We invite you to submit both introductory and case study abstracts to our AGU session:
P025: Machine Learning in Planetary Science: Introductions and Applications
Machine Learning (ML) is the subfield of computer science that gives “computers the ability to learn without being explicitly programmed.” As increasingly large nonlinear datasets are acquired, autonomy and machine intelligence have to play a more critical role in the interpretation of data from planetary exploration missions. There is a need for frameworks that can rapidly and intelligently extract information from these datasets in a manner useful for scientific analysis. The community is starting to respond to this need by applying machine learning approaches on various levels. This session will present ways on introducing machine learning into your workflow and explore research that leverages machine learning methods to enhance our scientific understanding of planetary data, increasing the return of planetary exploration missions. This does include data analysis on ground as well as on board a spacecraft to increase autonomy and/or decrease data volume and novel approaches to mission timeline planning.
Your session conveners,
K.-Michael Aye (LASP), J. Helbert (DLR), Mario D’Amore (DLR), H.R. Kerner (ASU)