IN41D:
Big Data in the Geosciences: New Approaches to Storage, Sharing, and Analysis II Posters


Submit an Abstract to this Session


Session ID#: 57829

Session Description:
Existing and future Earth system data are 1) increasingly large and complex – often representing high spatial/temporal/spectral resolution and dimensionality from remote sensing or model results — and are 2) growing at an unprecedented rate (e.g., NASA SWOT and NISAR missions), making such data difficult to store, download, analyze, visualize, interpret, and understand by traditional methods. Therefore, this session explores new approaches to processing, storage, exchange, and analysis of large geoscience data. Perspectives on cloud-based approaches (e.g., ready science data systems, cloud-optimized storage, cloud analytics engine, and use of cloud resources for scientific discovery), geoscientific data analytics approaches (statistical, data mining, assimilation, deep learning, etc.), parallel algorithms and software employing high performance computing resources for scalable analysis and novel applications of traditional methods on large geoscience data are strongly desired.
Primary Convener:  Cedric H David, Jet Propulsion Laboratory, Pasadena, CA, United States
Conveners:  Dr. Forrest M Hoffman, PhD, Oak Ridge National Laboratory, Computational Sciences & Engineering Division, Oak Ridge, United States and Hamed Alemohammad, Radiant Earth Foundation, Washington, United States
Primary Liaison:  Dr. Forrest M Hoffman, PhD, Oak Ridge National Laboratory, Computational Sciences & Engineering Division, Oak Ridge, United States
Chairs:  Dr. Forrest M Hoffman, PhD, Oak Ridge National Laboratory, Computational Sciences & Engineering Division, Oak Ridge, United States, Hamed Alemohammad, Massachusetts Institute of Technology, Civil and Environmental Engineering, Cambridge, MA, United States, Michelle M Gierach, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States and Jitendra Kumar, Oak Ridge National Laboratory, Environmental Sciences Division, Oak Ridge, United States
OSPA Liaison:  Cedric H David, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States

Cross-Listed:
  • A - Atmospheric Sciences
  • B - Biogeosciences
  • H - Hydrology
  • OS - Ocean Sciences
Index Terms:

1855 Remote sensing [HYDROLOGY]
1908 Cyberinfrastructure [INFORMATICS]
1914 Data mining [INFORMATICS]
1942 Machine learning [INFORMATICS]

Abstracts Submitted to this Session:

Capabilities and Limitations of Client-Side Raster Processing (356424)
Daniel J. Dufour, Organization Not Listed, Washington, DC, United States
Seamless Transition of Data Analyses and Analytics from a Local Workstation to Scalable, Massively Distributed Processing on the Cloud Using the Open Source PODPAC Library (391758)
Jerry Bieszczad1, Mattheus P. Ueckermann1, Dara Entekhabi2, David R. Callender3 and David W Sullivan3, (1)Creare LLC, Hanover, NH, United States, (2)Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Cambridge, MA, United States, (3)Creare LLC, Hanover, United States
Working with Planetary-Scale Data in the Cloud (364729)
Joe Flasher, Amazon Web Services, Open Geospatial Data Lead, Washington, DC, United States
The MARA System and Its Applications (439002)
Galina Wind, Science Systems and Applications, Inc., Lanham, MD, United States, Steven E Platnick, NASA Goddard Space Flight Center, Greenbelt, MD, United States and Kerry Meyer, NASA Goddard Space Flight Center, Greenbelt, United States
Extending Earth-orbiting Satellite Processing Capabilities to the Cloud and Tools Needed to Support This Effort (450623)
Steve Dutcher1, Liam Gumley2, Bruce Flynn1, Greg Quinn3 and Kevin Hrpceck4, (1)Space Science and Engineering Center, University of Wisconsin-Madison, Madison, United States, (2)Cooperative Institute for Meteorological Satellite Studies, Space Science and Engineering Center (SSEC), Madison, WI, United States, (3)NASA Atmosphere-SIPS, University of Wisconsin-Madison, Madison, United States, (4)Space Science and Engineering Center, University of Wisconsin-Madison, Madison, WI, United States
A cluster-based algorithm for mining marine abnormal variation objects with a time series of remote sensing products (399948)
Cunjin Xue, RADI Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Key Laboratory of Digital Earth Science, Chinese Academy of Sciences, Beijing, China and Jingyi Liu, nstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Key Laboratory of Digital Earth Science, Chinese Academy of Sciences, Beijing, China
Applying Recurrence Analysis to Illustrate the Co-existence of Chaotic and Non-Chaotic Solutions within a Generalized Lorenz Model (381659)
Tiffany Reyes, San Diego State University, San Diego, CA, United States and Bo-Wen Shen, San Diego State University, Mathematics and Statistics, San Diego, CA, United States
Building Web Applications to Support the Execution of NWM at Watershed Scales (443906)
Zhiyu Li, University of Illinois at Urbana-Champaign, CyberGIS Center for Advanced Digital and Spatial Studies, Urbana, United States, Daniel P Ames, Brigham Young University, Civil and Construction Engineering, Provo, United States, Anthony M Castronova, Consortium of Universities for the Advancement of Hydrological Science, Washington, DC, United States and Martin Seul, Consortium of Universities for the Advancement of Hydrologic Science, Inc., Washington, United States
High-performance Spatio-temporal Fusion Models for Remote Sensing Images with Graphics Processing Units (388281)
Qingfeng Guan and Xuantong Peng, China University of Geosciences Wuhan, Wuhan, China
A New Approach to Travel Time Estimation Using Low-Frequency Floating Car Data Based on Long short-term memory neural network (366567)
Sheng Hu and Liang Wu, China University of Geosciences Wuhan, Faculty of Information Engineering, Wuhan, China
Monitoring Field-Scale Crop Health and Production in the Cloud (466633)
Damien J Sulla-Menashe, Mark A Friedl, Ophir Horn, Bobby H Braswell Jr and David Potere, TellusLabs, Somerville, MA, United States
Multidimensional Spatio-temporal Data Clustering Based on Improved AP Algorithm (346686)
Haifu Cui1,2 and Liang Wu2, (1)China University of Geosciences Wuhan, Wuhan, China, (2)China University of Geosciences Wuhan, Faculty of Information Engineering, Wuhan, China
Novel Random Projection-based Dimensionality Reduction Methods as a Foundation for Scalable and Efficient Big Data Analytics in Geoscience Applications (356679)
Vineetha Menon, University of Alabama in Huntsville, Computer Science, Huntsville, AL, United States and Sundar A Christopher, University of Alabama in Huntsville, Atmospheric and Earth Science, Huntsville, AL, United States
Parallel K-means Clustering of Geospatiotemporal Data Sets Using Manycore CPU Architectures (444858)
Richard T Mills, Argonne National Laboratory, Mathematics and Computer Science Division, Argonne, IL, United States, Vamsi Sripathi, Intel Corporation, Hillsboro, OR, United States, Sarat Sreepathi, Oak Ridge National Laboratory, Oak Ridge, United States, Jitendra Kumar, Oak Ridge National Laboratory, Climate Change Science Institute and Environmental Sciences Division, Oak Ridge, TN, United States, Dr. Forrest M Hoffman, PhD, Computational Earth Sciences Group and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, United States and William Walter Hargrove, USDA Forest Service Southern Research Station, Eastern Forest Environmental Threat Assessment Center, Hot Springs National, AR, United States
Standardization Efforts to Enable Large Scale In-Cloud Processing of Earth Observation Data (418808)
Bart De Lathouwer, Open Geospatial Consortium Europe, Brussels, Belgium, Ingo Simonis, Open Geospatial Consortium, Frankfurt, Germany and Scott Serich, Open Geospatial Consortium, Leesburg, VA, United States
The Ocean Carbon States Database: multivariate application of cluster analysis on the ocean carbon cycle (466466)
Rebecca Latto and Anastasia Romanou, NASA Goddard Institute for Space Studies, New York, NY, United States

See more of: Earth and Space Science Informatics