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Curating a Standardized Dataset for Statistical Biosignature Classification In recent years, machine learning has been explored as a toolkit for planetary science and operations [Helbert, Azari]. Machine learning has been used to improve our understanding of possible biosignatures and mineral signatures to improve science return on future missions [Warren-Rhodes, Cleaves].
Document ID
20240001162
Acquisition Source
Ames Research Center
Document Type
Extended Abstract
Authors
Tao Sheng (University of Pittsburgh at Greensburg Greensburg, Pennsylvania, United States)
Sunanda Sharma (California Institute of Technology Pasadena, United States)
Abdullah Shahid (North Carolina State University Raleigh, United States)
Diana Gentry (Ames Research Center Mountain View, United States)
Date Acquired
January 25, 2024
Subject Category
Earth Resources and Remote Sensing
Meeting Information
Meeting: Astrobiology Science Conference (AbSciCon)
Location: Providence, RI
Country: US
Start Date: May 5, 2024
End Date: May 10, 2024
Sponsors: American Geophysical Union
Funding Number(s)
CONTRACT_GRANT: AMESVE10012012
Distribution Limits
Public
Copyright
Use by or on behalf of the US Gov. Permitted.
Keywords
Biosignatures
Astrobiology
Machine Learning

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