1. Automation and Visualization in Geographic Immersive Virtual Environments Thomas J. Pingel, Northern Illinois University Keith C. Clarke, University of California Santa Barbara AutoCarto 2012 International Research Symposium September 16-20, 2012 Columbus, Ohio

2. 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?

3. Outline Project overview Code base Terrain generation from LiDAR Acquisition for of audio and video for model overlay

4. 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.

5. 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

6. 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

7. 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

8. X3D Development Avalon & 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

9. 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

10. Point cloud of building and surrounding area

11. Terrain Extraction is Important Davidson Library sits approximately 6 meters above the ground due to a terrain layer error.

12. 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

15. 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

18. SMRF + Bonemaps at El Pilar, Guatemala Digital Surface Model

19. SMRF + Bonemaps at El Pilar, Guatemala SMRF-derived terrain layer

20. Video Overlay Aerostat-based video capture Smartphone capture and relay Native video texturing in X3D

21. 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.