Primary Convener:
Ramanakumar Sankar, University of California Berkeley

Convener:
Abigail Azari, University of Alberta
Hannah Kerner, Arizona State University
Lior Rubanenko, Tel Aviv University

Early Career Convener:
Ramanakumar Sankar, University of California Berkeley

Chair:
Ramanakumar Sankar, Florida Institute of Technology
Abigail Azari, University of California Berkeley
Mirali Purohit, Arizona State University

Many facets of research in planetary science rely on analyzing large volumes of in-situ and remote spacecraft data. Traditionally, these data were collected and analyzed manually. In recent decades, developments in machine learning (ML) techniques have begun to gradually augment traditional methods, answering the need for automation that can efficiently and intelligently extract information from large datasets in a useful manner.This session will be 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 encompass studies from Earth-based data and existing and future planetary missions. Submissions are welcome for applications across science and engineering, including but not limited to: spacecraft operations and mission planning; surface, atmosphere, and space environment; object detection and classification; change detection; ML augmented physics-based models; interpretable methods; and other studies that apply ML and data science methods to planetary science.

Index Terms
1906 Computational models, algorithms
1916 Data and information discovery
1942 Machine learning
6297 Instruments and techniques

Cross-Listed:
SH - SPA-Solar and Heliospheric Physics
SY - Science and Society
SM - SPA-Magnetospheric Physics
EP - Earth and Planetary Surface Processes

Neighborhoods:
4. Beyond Earth

Suggested Itineraries:
Space Weather
Machine Learning and AI