mobile device visualization of cloud generated terrain viewsheds
DESCRIPTION
Mobile Device Visualization of Cloud Generated Terrain Viewsheds. Chris Mangold College of Earth and Mineral Science Penn State University State College, PA [email protected] Advisor: Dr. Peter Guth. Motivations . Mobile visualization of GIS data - PowerPoint PPT PresentationTRANSCRIPT
Mobile Device Visualization of Cloud Generated Terrain Viewsheds
Chris MangoldCollege of Earth and Mineral Science
Penn State UniversityState College, PA
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
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)
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
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
Cloud GIS
2 KM Radius RF Propagation IFSAR 5 M 1.7 KM Observer Viewshed IFSAR 5 M2.5 KM Slope Position ClassificationIFSAR 5 M
Computing Efficiencies Apache Hadoop MapReduce framework Virtualized commodity and clustered resources (GPUs)
Terrain spatial analysis web services REST APIs
(MrGeo, DigitalGlobe 2014)
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 1/3”NED 1”
Lidar – 1.0 MeterLidar 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
LOP Augmented Curtain Generation
AOI curtain base evaluation imageScale: 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
Calculate azimuth weighted valueVisualization of calculated AOI curtain base.
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
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.
LOP Application Evaluation
Measure
Observer viewshed cloud request time
Time to render LOP augmented curtain
Detection of a LOP
LOP basemap with viewshed overlay.
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 Broad low LOP probability area (25.0o - 45.0o) 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
Sources
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