TU4.R15.3
A Contrastive Self-Supervised Learning Model for Soil Moisture Retrieval from P-band PolSAR
Sam Khallaghi, Clark University, United States; Anke Fluhrer, Thomas Jagdhuber, German Aerospace Center, Germany; Hamed Alemohammad, Clark University, United States
Session:
TU4.R15: Foundation Models for Environmental Applications Oral
Track:
AI and Big Data
Location:
TBD
Presentation Time:
Tue, 11 Aug, 16:45 - 17:00
Session Co-Chairs:
Ronny Hänsch, DLR and Dalton Lunga, ORNL
Presentation
Discussion
Resources
No resources available.
Session TU4.R15
TU4.R15.1: CHALLENGES AND A STAGED DESIGN FOR APPLYING ALPHAEARTH FOUNDATIONS TO SEAGRASS COVERAGE ESTIMATION
Kota Sato, Takashi Kobayashi, Yuta Nakano, Kirara Kotani, Masaki Hisada, NTT, Inc., Japan
TU4.R15.2: SAM-Mosaic: Scaling SAM2 to Large Remote Sensing Imagery
Osmar Carvalho, Osmar Abílio de Carvalho Junior, Anesmar Albuquerque, Daniel Silva, University of Brasília, Brazil
TU4.R15.3: A Contrastive Self-Supervised Learning Model for Soil Moisture Retrieval from P-band PolSAR
Sam Khallaghi, Clark University, United States; Anke Fluhrer, Thomas Jagdhuber, German Aerospace Center, Germany; Hamed Alemohammad, Clark University, United States
TU4.R15.4: FOUNDATION AI MODELS FOR AEROSOL OPTICAL DEPTH ESTIMATION FROM PACE SATELLITE DATA
Zahid Hassan Tushar, Sanjay Purushotham, University of Maryland, Baltimore County, United States
TU4.R15.5: Crop Mapping for Indian Region with Geospatial Foundation Model
Kamal Das, Jagabondhu Hazra, Manikandan Padmanaban, Amith Singhee, Jaikrishnan Hari, Campbell Watson, Juan Bernabe Moreno, IBM Research, India; Karan Choudhary, Sunil Dubey, Preeti Tahlani, S Bandyopadhyay, Mahalanobis National Crop Forecast Centre, India
Contacts