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GeoDec: Enabling Geospatial Decision Making

GeoDec is a platform developed by the University of Southern California that rapidly and accurately builds information-rich and realistic geospatial spaces with a temporal dimension, supporting visualization, querying, and data analysis capabilities. It addresses challenges such as realistic rendering, information fusion, interactive query and access, scalable infrastructure, and efficiency in time-to-build. GeoDec has a wide range of applications in city planning, emergency response, military intelligence, simulation and training, computer games, real estate, news broadcasting, and more.

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GeoDec: Enabling Geospatial Decision Making

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  1. GeoDec: Enabling Geospatial Decision Making University of Southern California Los Angeles, CA 900890781 shahabi@usc.edu http://infolab.usc.edu PhD StudentsUgur Demiryurek Jeff KhoshgozaranSonghua Xing Undergrad Student Fernando Arreola FacultyCyrus Shahabi (DB)Craig Knoblock (AI) Ulrich Neumann (CG) Ram Nevatia (CV) StaffFarnoush Banaei-Kashani Luciano Nocera

  2. Vision (What?): Rapidly and accurately building an information-rich and realisticgeospatial space (e.g., a city) with temporal dimension, supporting visualization, querying and data analysis capabilities Challenges (How?): Realistic rendering Accurate information fusion Interactive query and access Scalable infrastructure Efficient in time-to-build Applications (Why?): City planners Emergency response and first responders Military intelligence Simulation & training Computer games Real-estate News broadcast GeoDec Mission!

  3. GeoDec Data Sources

  4. Outline • Underlying Technologies • Building 3D Models • Static and Dynamic Textures • Geospatial Data Fusion • Effective Presentation and Querying • Current Status and Future plan

  5. Outline • Underlying Technologies • Building 3D Models • Static and Dynamic Textures • Geospatial Data Fusion • Effective Presentation and Querying • Current Status and Future plan

  6. 3D Building Modeling Overview(Nevatia et. al)

  7. Large Site Modeling Example (USC Campus) 3D models with texture rendered in VRML 3D building models rendered in VRML • 256 building components modeled in about two hours

  8. Example models

  9. Outline • Underlying Technologies • Building 3D Models • Static and Dynamic Textures • Geospatial Data Fusion • Effective Presentation and Querying • Current Status and Future plan

  10. Textures for building facades(Neumann et. al) • Aerial views provide streets, roofs, and open spaces (parks, plazas, etc…) • Ground views capture building facades • Occlusions from landscaping, poles, architectural protrusions (e.g., entry canopies) need to be removed • Requires fusion of images from multiple perspectives to see behind occlusions – methods to reduce parallax artifacts • Alternatively, portions of the textures need to be synthesized based on building-specific texture patterns • App1-The Grand Ave. Project: • “Allow people to explore the physical location of the Grand Avenue park virtually.” • See: http://www.learcenter.org/html/about/?&cm=grand/3d

  11. Social Image Mapping (SIM) • Improving time-to-build • Collaboration with NSF’s STC at UCLA: CENS: Center for Embedded Networked Sensing

  12. Social Image Mapping (SIM)

  13. Social Image Mapping (SIM)

  14. Dynamic Textures from Video Streams • App2-The DC Project: • Five video stream fusion on 3D models in an area at Washington DC

  15. Outline • Underlying Technologies • Building 3D Models • Static and Dynamic Textures • Geospatial Data Fusion • Effective Presentation and Querying • Current Status and Future plan

  16. Goal: Vector, Map and Image Data Fusion(Knoblock & Shahabi) TIGER/Line Vector Data Tram Route/Stop Map Satellite Imagery Automatic Map to Imagery Conflation Automatic Vector to Imagery Conflation Aligned Vector Data overlapped with the 3D model Aligned map overlapped with the 3D Model • App3-The Grand Ave. Project: • “3-D model of the proposed park area that has been enhanced with maps, • road information, still photos, panoramic images and historical information about • the unique art and architecture in the area.” • See: http://www.learcenter.org/html/about/?&cm=grand/3d

  17. Demo: Sponsors: • NSF • IMSC • ITR • PECASE • Google • Annenberg • Microsoft • Chevron • App4-The USC's 125th Anniversary: • Adding moving objects and glove interface

  18. Outline • Underlying Technologies • Building 3D Models • Static and Dynamic Textures • Geospatial Data Fusion • Effective Presentation and Querying • Current Status and future plan

  19. Query Formation/Visualization – Negaah(Shahabi et. al) Negaah – New User Interface • Supports formation of queries with user-defined spatio-temporal parameters • Query types: • Range • Spatial • Temporal • Spatiotemporal • shortest path • nearest neighbor • trajectories • events • objects (3D models, parcels, …) • Line of Sight

  20. MS VE Google Earth Negaah Unified Query Results GUI interface KML Files • Range Queries • Video • Vector Data • Buildings • Tram Location • NN Queries • Shortest Path Queries Jooya Query Results Unified Query Darya Yima Prometheus Video Streams Web pages GIS Datasets Video Streams Semi Automatic Processes Gazetteer Data Satellite/Aerial Images iMVS Static/Dynamic Textures Conflation Vector/Raster Map Temporal Data

  21. Complexapps Scale-up Paradigm Shift Arc GIS Windows Live Local GeoDec Arc Explorer Google Earth Analysis & Process at Applications Analysis & Process distributed between application & data Servers Analysis & Process at Data Servers

  22. So What’s unique about GeoDec? • Not a visualization GUI with data access features added in an ad-hoc manner as an after thought! • Three tier architecture with distribution of code where it belongs • All objects are tagged and indexed spatially and temporally • Access at different tiers: GUI, Web-services, database • Time dimension • Extended querying capabilities • We have the source code! • Go wild! Your ideas will not be hindered by the restricted API!

  23. Outline • Underlying Technologies • Building 3D Models • Static and Dynamic Textures • Geospatial Data Fusion • Effective Presentation and Querying • Current Status and future plan

  24. In the News… Lectures

  25. Where we’re going? • Up to now, the focus was on: • Modeling and visualization • Acquisition, storage, integration of real datasets • Implementing the software architecture • Developing generic query types • Some “toy” applications: DC, USC campus, downtown LA • What’s next: • GeoDec+: Challenges of • Real applications, multidisciplinary collaboration (e.g., NSF CDI pre-proposal: pollution in Megacities w/ Keck School of Medicine, Env. & Civil Eng, Earth Science; School of Architecture) • Dynamic datasets: climate data, pollutant data, transportation data, …

  26. GeoDec+: Vision • Marrying • Information richness of GIS systems • Interaction flexibility of computer games & simulations • Challenge: Query and access of real-world large spatio-temporal data as if they are synthetically generated data • Index 3d data (e.g., surface data) for querying and not just rendering

  27. Thanks!

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