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High-Resolution National Elevation Dataset: CyberGIS Challenges and Opportunities for Scalable Spatial Data Access and

High-Resolution National Elevation Dataset: CyberGIS Challenges and Opportunities for Scalable Spatial Data Access and Analytics. Yan Liu 1,3,5 , Babak Behzad 1,2 , Anand Padmanabhan 1,3,5 , Eric Shook 1,3 , Shaowen Wang 1,2,3,4,5 , and Yanli Zhao 1,3

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High-Resolution National Elevation Dataset: CyberGIS Challenges and Opportunities for Scalable Spatial Data Access and

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  1. High-Resolution National Elevation Dataset:CyberGIS Challenges and Opportunities for Scalable Spatial Data Access and Analytics Yan Liu1,3,5, Babak Behzad1,2, Anand Padmanabhan1,3,5, Eric Shook1,3, Shaowen Wang1,2,3,4,5, and Yanli Zhao1,3 1 CyberInfrastructure and Geospatial Information Laboratory (CIGI) 2 Department of Computer Science 3 Department of Geography and Geographic Information Science 4Department of Urban and Regional Planning 5National Center for Supercomputing Applications (NCSA) University of Illinois at Urbana-Champaign Michael P. Finn and E. Lynn Usery U.S. Geological Survey U.S. Department of the Interior

  2. Outline • Introduction • NED data access • Interfaces and performance issues • Computational challenges • Data-intensive spatial analysis • Experience and solutions • CyberGIS • Scalable spatial data access and analytics • Concluding discussions

  3. National Elevation Dataset (NED) • Digital elevation models (DEM) • Product of the USGS National Map • Resolutions: 3-meter, 10-meter, 30-meter • Formats: ArcGrid, GridFloat, IMG • Organized as 1 degree x 1 degree tiles • Sizes (U.S. continent) • 10-meter: 936 tiles; 440GB raw files; 1TB with pyramid tiles • http://nationalmap.gov/elevation.html

  4. NED Access Challenges • Data integration and processing • Data are stored on multiple file/database servers • Data processing is needed to extract subsets of data from the data collection • Downloading becomes complex, involving processing operations such as location, extraction, aggregation, archiving, and transfer among data servers • Computationally intensive • User interface • Usability is crucial to make big data usable • Programmable interface for automatic downloading

  5. CyberGIS Analytics Based on NED • CyberGIS: high-performanceand collaborative GIS based on cyberinfrastructure • http://cybergis.org • Viewshed analysis • http://sandbox.cigi.illinois.edu • Web Mapping Service for online visualization • NED WMS layer built using GeoServer • Pre-generated pyramid tiles for 20-level zooming CyberGIS Gateway

  6. The Great FloodProject • A 75-minute multimedia work of original music and film inspired by the 1927 Mississippi River floods • http://www.ncsa.illinois.edu/News/Stories/ELLNORAflood/ • Contributors include • Bill Frisell, Grammy Award-winning guitarist and composer • Bill Morrison, Obie-winning experimental filmmaker • Illinois Emerging Digital Research and Education in Arts Media Institute (eDream) • Advanced Visualization Laboratory (AVL) at the National Center for Supercomputing Applications (NCSA) • CyberInfrastructure and Geospatial Information Laboratory (CIGI), University of Illinois at Urbana-Champaign • Used NED • Approximately 70GB 10-meter NED tiles covering the Mississippi river valley were used for creating the 3D landscape animation

  7. Open YouTube URL http://www.youtube.com/watch?v=Lgy7mDJ_fVI Relevant parts: 0:00 – 0:24, historical maps; 0:25 – 1:16, 3D digital map animation based on 1/3 arc sec NED

  8. NED Data Access

  9. NED Download: User Interface • Download tool web interface • http://cumulus.cr.usgs.gov/webappcontent/neddownloadtool/NEDDownloadToolDMS.html • New interface • National Map Viewer: http://viewer.nationalmap.gov/viewer/

  10. NED Downloading Process 1. Queue a request 2. Launch data extractor Click each URL 3. Extract data 4. Archive data files File list 5. Notify data readiness 6. User download Please repeat 936 times to get all 1 degree x 1 degree tiles for U.S. continent!

  11. NED Downloading Web Service Interface Start download Check status Download Cleanup

  12. NED Downloader • Goal • Provide an easy-to-use NED downloading utility by supporting batch downloads and managing downloading status transition automatically • Software • Linux-based • Bash + PHP • Open source (MIT license) • Hosted on CyberGIS SVN • http://svn.cybergis.org/pub/ned-downloader/ • Status • Used by the National Science Foundation CyberGIS project team for NED data integration and the Great Flood project

  13. Computational Challenges in Related CyberGIS Analytics

  14. Why CyberGIS? • Most of commonly used GIS software is based on sequential computing • Not scalable for big data analytics • Many runtime Input/output (I/O) steps in an analysis workflow • Transfer of big data to / from cyberinfrastructure resources

  15. Viewshed Analysis • Input DEM • HTTP downloading • Data processing using GDAL commands • High-performance viewshed computation • Exploiting Graphic Processing Units (GPU) • Output transfer • GridFTP – a parallel file transfer protocol • Computational bottlenecks • The test viewshed analysis (see figure) handled 3.9GB raster data in total • 1.8GB input NED; 436MB output; 1.67GB runtime output • Execution time: 4 minutes 55 seconds • Input data transfer – 21 seconds; input data processing - 114 seconds; • Computing - 65 seconds; • output data processing - 88 seconds; output transfer – 7 seconds • Input/output data processing took 68.4% of analysis time

  16. Resolving Computational Bottlenecks • Reduce the number of runtime I/O steps • Employ high-performance I/O techniques Input Processing CPU Output Processing Analysis CPU … CPU CPU GPU CPU CPU … … GPU GPU Input Data Storage Output Data Storage Input Files Transfer Input Output Output Files Transfer Input Output Transfer Transfer Input Output Transfer Transfer

  17. Experience and Solutions

  18. CyberGIS Approach • Tightly couple geospatial data processing libraries to eliminate unnecessary I/O operations • Exploit parallel I/O for geospatial data processing • Integrate high-performance data transfer capability in CyberGIS analytics

  19. Integrated CyberGIS Architecture Applications Scalable Analytical Libraries Scalable Data Libraries Spatial Middleware Geospatial Parallel Computing GRASS NetCDF OpenMP CUDA GDAL HDF5 MPI CyberGIS computational resources Dependent Libraries CyberGIS Software Environment Parallel File Systems Processors Memory Network

  20. Highlights • Analytical libraries • pRasterBlaster (a high-performance map reprojection library under joint development by CEGIS and CIGI) • Data libraries • Parallel Geospatial I/O library (pGIO) with NetCDF/HDF5 support is to be released soon • GDAL+MPI IO for parallel I/O of GeoTIFF format is under development • Spatial middleware • GridFTP transfer between CyberGIS data source sites and XSEDE sites • CEGIS <-> supercomputer centers (NCSA, SDSC, TACC) • CyberGIS computational resources • CEGIS high-performance computers • CIGI cloud infrastructure • Key national cyberinfrastructure environments • NSF XSEDE (http://xsede.org) • Open Science Grid (http://opensciencegrid.org)

  21. Parallel I/O Strategies Row-wise I/O Column-wise I/O Block-wise I/O … P0 P1 P2 Pn P0 P1 P0 P1 P2 P2 … Pn Storage Device Storage Device Storage Device Pn

  22. High-Performance Data Transfer CEGIS Background image source: https://www.xsede.org/documents/10157/169907/xsedenet.pdf

  23. Data Transfer Service between USGS and XSEDE • Technology • GridFTP, a secure and high-performance data transfer protocol • Data transfer service setup • USGS GridFTP server: usgs-ybother.srv.mst.edu • Globus Toolkit 5 • Data transfer capability • Parallel data channels for large dataset transfer • Data transfer is initiated in the CyberGIS Gateway as a third- party transfer • Transfer rate: up to 100MB/second XSEDE

  24. Concluding Discussions • Usability of NED can be significantly improved if the data access interface can be made more friendly • Big data require cyberinfrastructure and significant computational power for scalable data access and analytics • CyberGIS has emerged as a new-generation GIS for resolving these challenges and represent significant opportunities for the National Map communities

  25. References • Canters, F. (2002). Small-Scale Map Projection Design. London: Taylor & Francis. • Finn, Michael P., and David M. Mattli (2012). User’s Guide for the mapIMG 3: Map Image Reprojection Software Package. U. S. Geological Survey Open-File Report 2011-1306, 12 p.. • Finn, Michael P., Daniel R. Steinwand, Jason R. Trent, Robert A. Buehler, David Mattli, and Kristina H. Yamamoto (2012). A Program for Handling Map Projections of Small Scale Geospatial Raster Data. Cartographic Perspectives, Number 71, pages 53 – 67. • Wang, S., Anselin, L., Bhaduri, B., Crosby, C., Goodchild, M. F., Liu, Y., and Nyerges, T. L (2013). CyberGIS Software: A Synthetic Review and Integration Roadmap. International Journal of Geographical Information Science, DOI:10.1080/13658816.2013.776049 • Wang, S., and Liu, Y. (2009) TeraGrid GIScience Gateway: Bridging Cyberinfrastructure and GIScience.International Journal of Geographical Information Science, 23 (5): 631–656. • Zhao, Y., Padmanabhan, A., and Wang, S. (2013) A Parallel Computing Approach to Viewshed Analysis of Large Terrain Data Using Graphics Processing Units. International Journal of Geographical Information Science, 27 (2): 363-384.

  26. DISCLAIMER & ACKNOWLEDGEMENT • DISCLAIMER: Any use of trade, product, or firm names in this paper is for descriptive purposes only and does not imply endorsement by the U.S. Government • ACKNOWLEDGEMENT: This work is supported in part by the National Science Foundation (NSF) under Grant Numbers: BCS-0846655 and OCI-1047916. Computational experiments used the NSF Extreme Science and Engineering Discovery Environment (XSEDE) (Award Number SES090019), which is supported by NSF under Grant Number OCI-1053575

  27. High-Resolution National Elevation Dataset:CyberGIS Challenges and Opportunities for Scalable Spatial Data Access and Analytics Comments / Questions? Contact: usery@usgs.gov or shaowen@illinois.edu University of Illinois at Urbana-Champaign CyberInfrastructure and Geospatial Information Laboratory Department of Computer Science Department of Geography and Geographic Information Science Department of Urban and Regional Planning National Center for Supercomputing Applications

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