IN009
Solving Training Data Bottlenecks for Artificial Intelligence/Machine Learning in Earth Science eLightning

Tuesday, 8 December 2020: 10:30-11:30
Virtual
Primary Convener:  Manil Maskey, NASA Marshall Space Flight Center, MSFC, Huntsville, AL, United States
Conveners:  Hamed Alemohammad, Radiant Earth Foundation, San Francisco, CA, United States, Rahul Ramachandran, NASA Marshall Space Flight Center, Huntsville, AL, United States and Subit Chakrabarti, Indigo Agriculture, Boston, MA, United States
Primary Liaison:  Manil Maskey, University of Alabama in Huntsville, Huntsville, AL, United States
Chairs:  Manil Maskey, University of Alabama in Huntsville, Huntsville, AL, United States, Hamed Alemohammad, Radiant Earth Foundation, San Francisco, CA, United States, Rahul Ramachandran, NASA Marshall Space Flight Center, Huntsville, AL, United States and Subit Chakrabarti, Indigo Agriculture, Boston, MA, United States
OSPA Liaison:  Hamed Alemohammad, Radiant Earth Foundation, San Francisco, CA, United States
10:30
IN009-01
Deep learning for label-scarce remote sensing applications (Invited) (767250)
Sherrie Wang and David B Lobell, Stanford University, Stanford, CA, United States
10:33
IN009-02
Fine-tuning land cover models efficiently with transfer learning: a tool and case studies (Invited) (745005)
Caleb Robinson, Anthony Ortiz, Siyu Yang, MD Nasir, Jane Wang and Juan Lavista Ferres, Microsoft Corporation, AI For Good Research Lab, Redmond, United States
10:36
IN009-03
Leveraging Global Crop-Land Datasets to Improve Model Performance for Crop Classification in Data-Sparse Regions (715413)
Gabriel Tseng1, Hannah Rae Kerner2, Inbal Becker-Reshef3 and Catherine Lilian Nakalembe3, (1)University of Maryland/NASA Harvest, College Park, United States, (2)University of Maryland College Park, Geography, College Park, MD, United States, (3)University of Maryland College Park, Geographical Sciences, College Park, MD, United States
10:39
IN009-04
Post-season and in-season crop type classification for smallholder farms: reducing reliance on labeled data by learning latent features in unlabeled data (683976)
Hannah Rae Kerner1, Gabriel Tseng2, Inbal Becker-Reshef3 and Catherine Lilian Nakalembe3, (1)University of Maryland College Park, Geography, College Park, MD, United States, (2)University of Maryland/NASA Harvest, College Park, United States, (3)University of Maryland College Park, Geographical Sciences, College Park, MD, United States
10:42
IN009-05
Removing Infrastructure Noise in Airborne Electromagnetic Data with Deep Learning (705660)
Burke J Minsley1, Natalya Rapstine2, Nathan Leon Foks2 and Bethany Burton1, (1)USGS, Geology, Geophysics, and Geochemistry Science Center, Denver, CO, United States, (2)USGS, Advanced Research Computing Science Analytics and Synthesis, Denver, CO, United States
10:45
IN009-06
Expanding NeMO-Net Machine Learning Capabilities for Citizen Science (754863)
Alan Sheng Xi Li1, Ved Chirayath1, Jarrett van den Bergh1 and Juan Luis Torres-Perez2, (1)NASA Ames Research Center, Moffett Field, CA, United States, (2)NASA Ames Research Center, Earth Science Division, Moffett Field, CA, United States
10:48
IN009-08
LandCoverNet: Generating a Human-Verified Global Land Cover Classification Training Dataset (768191)
Hamed Alemohammad, Radiant Earth Foundation, San Francisco, CA, United States
10:51
IN009-09
A Novel Automatic Learning-based Method of Training Sample Selection Using Multiple Datasets for Time-series Land Cover Mapping (743023)
Congcong Li1, George Z Xian2 and Qiang Zhou1, (1)USGS Earth Resources Observation and Science (EROS) Center Sioux Falls, Sioux Falls, SD, United States, (2)USGS EROS, Sioux Falls, SD, United States
10:54
IN009-10
Labeling and Managing Image Data for Machine Learning in the Earth Sciences (751973)
Prasanna Koirala1, Ashish Acharya1, Brian Freitag1, Iksha Gurung1, Manil Maskey2 and Rahul Ramachandran3, (1)University of Alabama in Huntsville, Huntsville, AL, United States, (2)NASA Marshall Space Flight Center, MSFC, Huntsville, AL, United States, (3)NASA Marshall Space Flight Center, Huntsville, AL, United States
10:57
IN009-11
A Quantitative Analysis on the Use of Supervised Machine Learning in Earth Science (737530)
George Priftis1, Katrina Virts2, Ashlyn Shirey2, Muthukumaran Ramasubramanian2, Hassan Muhammad2, Ashish Acharya2 and Rahul Ramachandran3, (1)University of Alabama in Huntsville, Atmospheric Science, Huntsville, AL, United States, (2)University of Alabama in Huntsville, Huntsville, AL, United States, (3)NASA Marshall Space Flight Center, Huntsville, AL, United States
11:00
IN009-12
Generating Synthetic Training Data for Satellite Imagery Applications (768853)
Tharun Mohandoss1, Aditya Kulkarni1, Hamed Alemohammad1, Daniel Northrup2 and Ernest Mwebaze3, (1)Radiant Earth Foundation, San Francisco, CA, United States, (2)Benson Hill, St. Louis, MO, United States, (3)Google, Accra, Ghana
11:03
Discussion
See more of: Earth and Space Science Informatics
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