Machine Learning (ML) is the subfield of computer science that gives "computers the ability to learn without being explicitly programmed." As increasingly large, complex datasets are acquired, autonomy and machine intelligence will have a 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. This session will explore research that leverages machine learning methods to enhance our scientific understanding of planetary data and to increase the scientific return from exploration missions. This session will also explore ways of introducing machine learning into existing analysis workflows. Topics may encompass data analysis on the ground (post-downlink) as well as onboard a spacecraft for existing or future planetary exploration missions and instruments.

• IN - Earth and Space Science Informatics