P11E:
Rise of Machine Learning: Salvation for Planetary Science in Times of Increasing Data Volume and Complexity I Posters

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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, School of Computing and Augmented Intelligence, Tempe, AZ, United States and Klaus-Michael Aye, Laboratory for Atmospheric and Space Physics, Boulder, CO, United States
Chairs:  Jörn Helbert, German Aerospace Center DLR, Berlin, Germany and Klaus-Michael Aye, Laboratory for Atmospheric and Space Physics, Boulder, CO, United States
OSPA Liaison:  Jörn Helbert, German Aerospace Center DLR, Berlin, Germany

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:

The Python Spectral Analysis Tool (PySAT): A Powerful, Flexible, Preprocessing and Machine Learning Library and Interface (233006)
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, United States, (3)Jacobs Technology, NASA Johnson Space Center, Houston, TX, United States, (4)NASA Johnson Space Center, Houston, TX, United States, (5)USGS, Flagstaff, AZ, United States
A Geospatial Data Recommender System based on Metadata and User Behaviour (268281)
Yun Li1, Yongyao Jiang1, Chaowei Phil Yang2, Edward M Armstrong3, Thomas Huang3, David F Moroni4, Christopher J Finch5 and Lewis John McGibbney6, (1)George Mason University Fairfax, Fairfax, VA, United States, (2)George Mason Univ., Fairfax, United States, (3)NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States, (4)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States, (5)NASA Jet Propulsion Laboratory, Pasadena, CA, United States, (6)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States
A Deep Learning Approach to LIBS Spectroscopy for Planetary Applications (282922)
Terry Hazel Mullen1, Mario Parente1, Ian Gemp2 and Melinda Darby Dyar3, (1)University of Massachusetts Amherst, Electrical and Computer Engineering, Amherst, MA, United States, (2)University of Massachusetts Amherst, College of Information and Computer Sciences, Amherst, MA, United States, (3)Mount Holyoke College, Department of Astronomy, South Hadley, United States

See more of: Planetary Sciences