Session 3A AI Applied to Airborne or Spaceborne Earth Observation Datasets

Tuesday, 14 January 2020: 8:30 AM-10:00 AM
156A (Boston Convention and Exhibition Center)

Cochairs:
James M. Kurdzo, MIT Lincoln Laboratory, Lexington, MA and Sid Boukabara, NOAA/NESDIS, College Park, MD

Description:

This session focuses on AI applied to satellite or aircraft data.


Papers:
8:30 AM
3A.1
NN Technique for Producing Consistent Ocean Color Data for Assimilation in Ocean Models
Vladimir Krasnopolsky Krasnopolsky, NOAA, College Park, MD

8:45 AM
3A.2
Machine Learning for inpainting QuikSCAT winds in Hawaii's Lee Region
William Chapman, 9500 Gilman Dr., La Jolla, CA; SIO, La Jolla, CA; SIO, La Jolla, CA; and T. J. Kilpatrick

9:00 AM
3A.3
Using Deep Learning to Extract Regions of Interest (ROI) in Real-Time from Geostationary Satellite Data
Christina Kumler, NOAA, Boulder, CO; and J. Stewart, D. Hall, and M. Govett

9:15 AM
3A.4
The optimal single-scattering properties for retrieving ice cloud properties based on machine learning techniques
Yi Wang, Texas A&M University, College Station, TX; and P. Yang and Y. Huang

9:30 AM
3A.5
Neural Network Techniques for Hyperspectral IR Profiling of Cloudy Atmospheres
Adam B. Milstein, MIT Lincoln Laboratory, Lexington, MA; and W. J. Blackwell

9:45 AM
3A.6
Optical Flow for Intermediate Frame Interpolation of Multispectral Geostationary Satellite Data
Thomas Vandal, NASA / BAERI, Mountain View, CA; and R. Nemani

Abstract Submissions Closed



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