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  4. Machine learning on Mars: a new lens on data from planetary exploration missions
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Machine learning on Mars: a new lens on data from planetary exploration missions

Full metadata

Description

There are more than 20 active missions exploring planets and small bodies beyond Earth in our solar system today. Many more have completed their journeys or will soon begin. Each spacecraft has a suite of instruments and sensors that provide a treasure trove of data that scientists use to advance our understanding of the past, present, and future of the solar system and universe. As more missions come online and the volume of data increases, it becomes more difficult for scientists to analyze these complex data at the desired pace. There is a need for systems that can rapidly and intelligently extract information from planetary instrument datasets and prioritize the most promising, novel, or relevant observations for scientific analysis. Machine learning methods can serve this need in a variety of ways: by uncovering patterns or features of interest in large, complex datasets that are difficult for humans to analyze; by inspiring new hypotheses based on structure and patterns revealed in data; or by automating tedious or time-consuming tasks. In this dissertation, I present machine learning solutions to enhance the tactical planning process for the Mars Science Laboratory Curiosity rover and future tactically-planned missions, as well as the science analysis process for archived and ongoing orbital imaging investigations such as the High Resolution Imaging Science Experiment (HiRISE) at Mars. These include detecting novel geology in multispectral images and active nuclear spectroscopy data, analyzing the intrinsic variability in active nuclear spectroscopy data with respect to elemental geochemistry, automating tedious image review processes, and monitoring changes in surface features such as impact craters in orbital remote sensing images. Collectively, this dissertation shows how machine learning can be a powerful tool for facilitating scientific discovery during active exploration missions and in retrospective analysis of archived data.

Date Created
2019
Contributors
  • Kerner, Hannah Rae (Author)
  • Bell, James F. (Thesis advisor)
  • Ben Amor, Heni (Thesis advisor)
  • Wagstaff, Kiri L (Committee member)
  • Hardgrove, Craig J (Committee member)
  • Shirzaei, Manoochehr (Committee member)
  • Arizona State University (Publisher)
Topical Subject
  • Computer Science
  • remote sensing
  • geology
  • artificial intelligence
  • deep learning
  • Machine Learning
  • Mars
  • Planetary Science
  • remote sensing
  • Machine Learning
  • Planetary science--Data processing.
  • Planetary Science
Geographic Subject
  • Mars (Planet)--Exploration--Data processing.
  • Mars (Planet)
Resource Type
Text
Genre
Doctoral Dissertation
Academic theses
Extent
xiv, 269 pages : color illustration
Language
eng
Copyright Statement
In Copyright
Primary Member of
ASU Electronic Theses and Dissertations
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.54942
Statement of Responsibility
by Hannah Rae Kerner
Description Source
Viewed on August 26, 2020
Level of coding
full
Note
Partial requirement for: Ph.D., Arizona State University, 2019
Note type
thesis
Includes bibliographical references
Note type
bibliography
Field of study: Computer science
System Created
  • 2019-11-06 03:40:49
System Modified
  • 2021-08-26 09:47:01
  •     
  • 1 year 9 months ago
Additional Formats
  • OAI Dublin Core
  • MODS XML

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