IN31B:
Exploiting Big Earth Data: GIS and Beyond I Posters

Submit an Abstract to this Session



Session ID#: 27110

Session Description:
Developments in Geographic Information Services (GIS), Cloud Computing, Analytics, Machine Learning, and High Performance Computing (HPC) enable research and development communities to accelerate modeling, visualization, and analysis. GIS are a critical bridge that bring together large and diverse data sets to broad communities from science to decision makers. Powerful capabilities now use such technologies as NoSQL, MapReduce, Spark, Deep Learning, and other technologies to accelerate large scale data analytics. Combined with new emerging information technology capabilities, scientists are being presented with significant enhancements in capabilities to address problems of unmatched size and complexity.

This session explores emerging examples (software and hardware) of advances in the use of GIS, cloud, machine/deep learning, and HPC as applied in big Earth data exploitation. Emphasis will be placed on examples of the application of technologies in research and development environments that demonstrate the value of these tools to Earth science and related application users.

Primary Convener:  Daniel Duffy, NASA Goddard Space Flight Center, Greenbelt, MD, United States
Convener:  Michael M Little, NASA Headquarters, Washington, DC, United States
Chairs:  Daniel Duffy, NASA Center for Climate Simulation, Greenbelt, United States and Michael M Little, NASA Headquarters, Washington, DC, United States
OSPA Liaison:  Michael M Little, NASA Headquarters, Washington, DC, United States
Index Terms:

1916 Data and information discovery [INFORMATICS]
1928 GIS science [INFORMATICS]
1932 High-performance computing [INFORMATICS]
1942 Machine learning [INFORMATICS]

Abstracts Submitted to this Session:

The Benefits and Complexities of Operating Geographic Information Systems (GIS) in a High Performance Computing (HPC) Environment (299858)
James Shute1, Laura Carriere2, Daniel Duffy3, Elizabeth Embury Hoy4, Julien Peters5, Yingshuo Shen2 and Dalia Kirschbaum6, (1)CSRA/NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)Climate Model Data Service, NASA Goddard Space Flight Center, Greenbelt, MD, United States, (3)NASA Center for Climate Simulation, Greenbelt, United States, (4)Global Science and Technology Inc Greenbelt, Greenbelt, MD, United States, (5)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (6)CNR-IRPI, Perugia, Italy
NASA's EOSDIS has a large amount of Earth science data in its archives, distributed through discipline-specific DAACs. We will demonstrate the use of NASA Earth science data in a GIS to include a diverse amount of formats, missions, instruments, and applications that has become more and more user-friendly and interoperable in the last several years. (284983)
Ross Bagwell, NASA Goddard Space Flight Center, Greenbelt, MD, United States, Byron Peters, NASA, Greenbelt, MD, United States and Stephen W Berrick, NASA Goddard Space Flight Center, ESDIS Project Office, Greenbelt, MD, United States
Aurora Research: Earth/Space Data Fusion Powered by GIS and Python (261507)
Virginia L Kalb, NASA Goddard Space Flight Center, Greenbelt, United States, Yaireska M Collado-Vega, NASA GSFC, Greenbelt, United States, Elizabeth MacDonald, Los Alamos National Laboratory, Los Alamos, NM, United States and Burcu Kosar, NASA Goddard Space Flight Center, Greenbelt, MD, United States
Predicting Seagrass Occurrence in a Changing Climate Using Random Forests (257043)
Orhun Aydin, ESRI, Redlands, CA, United States and Kevin A Butler, Environmental Systems Research Institute, Redlands, CA, United States
Framework for Detection and Localization of Extreme Climate Event with Pixel Recursive Super Resolution (236824)
Jiwoo Lee, Chengzhu Zhang, Soo Kyung Kim, Sasha Ames and Dean Norman Williams, Lawrence Livermore National Laboratory, Livermore, CA, United States
Modelling population distribution using remote sensing imagery and location-based data (235881)
Jinchao Song, University of Copenhagen, København K, Denmark and Alexander V. Prishchepov, University of Copenhagen, Copenhagen, Denmark

See more of: Earth and Space Science Informatics