TH1.R8.2
EFFICIENT SPATIOTEMPORAL CHARACTERISATION OF RIVER PLUMES USING DEEP LEARNING: THE CASE OF SURUGA BAY, JAPAN
Takashi Kobayashi, Kirara Kotani, Kota Sato, Masaki Hisada, NTT, Inc. Space Environment and Energy Laboratories, Japan
Session:
TH1.R8: Remote sensing for coastal sustainability Oral
Track:
Community Contributed Themes
Location:
TBD
Presentation Time:
Thu, 13 Aug, 08:45 - 09:00
Session Co-Chairs:
Jing Ling, Guangdong University of Technology and Hongsheng Zhang, The University of Hong Kong
Presentation
Discussion
Resources
No resources available.
Session TH1.R8
TH1.R8.1: Information Theory-Based Causal Analysis Applied to Ocean Satellite Data
Kazuki Kohyama, Kirara Kotani, Takashi Kobayashi, Helen Stewart, Rin Irie, Masaki Hisada, NTT, Inc., Japan
TH1.R8.2: EFFICIENT SPATIOTEMPORAL CHARACTERISATION OF RIVER PLUMES USING DEEP LEARNING: THE CASE OF SURUGA BAY, JAPAN
Takashi Kobayashi, Kirara Kotani, Kota Sato, Masaki Hisada, NTT, Inc. Space Environment and Energy Laboratories, Japan
TH1.R8.4: HIGH-RESOLUTION OPTICAL DEEP LEARNING FOR SEAGRASS CHANGE DETECTION WITH ICESAT-2 DEPTH ANALYSIS
Anthony Campbell, University of Maryland, Baltimore County, United States; Peter Macreadie, RMIT University, Australia; Lindsey Smart, The Nature Conservancy, United States; Lola Fatoyinbo, Massachusetts Institute of Technology, United States; James Cordery, Jocelyn Panjikaran, Six Senses, Maldives; Chris Neigh, NASA Goddard Space Flight Center, United States
Contacts