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 Science4 Department of Urban and Regional Planning

5 National Center for Supercomputing Applications (NCSA)University of Illinois at Urbana-Champaign

Michael P. Finn and E. Lynn Usery

U.S. Geological SurveyU.S. Department of the Interior

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

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

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

CyberGIS Analytics Based on NED• CyberGIS: high-performance and

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 zoomingCyberGIS Gateway

The Great Flood Project• 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

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

NED Data Access

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/

NED Downloading Process

File list

Click each URL

1. Queue a request 2. Launch data extractor

3. Extract data 4. Archive data files

5. Notify data readiness 6. User download

Please repeat 936 times to get all 1 degree x 1 degree tiles for U.S. continent!

NED Downloading Web Service Interface

Start download

Check status

Download

Cleanup

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

Computational Challenges in Related CyberGIS Analytics

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

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

Resolving Computational Bottlenecks

Input Data

Storage

TransferInput Files

Input Output Output Files

Output Data

StorageTransfer

Transfer Input Output

Input Output

CPUCPU

GPU

…

Input Processing

CPUCPU

GPU

…

CPUCPU

GPU

…

Analysis Output Processing

Transfer

Transfer

Transfer

• Reduce the number of runtime I/O steps• Employ high-performance I/O techniques

Experience and Solutions

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

Integrated CyberGIS Architecture

GDAL

OpenMPNetCDFGRASS

HDF5

Parallel File Systems Processors Network

MPI

CUDA

CyberGIS computational resources

Dependent Libraries

CyberGIS Software Environment

Applications Scalable Analytical Libraries

Scalable Data Libraries

Spatial Middleware

Geospatial Parallel Computing

Memory

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)

Parallel I/O Strategies

Storage Device

. . .

P0

P1

P2

Pn. . .

…

P0 P1 P2 … Pn

Storage Device

. . .

P0 P1

P2

PnStorage Device

Row-wise I/O Column-wise I/O Block-wise I/O

High-Performance Data Transfer

Background image source: https://www.xsede.org/documents/10157/169907/xsedenet.pdf

CEGIS

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

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

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.

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

U.S. Department of the InteriorU.S. Geological Survey

Comments / Questions?

Contact: usery@usgs.gov or shaowen@illinois.edu

University of Illinois at Urbana-ChampaignCyberInfrastructure and Geospatial Information LaboratoryDepartment of Computer ScienceDepartment of Geography and Geographic Information ScienceDepartment of Urban and Regional PlanningNational Center for Supercomputing Applications

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

Spatial Data Access and Analytics