IN006
Scalable Cloud Optimized Spatial Data Infrastructure (SDI) for Earth Science Big Data and Analytics
Session ID#: 80361
Session Description:
Scientists and engineers use geospatial data to manage organizational infrastructure, quantify the need and risk associated, and plan future development. The diversity and complexity of scientific data pose challenges in how it is stored, accessed, managed, shared, visualized, and fused for answering scientific questions. SDI for Earth and Space Sciences is built on frameworks that supports to enable Earth Observations such as satellite imagery, climate-weather observations, and forecasts that are service oriented and distributed via Geographic Information System (GIS). Such frameworks allow us to interoperate with systems of systems such as cloud computing, analysis ready data; analytics that can integrate with Artificial Intelligence to generate science products based on users need. This session explores critical components of cloud-optimized SDIs, workflows for spatio-temporal data ingest, archive, storage, visualization and distribution, and the use of GIS, spatial machine/deep learning, HPC as applied in big Earth data exploitation, 3D, 4D Earth Science Data.
Index Terms:
1908 Cyberinfrastructure [INFORMATICS]
1926 Geospatial [INFORMATICS]
1942 Machine learning [INFORMATICS]
1996 Web Services [INFORMATICS]
Primary Convener: Sudhir R Shrestha, ESRI, Redlands, CA, United States and daniel duffy
Conveners: Daniel Duffy, NASA Center for Climate Simulation, Greenbelt, MD, United States, Robert Edward Wolfe, NASA GSFC, Greenbelt, MD, United States, Manil Maskey, NASA Marshall Space Flight Center, Huntsville, AL, United States and daniel duffy
Primary Liaison: Dawn Wright, Environmental Systems Research Institute, Redlands, CA, United States and daniel duffy
Evaluation of Big Data Technology Frameworks Apache Spark and Pangeo as Scalable Processing Components of a SDI for Meteorological and Climate Applications (487914)
Julia Wagemann1,2, Stephan Siemen1, Bernhard Seeger3 and Joerg Bendix2, (1)ECMWF, Forecast, Reading, United Kingdom, (2)University of Marburg, Laboratory for Climatology and Remote Sensing, Marburg, Germany, (3)University of Marburg, Mathematics and Computer Science, Marburg, Germany
FOUR YEARS OF PROGRESS IN ANALYTIC CENTER FRAMEWORKS: LESSONS LEARNED (502271)
Michael M Little1, Daniel Duffy2, Ramakrishna R Nemani3, Svetla M Hristova-Veleva4 and Thomas Huang4, (1)NASA Headquarters, Earth Science Technology Office, Washington, DC, United States, (2)NASA Center for Climate Simulation, Greenbelt, MD, United States, (3)NASA Ames Research Center, Moffett Field, CA, United States, (4)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States
ICED (International Center for Earth Data) sustainable cloud platform for Near Real Time data (503831)
David W Gallaher1, Richard Arthur McAllister2, Christopher K Pankratz3, James Craft3, Glenn Grant4 and Kevin M Schaefer5, (1)National Snow and Ice Data Center, Boulder, CO, United States, (2)Montana State University, Computer Science, Bozeman, MT, United States, (3)University of Colorado, Laboratory for Atmospheric and Space Physics, Boulder, CO, United States, (4)CIRES, Port Townsend, WA, United States, (5)National Snow and Ice Data Center, Cooperative Institute for Research in the Environmental Sciences, University of Colorado at Boulder, Boulder, Colorado, U.S.A, Boulder, CO, United States
Distributed Inference and API Hosting for an Image Analysis Service: A Case Study on Land Cover Mapping (510067)
Dan Morris1, Patrick Flickinger1, Caleb Robinson1, Jennifer Marsman1, Nebojsa Jojic2 and Lucas N Joppa1, (1)Microsoft, AI for Earth, Redmond, WA, United States, (2)Microsoft, Microsoft Research, Redmond, WA, United States
Exploring GPU Acceleration for Rapid Visualization and Analysis of NASA Earth Science Data (519756)
Jacob Austin, Columbia University of New York, Computer Science, New York, NY, United States, Paxtan Huish Laker, University of California Berkeley, Electrical Engineering and Computer Science, Berkeley, CA, United States and Joe T Roberts, NASA Jet Propulsion Laboratory, Pasadena, CA, United States
STHBase:Spatio-temporal Organization framework Based on HBase for Geospatial Big Data (533952)
Quan Xiong1, Xiaochuang Yao1, Wei Liu1, Diyou LIU2, Sijing Ye3, Xiaodong Zhang1, Dehai Zhu1 and Zhe Liu1, (1)CAU China Agricultural University, College of Land Science and Technology, Beijing, China, (2)China Agricultural University(CAU), Department of Geographic Information Engineering, College of Land Science and Technology, Beijing, China, (3)BNU Beijing Normal University, Beijing, China
The Earth Data Analytic Services Framework (545584)
Thomas Patrick Maxwell1, Gerald L Potter1, Laura Carriere2 and Daniel Duffy3, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)NCCS, NASA Goddard, Greenbelt, MD, United States, (3)NASA Center for Climate Simulation, Greenbelt, MD, United States
NEXUS Hyper Grid: Analytics Engine Scalable for Climate Model Evaluation on Cloud and High-Performance Computing Platforms (567046)
Joseph Charles Jacob1, Brian D Wilson1 and Huikyo Lee2, (1)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States, (2)Jet Propulsion Laboratory, Caltech, Altadena, CA, United States
Monitoring the Pulse of our Planet: Towards the 100 Trillion Pixel Challenge (572732)
Budhendra L Bhaduri1, Dalton Lunga2 and Robert Stewart1, (1)Oak Ridge National Laboratory, Oak Ridge, TN, United States, (2)Oak Ridge National Laboratory, Geographic Data Science, Oak Ridge, TN, United States
Cloud Giovanni: Reining in Costs and Improving Performance with Analytical Data Stores using Scalable Serverless Architecture (596820)
Hailiang Zhang1, Mahabal Hegde2, Christine Smit3, Maksym Petrenko1 and Long Pham4, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)NASA GSFC GES DISC (ADNET Systems), Greenbelt, MD, United States, (3)Telophase Corp, Greenbelt, MD, United States, (4)NASA - GSFC, Greenbelt, MD, United States
Leveraging Cloud Computing Resources to Automate and Optimize Probabilistic Flood Analyses (603596)
Mohamed M Morsy1, Seth Lawler1, Siamak Esfandiary2, Bakinam Tarik Essawy1 and Mathew Mampara1, (1)Dewberry, Fairfax, VA, United States, (2)Federal Emergency Management Agency, Washington, DC, United States
Spatially and Temporally Consistent Smallsat-Derived Basemaps for Analytic Applications (607747)
Joe D Kington IV1, Kelsey A Jordahl1, Anudeep N Kanwar1, Amit Kapadia1, Maurice Schönert2 and Kevin Wurster1, (1)Planet Labs, San Francisco, CA, United States, (2)Planet Labs Germany GmbH, Berlin, Germany
Acquiring, processing, and analyzing open broadcast science data with extremely low latency at scale with AWS Groundstation and cloud processing. (615215)
Daniel Pilone1, Andrew Pawloski2 and Matthew Hanson2, (1)Element 84, Inc., Alexandria, VA, United States, (2)Element 84, Inc., Alexandria, United States
Developing a platform for multidimensional raster analytics (632883)
Hong Xu1, Kaixi Zhang2, Peng Gao2 and Raster Team, Development, Esri, (1)ESRI, Software Development, Redlands, CA, United States, (2)Esri, Redlands, CA, United States
STARE-based Interactive Analytics for Earth Science Big Data (634424)
Hongfeng Yu1, Kwo-Sen Kuo2,3, Michael Lee Rilee3,4, Yu Pan1 and Jin Wang1, (1)University of Nebraska Lincoln, Lincoln, NE, United States, (2)University of Maryland, College Park, MD, United States, (3)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (4)Rilee Systems Technologies, Derwood, MD, United States
