FR2.R15.4
Ship detection using ALOS PALSAR raw data without synthetic aperture
Syusuke Yasui, Fumitaka Ogushi, Yokoya Hiroshi, Kazushi Motomura, Naruo Kanemoto, Space Shift Inc.
Session:
FR2.R15: Data-Driven and Physics-Based Learning Oral
Track:
AI and Big Data
Location:
Jefferson West
Presentation Time:
Friday, 14 August, 11:45 - 12:00
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; Sagi Nathan, Hebrew University of Jerusalem; Dori Nissenbaum, Weizmann Institute of Science; Meira Barron, Hebrew University of Jerusalem; Yoav Levi, Israel Meteorological Service; Yinon Rudich, Weizmann Institute of Science
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
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
FR2.R15.4: Ship detection using ALOS PALSAR raw data without synthetic aperture
Syusuke Yasui, Fumitaka Ogushi, Yokoya Hiroshi, Kazushi Motomura, Naruo Kanemoto, Space Shift Inc.
FR2.R15.5: DYNAMIC TARGETING OF CONVECTIVE PRECIPITATION WITH REINFORCEMENT LEARNING
Suvan Kumar, University of Southern California; Josue Tapia, Paul Grogan, Arizona State University
Resources
No resources available.