides a set of ten recommendations aimed at advancing the adoption and effectiveness of machine learning in planetary science.
Findings
ML can greatly improve data analysis in planetary science.
The paper offers strategic recommendations for ML integration.
Enhanced ML use can accelerate discoveries in planetary research.
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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