MO3.M2.5
Physics-Informed Temporal Transformer for Landslide Kinematics Prediction
Mohammad Amin Khalili, Sadegh Madadi, University of Naples, Italy; Infante Donato, Ciro Sepe, SINTEMA Engineering srl, Italy; Massimo Ramondini, Diego Di Martire, University of Naples, Italy
Session:
MO3.M2: Advancements in Deep Learning for SAR Remote Sensing Applications I Oral
Track:
Community-Contributed Sessions
Location:
Mezzanine: Room M2
Presentation Time:
Monday, 4 August, 14:00 - 14:15
Session Co-Chairs:
Shubham Awasthi, Virginia Polytechnic Institute and State University and Gunjan Joshi, Helmholtz-Zentrum Dresden-Rossendorf
Session MO3.M2
MO3.M2.1: FIELD-SCALE SOIL MOISTURE ESTIMATED FROM SENTINEL-1 SAR DATA USING A KNOWLEDGE-GUIDED DEEP LEARNING APPROACH
Yi Yu, Patrick Filippi, Thomas Bishop, The University of Sydney, Australia
MO3.M2.2: PROTECTING SAR IMAGERY FROM ADVERSARIAL ATTACK USING A DIFFUSION MODEL
Kendall Jenner, Luke Rosenberg, Leon Clark, Lockheed Martin Australia, Australia
MO3.M2.3: TESA-Net: A Novel Triplet Attention-Enhanced Skip Atrous Network for Urban Land Use and Land Cover Segmentation using SAR
Himanshi Srivastava, Uttam Kumar, International Institute of Information Technology Bangalore, India
MO3.M2.4: ISLAND WAKE SEMGENETATION FROM SYNTHETIC APERTURE RADAR USING A CHEBYSHEV U-NET
Madeleine Dawson, Parniyan Farvardin, Hans Graber, David Chapman, University of Miami, United States
MO3.M2.5: Physics-Informed Temporal Transformer for Landslide Kinematics Prediction
Mohammad Amin Khalili, Sadegh Madadi, University of Naples, Italy; Infante Donato, Ciro Sepe, SINTEMA Engineering srl, Italy; Massimo Ramondini, Diego Di Martire, University of Naples, Italy
Resources