Technical Program: Paper Detail || IEEE IGARSS 2023 || Pasadena, California, USA || 16

WE3.R17.5

DDM-FORMER: GLOBAL OCEAN WIND SPEED RETRIEVAL WITH TRANSFORMER NETWORKS

Daixin Zhao, Technische Universität München; German Aerospace Center, Germany; Konrad Heidler, Technische Universität München, Germany; Milad Asgarimehr, German Research Centre for Geosciences; Technische Universität Berlin, Germany; Caroline Arnold, German Climate Computing Center, Germany; Tianqi Xiao, Jens Wickert, German Research Centre for Geosciences; Technische Universität Berlin, Germany; Xiao Xiang Zhu, Technische Universität München, Germany; Lichao Mou, German Aerospace Center; Technische Universität München, Germany

Session:

WE3.R17: Machine Learning for GNSS Remote Sensing Oral

Location:

Room 212/214

Presentation Time:

Wed, 19 Jul, 13:48 - 14:00 Pacific Time (UTC -8)

Session Co-Chairs:

Milad Asgarimehr, Technische Universität Berlin; Massachusetts Institute of Technology and Lei Liu, University of Colorado Boulder and Benedikt Soja, ETH Zurich and Yang Wang, CU Boulder

Session Manager:

Shankho Subhra Pal

Session WE3.R17

WE3.R17.1: Machine learning-based exploitation of crowdsourced GNSS data for atmospheric studies

Benedikt Soja, Grzegorz Kłopotek, Yuanxin Pan, Laura Crocetti, Shuyin Mao, Mudathir Awadaljeed, Markus Rothacher, ETH Zurich, Switzerland; Linda See, Tobias Sturn, Rudi Weinacker, Ian McCallum, International Institute for Applied Systems Analysis, Austria; Vicente Navarro, European Space Agency, Austria

WE3.R17.2: MACHINE LEARNING APPLICATIONS FOR CLASSIFICATION AND RETRIEVAL OF SURFACE PARAMETERS FROM GNSS-R.

Emanuele Santi, Simone Pettinato, Institute of Applied Physics – National Research Council, Italy; Davide Comite, Nazzareno Pierdicca, La Sapienza University, Italy; Laura Dente, Leila Guerriero, Tor Vergata University, Italy; Maria Paola Clarizia, NIcolas Floury, European Space Agency, Netherlands

WE3.R17.3: A DEEP LEARNING APPROACH FOR DETECTION OF INTERNAL GRAVITY WAVES IN EARTH’S IONOSPHERE

Valentino Constantinou, Jet Propulsion Laboratory, California Institute of Technology, United States; Michela Ravanelli, Sapienza University of Rome, Italy; Hamlin Liu, Jacob Bortnik, University of California - Los Angeles, United States

WE3.R17.4: THE MUON SPACE DEEP-LEARNING FRAMEWORK FOR GENERALIZED RETRIEVALS FROM GNSS-R : SOIL MOISTURE AND OCEAN WIND SPEED

T. Maximillian Roberts, Dallas Masters, Clara Chew, Stephen Lowe, Dan McCleese, Muon Space, United States

WE3.R17.5: DDM-FORMER: GLOBAL OCEAN WIND SPEED RETRIEVAL WITH TRANSFORMER NETWORKS