Automation and Visualization in Geographic Immersive Virtual Environments

Automation and Visualization in Geographic Immersive Virtual Environments

Thomas J. Pingel, Northern Illinois UniversityKeith C. Clarke, University of California Santa Barbara

AutoCarto 2012 International Research SymposiumSeptember 16-20, 2012

Columbus, Ohio

Central Research Question:

How can we, in an automatable way, produce an immersive geographic virtual environment that

will assist in the interpretation, analysis, and understanding of specific, local events?

Outline

• Project overview• Code base• Terrain generation from LiDAR• Acquisition for of audio and video for model

overlay

Immersive Geographic Virtual Environments

• Immersive: “any virtual reality representation in which the user views her or her environment from a perspective view, and can freely move around in that environment”

• Multiple Psychologies of Space (Montello, 1993)– Figural , Vista, Environmental, Geographical

• Representing Environmental (or Geographical) spaces as Figural (or Vista) Objects while retaining some of the cognitive elements of each.

• Emphasis on representing places in a model that can both be manipulated as an object or experienced as a place.

Related Work• Google’s Earth and

Street View– Microsoft & Apple– No ability to alter the

terrain– Universality

• Virtual Tübingen– Designed for spatial

cognition testing– 200 structures, .5 x .15

km– Our study area

• 3.25 x 1.6 km• ~2000 structures

Image from Virtual Tübingen

Video Game Community

• Immense budgets and revenues– $65 billion annually

• Many perspectives– First Person Shooters– World of Warcraft – But few environment &

object perspectives• Highly structured

environments

Code Base – X3D• XML successor to VRML (and

GeoVRML)• Native Geo support• Native video texturing and

spatialized audio• Royalty free• Browsers can typically read other

3D formats (e.g., COLLADA)• Good input device integration

– Space M ouse– Microsoft Kinect– Wiimotes

X3D DevelopmentAvalon & X3DOM

• Integration of next-gen specs in Avalon– Instantreality.org

• Integration with HTML5 with X3DOM– X3dom.org

• Full rendering within browser– No-add ins required

Terrain generation

• LiDAR– Cheap– Highly accurate– Portable– But needs processing

• Assumption of little available geodata– Ground cues can be

very valuable in street network ID

Point cloud of building and surrounding area

Terrain Extraction is Important

Davidson Library sits approximately 6 meters above the ground due to a terrain layer error.

Terrain Extraction: The Simple Morphological Filter (SMRF)

• Emphasizes reducing Earth-as-Object error

• Still very good at reducing Object-as-Earth error

• Lowest total error rate of any published algorithm tested against ISPRS dataset

• tpingel.org/code

LiDAR Visualization (Bonemaps)• Image-like visualization of

Digital Surface Model• No registration errors• Slope-based intensity

mapping, w/ compensation for “cognitive slope”

• Higher contrast than hillshade

• Appropriate for mixed environments

SMRF + Bonemaps at El Pilar, Guatemala

Digital Surface Model

SMRF + Bonemaps at El Pilar, Guatemala

SMRF-derived terrain layer

Video Overlay

• Aerostat-based video capture

• Smartphone capture and relay

• Native video texturing in X3D

Acknowledgements

• IC Postdoc for funding the project.• Alan Glennon and Kitty Courier for kite

photography expertise.• William McBride for SRMF algorithm

development and aerostat design.