Mobile Device Visualization of Cloud Generated Terrain Viewsheds

Chris MangoldCollege of Earth and Mineral Science

Penn State UniversityState 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

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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