Mangold.ppt - Penn State University

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Mobile Device Visualization of Cloud
Generated Terrain Viewsheds
Chris Mangold
College of Earth and Mineral Science
Penn State University
State College, PA
csm202@psu.edu
Advisor: Dr. Peter Guth
Motivations
 Mobile visualization of GIS data
 Products of Terrain DTM/DSM spatial analysis
 Cloud GIS
 Mobile
 Augmented Reality (AR)
Rothera Point, Adelaide Island, Antarctica. Aster (v2) Global DEM overlay.
Augmented Reality (AR) in GIS
Libertytown, MD (layar,2014)
Yelp urban guide (Yelp,2014)
 Location Intelligence (LI) Mobile Apps
 Point vector based
 AR frameworks
 Next Generation
 3-D model rendering
 Raster data based
Fai della Paganella Trento, Italy
(Dalla Mura, 2012)
Least Observed Path (LOP) Application Concept
LI Mobile Application
 Provides a navigation path to avoid detection
 Renders AR geo-layer
 Consumes Cloud generated observer viewsheds
Cloud hosted GIS
LOP System Diagram - Work Flow
 Define LOP environment
 Request and consum observer viewshed results
 Geo-register result using devices sensors
 Generate and render AR geo-layer
Cloud GIS
2 KM Radius RF Propagation IFSAR 5 M
2.5 KM Slope Position Classification
IFSAR 5 M
1.7 KM Observer Viewshed IFSAR 5 M
(MrGeo, DigitalGlobe 2014)
 Computing Efficiencies
 Apache Hadoop MapReduce framework
 Virtualized commodity and clustered resources (GPUs)
 Terrain spatial analysis web services
 REST APIs
LOP Application UI
(Map View – Device Horizontal Orientation)
Map View
 OSMAnd open source framework
 Slippy map user interface
 Drop pin to identify observer locations
 WGS84 Web Mercator MBTiled base map
LOP Application UI
(Augmented Curtain View – Device Vertical Orientation)
Augmented Curtain View
 Renders AR curtain layer
 Recalculated as device location updates
 POSE derived from orientation sensors
 Visibility probability color ramp indicator
NED 1”
NED 1/3”
Lidar 10 M Aggregate Generalization
Lidar 3M Aggregate Generalization
Data source
Elevation model
ASTER GDEM 1”(~30 meter resolution)
DSM
Lidar 1 meter
DSM
NED 1” (~30 meter resolution)
DTM
NED 1/3” (~10 meter resolution)
DTM
SRTM 3” (~90 meter resolution)
DSM
Lidar – 1.0 Meter
LOP Augmented Curtain Generation
AOI curtain base evaluation image
Scale: 1 Pixel = 1 Meter
Scale received viewshed PNG images
Geo-register and merge images
Create evaluation bitmap
 Size bitmap to LOP evaluation AOI
 Normalize and scale viewshed images
 Geo-register images
 Merge and clip images to AOI
LOP Augmented Curtain Generation
Create AR curtain base
 Array of 360 RGB values
 Evaluate pixels within AOI
 RGB values to determine
visibility
 Calculate azimuth to location
 Track total and visible pixel
Visualization of calculated AOI curtain base.
 Calculate azimuth weighted value
LOP Augmented Curtain Generation
Render LOP geo-layer
 Overlay on Android surface view
 Determine screen orientation and size
 Apply weighted visibility for each azimuth
 Draw compass components
Augmented Curtain POSE
POSE
 AR: integrating virtual data with real world
 Enhance geo-register LOP curtain layer
 Manage device inertia sensors
 Magnetic
 Gravity
 Kalman filter
 Smoother rendering
LOP Application Evaluation
LOP evaluation site.
LOP site looking north through alley.
Environment
 Suburban office park setting
 Droid Incredible
 Target observation height 2 meters
 LOP AOI 200 m diameter
Viewshed origin point looking west.
LOP Application Evaluation
LOP basemap with viewshed overlay.
Measure
 Observer viewshed cloud request time
 Time to render LOP augmented curtain
 Detection of a LOP
LOP Application Evaluation
NED1” and other bare earth returns
 Performance response times < 0.5 seconds
 No detected LOP
LOP Application Evaluation
Lidar 10m
 Performance response times < 0.5 seconds
 Contiguous LOP path between 29.0o - 39.0o
LOP Application Evaluation
Lidar 3 m
 Performance response times < 0.5 seconds
 Contiguous LOP path between 34.0o - 40.0o
LOP Application Evaluation
Lidar 1 m
 Performance response times < 0.5 seconds
o
o
 Broad low LOP probability area (25.0 - 45.0 )
 Distinct LOP sections between 26.0o - 37.0o
Conclusions
 LOP, demonstrates geo-visualization of Cloud
generated viewsheds
 Add outlier filtering algorithms for 1 m Lidar
 Small LOP AOIs show no performance penalty
Future directions
 Evaluate LOP with larger spatial extents
 Optimize rendering algorithms
 Add depth projection to LOP curtain
 Investigate edge detection
 Evaluate porting application to Google Glass
Questions
 LOP, demonstrates geo-visualization of terrain based
raster data
 Add outlier filtering algorithms for 1 m Lidar
 Small LOP AOIs show no performance penalty
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Apache, 2013, ApacheTM Hadoop: http://hadoop.apache.org/, last accessed 19 Feb 2014.
Amazon, 2014, Amazon web services – Amazon EC2: http://aws.amazon.com/ec2/, last accessed 19 Feb 2014.
Baboud, Lionel. M. Cadik, E. Eisenmann and H.P. Seidel, 2011. Automatic photo-to-terrain alignment for the annotation of mountain pictures, IEEE
Conference on Computer Vision and Pattern Recognition, pp. 41-48
Dalla Mura, M., M. Zanin, C.Andreatta and P.Chippendale, 2012a. Augmented reality: Fusing the real and synthetic worlds, IEEE International Geosciences
and Remote Sensing Symposium, (8):170-173.
Dalla Mura, M., and P.Chippendale, 2012b. Real-World DEM Harmonization through Photo Re-Projection, IEEE International Symposium on Geoscience and
Remote Sensing, 2012. (7): pp 428-430
DigitalGlobe, 2014, DigitalGlobe – Defense & Intelligence http://www.digitalglobe.com/Industries/defense%26intelligence#overview, last accessed 17 Feb
2014.
Fielding R.T., 2000. Architectural Styles and the Design of Network-based Software Architectures, University of California, Irvine, 2000
http://www.ics.uci.edu/~fielding/pubs/dissertation/top.htm , last accessed 17 Feb 2014.
GeoTools, 2014, GeoTools - The Open Source Java GIS Toolkit: http://www.geotools.org/, last accessed 17 Feb 2014.
Google, 2014, GLASS: http://www.google.com/glass/start/, last accessed 17 Feb 2014.
JKalman, 2013, JKalman http://sourceforge.net/projects/jkalman/, last accessed 17 Feb 2014.
layar, LAYAR SDK: https://www.layar.com/developers/, last accessed 17 Feb 2014.
Mapbox, 2013a, TileMill: https://www.mapbox.com/tilemill/, last accessed 17 Feb 2014.
Mapbox, 2013, MBTiles tileset format: https://github.com/mapbox/mbtiles-spec, last accessed 19 Feb 2014.
NASA, 2014a, ASTER: http://asterweb.jpl.nasa.gov/, last accessed 19 Feb 2014.
NASA, 2014b, SRTM: http://www2.jpl.nasa.gov/srtm, last accessed 19 Feb 2014.
OsmAnd, 2014, OsmAnd – Map & Navigation: http://osmand.net/ , last accessed 17 Feb 2014.
Noguera, J.M., C. Ogayar and R. Joan-Arinyo, 2013. A scalable architecture for 3D map navigation on mobile devices, Personal and Ubiquitous Computing,
17(7): pp. 1487-1502.
Porzi, L., E. Ricci, TA. Ciarfuglia, and M. Zanin, 2012. Visual-inertial tracking on Android for augmented reality applications, IEEE Workshop on Environmental
Energy and Structural Monitoring Systems,(8):35-41.
Rosenberg, J., and A. Mateos, 2011. The Cloud at Your Service, Manning, Greenwich, pp 146-146.
Yelp, 2014, Yelp http://www.yelp.com, last accessed 17, Feb 2014.
USGS, 2013, The USGS Store – Map Locator & Downloader: https://store.usgs.gov/b2c_usgs/usgs/maplocator, last accessed 20, Dec 2013.
USGS, 2014, National Elevation Dataset: http://ned.usgs.gov/, Last accessed 17, Feb 2014.
Wikitude, Wikitude SDK: http://www.wikitude.com/products/wikitude-sdk/, last accessed 17 Feb 2014.
William and Mary, 2014, Virginia lidar: http://www.wm.edu/as/cga/VALIDAR/ , last accessed 08 Feb 2014.
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