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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,

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Presentation on theme: "Mobile Device Visualization of Cloud Generated Terrain Viewsheds Chris Mangold College of Earth and Mineral Science Penn State University State College,"— Presentation transcript:

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 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) Libertytown, MD (layar,2014) Yelp urban guide (Yelp,2014)

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 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 hosted GIS

6 Cloud GIS 2 KM Radius RF Propagation IFSAR 5 M 1.7 KM Observer Viewshed IFSAR 5 M2.5 KM Slope Position Classification IFSAR 5 M Computing Efficiencies Apache Hadoop MapReduce framework Virtualized commodity and clustered resources (GPUs) Terrain spatial analysis web services REST APIs (MrGeo, DigitalGlobe 2014)

7 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

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 1/3NED 1 Lidar – 1.0 MeterLidar 10 M Aggregate GeneralizationLidar 3M Aggregate Generalization Data sourceElevation model ASTER GDEM 1(~30 meter resolution)DSM Lidar 1 meterDSM NED 1 (~30 meter resolution)DTM NED 1/3 (~10 meter resolution)DTM SRTM 3 (~90 meter resolution)DSM

10 LOP Augmented Curtain Generation AOI curtain base evaluation image Scale: 1 Pixel = 1 Meter Scale received viewshed PNG imagesGeo-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 Environment Suburban office park setting Droid Incredible Target observation height 2 meters LOP AOI 200 m diameter LOP evaluation site. LOP site looking north through alley. Viewshed origin point looking west.

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

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 29.0 o - 39.0 o

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

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

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 1.Apache, 2013, Apache TM Hadoop: http://hadoop.apache.org/, last accessed 19 Feb 2014.http://hadoop.apache.org/ 2.Amazon, 2014, Amazon web services – Amazon EC2: http://aws.amazon.com/ec2/, last accessed 19 Feb 2014.http://aws.amazon.com/ec2/ 3.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 4.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. 5.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 6.DigitalGlobe, 2014, DigitalGlobe – Defense & Intelligence http://www.digitalglobe.com/Industries/defense%26intelligence#overview, last accessed 17 Feb 2014.http://www.digitalglobe.com/Industries/defense%26intelligence#overview 7.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. http://www.ics.uci.edu/~fielding/pubs/dissertation/top.htm 8.GeoTools, 2014, GeoTools - The Open Source Java GIS Toolkit: http://www.geotools.org/, last accessed 17 Feb 2014.http://www.geotools.org/ 9.Google, 2014, GLASS: http://www.google.com/glass/start/, last accessed 17 Feb 2014.http://www.google.com/glass/start/ 10.JKalman, 2013, JKalman http://sourceforge.net/projects/jkalman/, last accessed 17 Feb 2014.http://sourceforge.net/projects/jkalman/ 11.layar, LAYAR SDK: https://www.layar.com/developers/, last accessed 17 Feb 2014.https://www.layar.com/developers/ 12.Mapbox, 2013a, TileMill: https://www.mapbox.com/tilemill/, last accessed 17 Feb 2014. 13.Mapbox, 2013, MBTiles tileset format: https://github.com/mapbox/mbtiles-spec, last accessed 19 Feb 2014.https://github.com/mapbox/mbtiles-spec 14.NASA, 2014a, ASTER: http://asterweb.jpl.nasa.gov/, last accessed 19 Feb 2014. 15.NASA, 2014b, SRTM: http://www2.jpl.nasa.gov/srtm, last accessed 19 Feb 2014.http://www2.jpl.nasa.gov/srtm 16.OsmAnd, 2014, OsmAnd – Map & Navigation: http://osmand.net/, last accessed 17 Feb 2014.http://osmand.net/ 17.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. 18.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. 19.Rosenberg, J., and A. Mateos, 2011. The Cloud at Your Service, Manning, Greenwich, pp 146-146. 20.Yelp, 2014, Yelp http://www.yelp.com, last accessed 17, Feb 2014.http://www.yelp.com 21.USGS, 2013, The USGS Store – Map Locator & Downloader: https://store.usgs.gov/b2c_usgs/usgs/maplocator, last accessed 20, Dec 2013.https://store.usgs.gov/b2c_usgs/usgs/maplocator 22.USGS, 2014, National Elevation Dataset: http://ned.usgs.gov/, Last accessed 17, Feb 2014. 23.Wikitude, Wikitude SDK: http://www.wikitude.com/products/wikitude-sdk/, last accessed 17 Feb 2014.http://www.wikitude.com/products/wikitude-sdk/ 24.William and Mary, 2014, Virginia lidar: http://www.wm.edu/as/cga/VALIDAR/, last accessed 08 Feb 2014.http://www.wm.edu/as/cga/VALIDAR/


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