Mangold.ppt - Penn State University
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Transcript Mangold.ppt - Penn State University
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
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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