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.
@article{arxiv.2007.15129,
title = {Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade},
author = {Abigail R. Azari and John B. Biersteker and Ryan M. Dewey and Gary Doran and Emily J. Forsberg and Camilla D. K. Harris and Hannah R. Kerner and Katherine A. Skinner and Andy W. Smith and Rashied Amini and Saverio Cambioni and Victoria Da Poian and Tadhg M. Garton and Michael D. Himes and Sarah Millholland and Suranga Ruhunusiri},
journal= {arXiv preprint arXiv:2007.15129},
year = {2023}
}
Comments
10 pages (expanded citations compared to 8 page submitted version for decadal survey), 3 figures, white paper submitted to the Planetary Science and Astrobiology Decadal Survey 2023-2032