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Methodology for Statistical Classification of Biosignatures Information (SCOBI)Recent advancements in astrobiology highlight the crucial role of Machine Learning (ML) in identifying biosignatures, including analyzing terrestrial analogues for planetary mapping and habitat identification [1] and differentiating modern and ancient biosignatures [2].
Document ID
20240001165
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Abdullah Shahid (North Carolina State University Raleigh, United States)
Tao Sheng (University of Pittsburgh Pittsburgh, United States)
Sunanda Sharma (Jet Propulsion Laboratory La Cañada Flintridge, 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: 80NSSC18M0060
Distribution Limits
Public
Copyright
Use by or on behalf of the US Gov. Permitted.
Keywords
Biosignatures
Astrobiology
Machine Learning

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