MO3.R13.2
Hydrology Copilot: A Cloud-Native AI System for Hydrological Data Analysis
Mahya G.Z Hashemi, Sujay V. Kumar, Nasa Goddard Space Flight Center, United States; Jocelynn Hartwig, Juan Carlos Lopez, Microsoft, United States; Christopher R. Hain, NASA Marshall Space Flight Center, United States; John D. Bolten, Nasa Goddard Space Flight Center, United States
Session:
MO3.R13: GRSS ESI TC / HDCRS WG - Parallel and Innovative Computing Technologies for Earth Observation Oral
Track:
Community Contributed Themes
Location:
TBD
Presentation Time:
Mon, 10 Aug, 14:00 - 14:15
Session Co-Chairs:
Rocco Sedona, Forschungszentrum Jülich and Alberto S. Garea, University of Santiago de Compostela
Presentation
Discussion
Resources
No resources available.
Session MO3.R13
MO3.R13.1: ANALYZING THE COMPUTATIONAL COST OF EFFBAGAN IN DATA-SCARCE REMOTE SENSING SCENARIOS
Nicolás Vilela-Pérez, Dora B. Heras, Francisco Argüello, Universidade de Santiago de Compostela, Spain
MO3.R13.2: Hydrology Copilot: A Cloud-Native AI System for Hydrological Data Analysis
Mahya G.Z Hashemi, Sujay V. Kumar, Nasa Goddard Space Flight Center, United States; Jocelynn Hartwig, Juan Carlos Lopez, Microsoft, United States; Christopher R. Hain, NASA Marshall Space Flight Center, United States; John D. Bolten, Nasa Goddard Space Flight Center, United States
MO3.R13.3: EXPLORING THE FEASIBILITY OF HAILO 8L FOR EMBEDDED MULTISPECTRAL CLASSIFICATION
Alberto S. Garea, Álvaro Ordóñez, Javier López-Fandiño, Pablo Quesada-Barriuso, Francisco Argüello, Dora B. Heras, University of Santiago de Compostela, Spain
MO3.R13.4: Courier: A Distributed, Plugin-Based Infrastructure for Scalable Earth Observation Data Processing
Gwyn Uttmark, Evan Rose, James Fluke, Jeremy Solbrig, John Haynes, Yoo-Jeong Noh, Steven Miller, Colorado State University, United States; Melinda Surratt, Christopher Camacho, Christopher Selman, Andrew Thorpe, Lance Wilson, U.S. Naval Research Laboratory, United States
MO3.R13.5: REPRODUCIBLE END-TO-END INSAR PROCESSING ON HPC: CPU AND GPU BENCHMARKING WITH AN AI SURROGATE WORKLOAD
Mehdi Joud, Aalborg University in Copenhagen,, Denmark
Contacts