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International Geoscience and Remote Sensing Symposium

In 2021 a joint initiative of Belgium and The Netherlands

TH1.O-2.4

THE CASE FOR OPEN-ACCESS ML-READY GEOSPATIAL TRAINING DATA

Hamed Alemohammad, Radiant Earth Foundation, United States
Session:
Artificial Intelligence and Machine Learning Methods for Big Earth Data
Track:
Invited Sessions
Presentation Time:
Thu, 15 Jul, 09:15-09:30 (UTC)
Thu, 15 Jul, 11:15-11:30 Central Europe Summer Time (UTC +2)
Thu, 15 Jul, 17:15-17:30 China Standard Time (UTC +8)
Thu, 15 Jul, 05:15-05:30 Eastern Daylight Time (UTC -4)
Session Co-Chairs:
Manil Maskey, National Aeronautics and Space Administration (NASA) and Weiguo Han, UCAR
Session Manager:
Srikumar Sastry
Presentation
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Discussion
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Resources
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Session TH1.O-2
TH1.O-2.1: Augmenting Data Systems with Prediction based Embeddings
Rahul Ramachandran, National Aeronautics and Space Administration (NASA), United States; Muthukumaran Ramasubramanian, Iksha Gurung, University of Alabama in Huntsville, United States; Carson Davis, Manufacturing Technical Solutions, United States; Derek Koehl, University of Alabama in Huntsville, United States; Manil Maskey, Tsengdar Lee, National Aeronautics and Space Administration (NASA), United States
TH1.O-2.3: TOWARDS MACHINE LEARNING SUPPORT ON COVERAGES
Otoniel José Campos Escobar, Peter Baumann, Jacobs University Bremen, Germany
TH1.O-2.4: THE CASE FOR OPEN-ACCESS ML-READY GEOSPATIAL TRAINING DATA
Hamed Alemohammad, Radiant Earth Foundation, United States
TH1.O-2.5: Applying Machine Learning to Cropland Data Layer for Agro-geoinformation Discovery
Chen Zhang, George Mason University, United States; Zhengwei Yang, US Department of Agriculture, United States; Liping Di, Li Lin, Pengyu Hao, Liying Guo, George Mason University, United States
TH1.O-2.6: Preliminary Report on Deep Learning-based Daytime Clear-Sky Radiance for VIIRS
Xingming Liang, University of Maryland, United States; Quanhua Liu, NOAA NESDIS, United States

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