Many facets of research in planetary science rely on analyzing large volumes of in situ and remote spacecraft data. Developments in machine learning (ML) techniques are gradually augmenting traditional manual data collection and analysis pipelines. This advancement highlights the need for automation capable of efficiently and intelligently extracting information from large datasets.

This session is dedicated to data-driven research that leverages ML and data science to enhance our scientific understanding and return from planetary data and missions. Topics may include studies from Earth-based data relevant to planetary applications, as well as existing and future planetary missions. Submissions are welcome for applications across science and engineering, including but not limited to: operations, on-board autonomy, and mission planning; surface, atmosphere, and space environment characterization; object detection, classification, and segmentation; change detection; ML augmented physics-based models; interpretable methods and uncertainty quantification; foundation models; and broader ML and data science applications to planetary science.