Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade
Whitepaper #128 submitted to the Planetary Science and Astrobiology Decadal Survey 2023-2032. Topics: state of the profession; technology development; theory, computation, and modeling
by Abigail 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, and Suranga Ruhunusiri
Published onMar 18, 2021
Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade
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.
Creative Commons Attribution 4.0 International License (CC-BY 4.0)
