TH2.M2.4
Addressing remotely sensed precipitation data accuracy and sparsity with deep learning
Di Tian, Fang Wang, Auburn University, United States
Session:
TH2.M2: Give Earth a Chance: AI Algorithms for Environmental Monitoring II Oral
Track:
Community-Contributed Sessions
Location:
Room M2
Presentation Time:
Thursday, 7 August, 11:15 - 11:30
Session Co-Chairs:
Agata M. Wijata, KP Labs / Silesian University of Technology and Jakub Nalepa, KP Labs / Silesian University of Technology
Presentation
Discussion
Resources
No resources available.
Session TH2.M2
TH2.M2.1: EFFICIENT PLASTIC DETECTION IN COASTAL AREAS WITH SELECTED SPECTRAL BANDS
Ámbar Pérez-García, Universidad de Las Palmas de Gran Canaria, Spain; Tim H.M. van Emmerik, Wageningen University, Netherlands; Aser Mata, Plymouth Marine Laboratory, United Kingdom; Paolo F. Tasseron, Wageningen University, Netherlands; José F. López, Universidad de Las Palmas de Gran Canaria, Spain
TH2.M2.2: SeeKelp: AI-Driven Global Monitoring of Floating Kelp via Sentinel-2
James Lowman, Mojtaba Valipour, Wajahat Ali, Jesse Uszkay, Coastal Carbon, Canada
TH2.M2.3: Pixels to Insights: Deep learning for Floodwater Depth Mapping in Settlement Areas
Jeffrey Blay, Leila Hashemi-Beni, North Carolina Agricultural And Technical University, United States
TH2.M2.4: Addressing remotely sensed precipitation data accuracy and sparsity with deep learning
Di Tian, Fang Wang, Auburn University, United States
TH2.M2.5: ASSESSMENT OF PLANT ESTABLISHMENT AND MORTALITY ON A REVEGETATED BACKFILLED MINE PIT USING DRONE IMAGERY AND DEEP LEARNING
Andrew Esparon, Kirrilly Pfitzner, Timothy Whiteside, Jarrod Hodgson, Gabriel Maicas Suso, Joshua Koh, Department of Climate Change, Energy, the Environment and Water, Australia
Resources
No resources available.