Faculty Cyrus Shahabi (DB) Craig Knoblock (AI) Ulrich Neumann (CG) Ram Nevatia (CV)

National Science Foundation Engineering Research Center

FacultyCyrus Shahabi (DB) Craig Knoblock (AI)Ulrich Neumann (CG) Ram Nevatia (CV)

StaffFarnoush Banaei-KashaniLuciano Nocera

GeoDec: Enabling Geospatial Decision Making

University of Southern CaliforniaLos Angeles, CA 900890781

[email protected]://infolab.usc.edu

PhD StudentsUgur DemiryurekJeff KhoshgozaranSonghua Xing Undergrad StudentFernando Arreola

mailto:[email protected]

http://infolab.usc.edu/


GeoDec Mission!

Vision (What?): • Rapidly and accurately building an information-rich and realistic

geospatial 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 Data Sources


Outline

• Underlying Technologies– Building 3D Models– Static and Dynamic Textures– Geospatial Data Fusion– Effective Presentation and Querying

• Current Status and Future plan


Outline

• Underlying Technologies– Building 3D Models– Static and Dynamic Textures– Geospatial Data Fusion – Effective Presentation and Querying



3D Building Modeling Overview(Nevatia et. al)


Large Site Modeling Example (USC Campus)

• 256 building components modeled in about two hours

3D models with texture rendered in VRML

3D building models rendered in VRML


Example models


Outline




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:App1-The Grand Ave. Project:““Allow people to explore the physical location of the Grand Avenue park virtually.”Allow people to explore the physical location of the Grand Avenue park virtually.”See: See: http://www.learcenter.org/html/about/?&cm=grand/3d

http://www.learcenter.org/html/about/?&cm=grand/3d


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


Social Image Mapping (SIM)


Dynamic Textures from Video Streams

App2-The DC Project:App2-The DC Project:Five video stream fusion on 3D models in an area at Washington DC Five video stream fusion on 3D models in an area at Washington DC


Outline




TIGER/Line Vector Data

Satellite Imagery Tram Route/Stop Map

Automatic Vector to Imagery Conflation

Automatic Map to Imagery Conflation

Aligned Vector Data overlapped with the 3D model Aligned map overlapped with the 3D

Model

Goal: Vector, Map and Image Data Fusion(Knoblock & Shahabi)

App3-The Grand Ave. Project:App3-The Grand Ave. Project:““3-D model of the proposed park area that has been enhanced with maps, 3-D model of the proposed park area that has been enhanced with maps, road information, still photos, panoramic images and historical information about road information, still photos, panoramic images and historical information about the unique art and architecture in the area.”the unique art and architecture in the area.”See: See: http://www.learcenter.org/html/about/?&cm=grand/3d





Demo:Sponsors: • NSF

– IMSC– ITR– PECASE

• Google• Annenberg• Microsoft • Chevron

App4-The USC's App4-The USC's 125th Anniversary125th Anniversary::Adding moving objects and glove interfaceAdding moving objects and glove interface


Outline


• Current Status and future plan


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


Jooya

Google Earth NegaahMS VE

Darya Prometheus

Unified Query Results

GIS Datasets

Gazetteer Data

Vector/Raster Map

Web pages

Temporal Data

GUI interface

Unified Query Query Results

Satellite/Aerial Images

Video Streams

YimaVideo Streams

•Range Queries•Video•Vector Data•Buildings•Tram Location

•NN Queries•Shortest Path Queries

KML Files

ConflationStatic/Dynamic TexturesiMVS

Semi Automatic Processes


Analysis & Process atApplications

Arc GIS

Arc Explorer

Windows Live Local

Google Earth

GeoDec

Paradigm Shift

Analysis & Process atData Servers

Analysis & Process distributedbetween application & data Servers

Complex apps Scale-up


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!


Outline


• Current Status and future plan


In the News…

Lectures


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,

…


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


Thanks!

Faculty Cyrus Shahabi (DB) Craig Knoblock (AI) Ulrich Neumann (CG) Ram Nevatia (CV)

Documents

Transcript of Faculty Cyrus Shahabi (DB) Craig Knoblock (AI) Ulrich Neumann (CG) Ram Nevatia (CV)