As increasingly large and complex datasets are acquired, autonomy and machine intelligence will have a critical role in interpreting data from planetary missions. There is a need for frameworks that can rapidly and intelligently extract information from these datasets in a useful manner for scientific analysis. Such innovations will largely be driven by machine learning (ML) and data science methods.

This session will explore research leveraging ML and data science methods to enhance our scientific understanding and scientific return from planetary data and missions. Topics may encompass data analysis on the ground or onboard spacecraft for existing and future missions and instruments. Submissions are welcome for applications across science and engineering, including but not limited to: spacecraft operations and mission planning; surface, atmosphere, and space environment characterization; object detection and classification; change and novelty detection; interpretable methods; and other studies that apply ML and data science methods to planetary science.