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Mobile Device Visualization of Cloud Generated Terrain Viewsheds

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

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Mobile Device Visualization of Cloud Generated Terrain Viewsheds

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  1. 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

  2. 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.

  3. Augmented Reality (AR) in GIS Libertytown, MD (layar,2014) Yelp urban guide (Yelp,2014) Fai della Paganella Trento, Italy (Dalla Mura, 2012) • Location Intelligence (LI) Mobile Apps • Point vector based • AR frameworks • Next Generation • 3-D model rendering • Raster data based

  4. 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

  5. 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

  6. 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

  7. LOP Application UI(Map View – Device Horizontal Orientation) • Map View • OSMAndopen source framework • Slippy map user interface • Drop pin to identify observer locations • WGS84 Web Mercator MBTiled base map

  8. 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

  9. NED 1” NED 1/3” Lidar 10 M Aggregate Generalization Lidar 3M Aggregate Generalization Lidar – 1.0 Meter

  10. 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

  11. 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 • Calculate azimuth weighted value Visualization of calculated AOI curtain base.

  12. 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

  13. 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

  14. 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.

  15. LOP Application Evaluation LOP basemap with viewshed overlay. • Measure • Observer viewshed cloud request time • Time to render LOP augmented curtain • Detection of a LOP

  16. LOP Application Evaluation • NED1” and other bare earth returns • Performance response times < 0.5 seconds • No detected LOP

  17. LOP Application Evaluation • Lidar 10m • Performance response times < 0.5 seconds • Contiguous LOP path between 29.0o - 39.0o

  18. LOP Application Evaluation • Lidar 3 m • Performance response times < 0.5 seconds • Contiguous LOP path between 34.0o - 40.0o

  19. LOP Application Evaluation • Lidar 1 m • Performance response times < 0.5 seconds • Broad low LOP probability area (25.0o - 45.0o) • Distinct LOP sections between 26.0o- 37.0o

  20. Conclusions • LOP, demonstrates geo-visualization of Cloud generated viewsheds • Add outlier filtering algorithms for 1 m Lidar • Small LOP AOIs show no performance penalty

  21. 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

  22. 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

  23. Sources • Apache, 2013, ApacheTMHadoop: 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 • DallaMura, 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. • DallaMura, 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, JKalmanhttp://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, MBTilestileset 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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