G11A:
Big Data and Advanced Geocomputations

Submit an Abstract to this Session

Session ID#: 6212

Session Description:
Computational science and engineering have greatly advanced in recent years with the rapidly changing High-Performance Computing and Communication (HPC) technologies. With scientific data sizes generally increasing faster than computational power, new and sophisticated computational strategies are warranted.  As terabyte (TB) and larger data files are common in several areas of geoscience, advanced powerful computers and computer clusters are only part of the solution and more intelligent approaches are needed.

 This session is intended to bring together all those interested in big geospatial data challenges including diversity and complexity, distributed data storage and parallel processing, multi-resolution analysis and synthesis, semantic and syntactic data fusion, and advanced visualization. New ideas and suggestions in advanced geocomputations, grid and cloud computing, spatial data analytics, uncertainty quantification and web services are of particular importance. Contributions to any of these and related topics are invited for oral and/or poster presentation.

OSPA Liaison:  Kristy French Tiampo, University of Colorado at Boulder, CIRES, Department of Geological Sciences, Boulder, United States
Chairs:  J A Rod Blais, University of Calgary, Calgary, AB, Canada and Hassan A Karimi, University of Pittsburgh, Geoinformatics Laboratory, School of Information Sciences, Pittsburgh, PA, United States
Primary Convener:  J A Rod Blais, University of Calgary, Calgary, AB, Canada
Convener:  Hassan A Karimi, University of Pittsburgh, Geoinformatics Laboratory, School of Information Sciences, Pittsburgh, PA, United States
Co-Sponsor(s):
  • ES - Earth Surface Processes
  • GR - Geomatics and Regional Geology
  • H - Hydrology
  • SE - Solid Earth

Abstracts Submitted to this Session:

Least Squares Wavelet Analysis (34052)
Ebrahim Ghaderpour, York University, Toronto, ON, Canada and Spiros D Pagiatakis, York University, Department of Earth and Space Science, Toronto, ON, Canada
Stochastic Surfaces in the Least Squares Wavelet Analysis (34058)
Ebrahim Ghaderpour, York University, Toronto, ON, Canada and Spiros D Pagiatakis, York University, Department of Earth and Space Science, Toronto, ON, Canada
Principal component analysis of InSAR data (34985)
Kristy French Tiampo, University of Colorado at Boulder, CIRES, Department of Geological Sciences, Boulder, United States, Pablo J González, University of Leeds, COMET, School of Earth and Environment, Leeds, United Kingdom, Sergey V Samsonov, Natural Resources Canada, Ottawa, ON, Canada and Jose Fernandez, Institute of Geosciences (CSIC-UCM), Calle del Doctor Severo Ochoa, 7. Facultad de Medicina (Edificio Entrepabellones 7 y 8, 4ª planta) Ciudad Universitaria., Madrid, Spain
An integral image approach to performing multi-scale topographic position analysis (34589)
John Barrie Lindsay, The University of Guelph, Geography, Environment and Geomatics, Guelph, ON, Canada, John C Gallant, CSIRO Land and Water, Canberra, Australia, Jaclyn Cockburn, University of Guelph, Department of Geography, Guelph, ON, Canada and Hazen Russell, Natural Resources Canada, Geological Survey of Canada, Ottawa, ON, Canada
Ice Mass Loss Monitoring in the Canadian Arctic: A Study on the Filtering Methods with Release-05 GRACE Data (35918)
Iliana Tsalis, Dimitrios Piretzidis, Elena Veselinova Rangelova and Michael G Sideris, University of Calgary, Calgary, AB, Canada
Wavelet spectral techniques for error mitigation of superconductive angular accelerometer output (36508)
Elaheh Mokhtari, Mohamed Mamdouh Elhabiby and Michael G Sideris, University of Calgary, Calgary, AB, Canada
Multilinear Filtering of Nonstationary Array Data and Inversion (35001)
J A Rod Blais, University of Calgary, Calgary, AB, Canada
MATLAB Tools for Earth’s Surface Deformation Studies (33493)
Mohammad Ali Goudarzi, Marc Cocard and Rock Santerre, Laval University, Department of Geomatics Sciences, Quebec City, QC, Canada
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