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Earth Science Deep Learning: Applications and Lessons LearnedDeep learning has revolutionized computer vision and natural language processing with various algorithms scaled using high-performance computing. At the NASA Marshall Space Flight Center (MSFC), the Data Science and Informatics Group (DSIG) has been using deep learning for a variety of Earth science applications. This paper provides examples of the applications and also addresses some of the challenges that were encountered.
Document ID
20180008578
Acquisition Source
Marshall Space Flight Center
Document Type
Conference Paper
Authors
Maskey, Manil (NASA Marshall Space Flight Center Huntsville, AL, United States)
Ramachandran, Rahul (NASA Marshall Space Flight Center Huntsville, AL, United States)
Miller, J. J. (Alabama Univ. Huntsville, AL, United States)
Zhang, Jia (Carnegie-Mellon Univ. Moffett Field, CA, United States)
Gurung, Iksha (Alabama Univ. Huntsville, AL, United States)
Date Acquired
December 18, 2018
Publication Date
July 22, 2018
Subject Category
Cybernetics, Artificial Intelligence And Robotics
Earth Resources And Remote Sensing
Report/Patent Number
MSFC-E-DAA-TN51352
Report Number: MSFC-E-DAA-TN51352
Meeting Information
Meeting: IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2018)
Location: Valencia
Country: Spain
Start Date: July 22, 2018
End Date: July 27, 2018
Sponsors: Institute of Electrical and Electronics Engineers
Funding Number(s)
CONTRACT_GRANT: NNM11AA01A
Distribution Limits
Public
Copyright
Portions of document may include copyright protected material.
Technical Review
Single Expert
Keywords
Training
Deep Learning
Supervised Learning
Neural Network

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