P037:
Rise of the Machine Learning: Salvation for Planetary Science in times of increasing data volume and complexity





Session ID#: 23942

Session Description:
Machine Learning (ML) is the subfield of computer science that gives "computers the ability to learn without being explicitly programmed." As tactical and strategic planning timelines compress and increasingly large nonlinear datasets are acquired, autonomy and machine intelligence has to play a more critical role in the interpretation of data from planetary exploration missions. There is a need for capable systems to be developed 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 explore research that leverages machine learning methods to enhance our scientific understanding of planetary data and increase the return of planetary exploration missions. This does include data analysis on ground as well on board to increase autonomy and/or decrease data volume and novel approaches to mission timeline planning.
Primary Convener:  Jörn Helbert, German Aerospace Center DLR Berlin, Berlin, Germany
Conveners:  Mario D'Amore, German Aerospace Center DLR Berlin, Berlin, Germany, Hannah Rae Kerner, Arizona State University, Tempe, AZ, United States and Klaus-Michael Aye, Laboratory for Atmospheric and Space Physics, Boulder, CO, United States

Cross-Listed:
  • EP - Earth and Planetary Surface Processes
  • IN - Earth and Space Science Informatics
  • NG - Nonlinear Geophysics
Index Terms:

1942 Machine learning [INFORMATICS]
6094 Instruments and techniques [PLANETARY SCIENCES: COMETS AND SMALL BODIES]
6297 Instruments and techniques [PLANETARY SCIENCES: SOLAR SYSTEM OBJECTS]

Abstracts Submitted to this Session:

Hannah Rae Kerner1, James F Bell III1 and Heni Ben Amor2, (1)Arizona State University, Tempe, AZ, United States, (2)Arizona State University, School of Computing, Informatics, and Decision Systems Engineering, Tempe, AZ, United States
Matthieu Laneuville, Tokyo Institute of Technology, Earth Life Science Institute, Tokyo, Japan, Elizabeth J Tasker, Japan Aerospace Exploration Agency, Institute of Space and Astronomical Science, Sagamihara, Japan and Nicholas Guttenberg, Araya Brain Imaging, Tokyo, Japan
Kelsey E Young1, Trevor G. Graff2, Jacob E Bleacher3, Patrick Whelley4, William Brent Garry4, A. Deanne Rogers5, Timothy D Glotch6, David Coan7, Marcum Reagan8, Cynthia A Evans9 and Daniel H Garrison10, (1)University of Maryland College Park, College Park, MD, United States, (2)Jacobs Technology, NASA Johnson Space Center, Houston, TX, United States, (3)NASA GSFC, Greenbelt, MD, United States, (4)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (5)Stony Brook University, Stony Brook, United States, (6)Stony Brook University, Stony Brook, NY, United States, (7)Aerospace at NASA JSC, Houston, United States, (8)NASA JSC, Houston, United States, (9)NASA Johnson Space Center, Astromaterials Acquisition and Curation Office, Houston, TX, United States, (10)Jacobs/NASA JSC, Houston, TX, United States
Ryan B Anderson1, Nicholas Finch1, Samuel M Clegg2, Trevor G. Graff3, Richard V Morris4, Jason Laura1 and Lisa R Gaddis5, (1)USGS Astrogeology Science Center, Flagstaff, AZ, United States, (2)Los Alamos National Laboratory, Los Alamos, NM, United States, (3)Jacobs Technology, NASA Johnson Space Center, Houston, TX, United States, (4)NASA Johnson Space Center, Astromaterials Research and Exploration Science Division, Houston, TX, United States, (5)USGS, Flagstaff, AZ, United States
M Hosein Shahnas1, David A Yuen2 and Russell Pysklywec1, (1)University of Toronto, Department of Earth Sciences, Toronto, ON, Canada, (2)Department of Earth Sciences, Minnesota Supercomputing Institute, University of Minnesota Twin Cities, Minneapolis, MN, United States
Guillaume Rongier and Victor Pankratius, MIT Haystack Observatory, Westford, MA, United States
Mario D'Amore1, Rémi Le Scaon2, Jörn Helbert3 and Alessandro Maturilli1, (1)German Aerospace Center DLR Berlin, Berlin, Germany, (2)Ecole Polytechnique, Université Paris-Saclay, Paris, France, (3)DLR, Berlin, Germany
Jörn Helbert1, Melinda Darby Dyar2, Alessandro Maturilli1, Mario D'Amore1, Sabrina Ferrari3, Nils Tobias Mueller4 and Suzanne E Smrekar5, (1)German Aerospace Center DLR Berlin, Berlin, Germany, (2)Mount Holyoke College, South Hadley, MA, United States, (3)University of Pavia, Pavia, Italy, (4)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States, (5)NASA Jet Propulsion Laboratory, Pasadena, CA, United States
Yun Li1, Yongyao Jiang1, Chaowei Phil Yang2, Edward M Armstrong3, Thomas Huang4, David F Moroni4, Christopher J Finch5 and Lewis John McGibbney4, (1)George Mason University Fairfax, Fairfax, VA, United States, (2)George Mason Univ., Fairfax, VA, United States, (3)Jet Propulsion Lab, Pasadena, CA, United States, (4)Jet Propulsion Laboratory, Pasadena, CA, United States, (5)NASA Jet Propulsion Laboratory, Pasadena, CA, United States
Melinda Darby Dyar1, Boucher F. Thomas2, Mario Parente3, Ian Gemp2 and Terry Hazel Mullen4, (1)Mount Holyoke College, South Hadley, MA, United States, (2)University of Massachusetts Amherst, College of Information and Computer Sciences, Amherst, MA, United States, (3)UMASS-Elect & Comp Engrg, Amherst, MA, United States, (4)University of Massachusetts Amherst, Electrical and Computer Engineering, Amherst, MA, United States
Terry Hazel Mullen, University of Massachusetts Amherst, Electrical and Computer Engineering, Amherst, MA, United States, Mario Parente, UMASS-Elect & Comp Engrg, Amherst, MA, United States, Ian Gemp, University of Massachusetts Amherst, College of Information and Computer Sciences, Amherst, MA, United States and Melinda Darby Dyar, Mount Holyoke College, South Hadley, MA, United States

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