FR2.R15.3
A SPATIAL AUTOCORRELATION-BASED KERNEL REMOVAL SCHEME FOR RESOURCE-EFFICIENT PREDICTION OF REMOTELY SENSED DATA USING CNN
Monidipa Das, Indian Institute of Science Education and Research Kolkata, India
Session:
FR2.R15: Data-Driven and Physics-Based Learning Oral
Track:
AI and Big Data
Location:
TBD
Presentation Time:
Fri, 14 Aug, 11:30 - 11:45
Session Co-Chairs:
Jens Nieke, European Space Research and Technology Centre (ESTEC) and Corrado Chiatante,
Presentation
Discussion
Resources
No resources available.
Session FR2.R15
FR2.R15.1: RadarGaugeNet2: Gauge-Supervised Multimodal AI for Minute-to-Hour Precipitation Nowcasting
Ron Sarafian, Weizmann Institute of Science, Israel; Sagi Nathan, Hebrew University of Jerusalem, Israel; Dori Nissenbaum, Weizmann Institute of Science, Israel; Meira Barron, Hebrew University of Jerusalem, Israel; Yoav Levi, Israel Meteorological Service, Israel; Yinon Rudich, Weizmann Institute of Science, Israel
FR2.R15.2: PHYSICS-INFORMED MACHINE LEARNING FOR SHORT-TERM FLOOD PREDICTION
Tewodros Gebre, Jagrati Talreja, Leila Hashemi-Beni, North Carolina Agricultural and Technical State University, United States
FR2.R15.3: A SPATIAL AUTOCORRELATION-BASED KERNEL REMOVAL SCHEME FOR RESOURCE-EFFICIENT PREDICTION OF REMOTELY SENSED DATA USING CNN
Monidipa Das, Indian Institute of Science Education and Research Kolkata, India
Contacts