Uncertainty Quantification and Visualization: Geo-Spatially Registered Terrains and Mobile Targets Suresh Lodha Computer Science, University of California, Santa Cruz

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Uncertainty Quantification and Visualization
Geo-Spatially Registered Terrains and Mobile
TargetsSuresh LodhaComputer Science,
University of California, Santa Cruz

• Common consistent representation of multiple
  views of geo-spatially registered terrains
• Low uncertainty compression algorithms preserving
  line features within terrains
• Visualization of uncertainty of GPS-tracked
  mobile targets
• Integration of mobile targets and terrains with
  geographic databases for decision-making

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Accomplishments - I

• Development of GIS infrastructure for
  context-aware situational visualization
• Development of GPS infrastructure for mobile
  visualization
• Work on consistency and uncertainty issues in
  mobile situational (GIS-GPS) visualization

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Accomplishments - II

• Modeling and quantifying uncertainty
• Probability-based uncertainty (collaboration with
  Pramod Varshney, Syracuse University)
• Spatio-temporal GPS uncertainty
• Low uncertainty line preserving compression
  algorithms for terrains (extension from point
  preserving algorithms from previous year)

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Accomplishments - III

• Integration of data and uncertainty within a
  global geospatial system (collaboration with
  Georgia Tech)
• Application to
• Geospatial visualization
• General Aviation
• Continuing work on
• Multimodal interaction (speech)
• Database querying
• Wireless networks
• for communicating and visualizing data and
  information with associated uncertainty
• Probability-based uncertainty (collaboration with
  Pramod Varshney, Syracuse University)
• Spatio-temporal GPS uncertainty
• Low uncertainty line preserving compression
  algorithms for terrains (extension from point
  preserving algorithms from previous year)

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GIS Infrastructure

• Aerial Imagery (DOQQs)
• Elevation Data
• Digital Elevation Models (DEMs)
• LIDAR Data
• Architectural Drawings
• Street Maps
• Schematic Diagrams

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GIS Images Aerial Imagery and LIDAR
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GIS Images DEM and AutoCAD
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GPS Infrastructure

• Ashtech Z-12/G-12 Sensors
• Standalone (1 meter) / Differential (1 cm)
• Velocity (.1 knots)
• L1/L2 frequency (ionospheric delay correction)
• RTK/RTCM messages
• 10 Hz update rate

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GPS Receiver Equipment
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Consistency and Uncertainty in Mobile Situational
Visualization

• Disparate data sources
• Different data formats
• Different coordinate systems
• Different resolutions/ sampling/ sizes
• Different accuracy
• Different time stamps
• Communication time lags

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Common Consistent Representation Multiple Views
of Terrains
Aerial Imagery
AutoCAD Drawing
LiDAR Data

• Common Coordinate System
• Geo-Spatial Registration
• Accuracy

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Common Consistent Representation Multiple Views
of Terrains


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Modeling and Visualizing Uncertainty

• Probability-based uncertain particle movement
• GPS-based spatio-temporal uncertainty in particle
  movement
• Low uncertainty compression algorithms preserving
  line features within terrains

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Algorithmic Computation

• Compute the probability of target at a point x
  after time t
• Probability at an initial location (p)
• Probability of movement along a direction (d)
• Probability of speed (s)
• Final probability p d s

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Computation of Probabilistic Locational
Uncertainty
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Uncertain Probabilistic Shapes
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GPS Sources of Uncertainty

• Measurement Errors
• Satellite clock drift, receiver clock drift,
  satellite location error, atmospheric effects,
  multipath effect, selective availability
• GPS Availability Issues
• GPS Integrity Anomalies and Vulnerability

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Parameters

• Mode
• Standalone / Differential
• Environment
• Urban / Foliage
• Movement
• Stationary
• Moving (Constant Velocity, Random)

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Modeling Static Data

• Number of accessible/used satellites
• Urban higher than foliage
• Standalone same as differential
• SNR (Signal to Noise Ratio) values
• Urban higher than foliage
• Standalone same as differential
• DOP (Dilution of Precision) values
• Urban smaller than foliage
• Standalone smaller than differential

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Satellite Availability
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Dilution of Precision

• Satellite Geometry and Orientation

Good satellite geometry
Poor satellite geometry
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SNR Modeling
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Observations and Analysis Constant Velocity Data
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Visualization
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Visualization
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Visualization
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Terrain Uncertainty

• Point feature preserving compression algorithms
  (last year MURI)
• Line feature preserving compression algorithms
• EMD (earth movers distance) concept extended to
  line features
• More efficient local algorithm
• Line preservation (coastlines etc.)

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Topology Degradation Metric

• EMD (Rubner et al. 98, Batra et al. 98, 99,
  Lodha et al. 2000)
• amount of work required to move one set of lines
  to another (similarity)
• Variables
• features
• Location of features
• Feature Attributes
• Length, Orientation

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Line EMD Error
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Line Preserving Compression
Unconstrained
Coastline preserving
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Line Preserving Compression
Coastline preserving
Original
Unconstrained
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Street movie
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Integration of Data and Uncertainty within VGIS

• Hierarchical zooming from the globe into the UCSC
  Campus (1/2 foot resolution imagery)
• Real-time visualization of GPS-tracked objects
  and associated uncertainty within VGIS

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Hierarchical Zooming into UCSC Campus
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Real-Time Mobile Uncertainty Visualization within
VGIS
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Uncertainty Quantification, Visualization and
Communication Continuing Work

• Heterogeneous Situational data
• Mobile Temporal data
• Data Fusion (Images, LIDAR)
• Multi-modal Interaction (speech)
• Database Querying
• Wireless Networks

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Collaborations - I

• Worked with Pramod Varshney on probabilistic
  uncertain particle movement (1 joint paper and 1
  jointly supervised student) continuing to
  collaborate on uncertainty with mobility
  constraints
• Worked with Bill Ribarsky on integration of
  uncertainty within VGIS (1 joint paper and 1
  jointly supervised student) continuing to
  collaborate on uncertainty in mobile situational
  visualization

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Collaborations - II

• Worked with Ulrich Neumann on development of GPS
  infrastructure
• Worked with Avideh Zakhor on acquistion of LIDAR
  data
• continuing to collaborate on uncertainty in GPS,
  LIDAR and image data

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Uncertainty in 3D GeoSpatial Registration from
Multiple Images
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Major Accomplishments

• Computation and visualization of uncertainty for
  terrains while preserving point and line features
  of terrains,
• Computation and visualization of uncertain mobile
  GPS-tracked targets embedded within a GIS
  Environment, and
• Embedding and visualization of uncertainty within
  the VGIS.

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Publications

• Suresh Lodha, A. P. Charaniya, Nikolai M.
  Faaland, and Srikumar Ramalingam,"Visualization
  of Spatio-Temporal GPS Uncertainty within a GIS
  Environment" to appear in the Proceedings of SPIE
  Conference on Aerospace/Defense Sensing,
  Simulation, and Controls, April 2002.
• Suresh K. Lodha, Nikolai M. Faaland, Amin P.
  Charaniya, Pramod Varshney, Kishan Mehrotra, and
  Chilukuri Mohan, "Uncertainty Visualization of
  Probabilistic Particle Movement",To appear in the
  Proceedings of The IASTED Conference on Computer
  Graphics and Imaging", August 2002.
• Suresh K. Lodha, Amin P. Charaniya, and Nikolai
  M.Faaland, "Visualization of GPS Uncertainty in
  a GIS Environment", Technical Report
  UCSC-CRP-02-22,University of California, Santa
  Cruz, April 2002, pages 1-100.
• Suresh K. Lodha, Nikolai M. Faaland, Grant Wong,
  Amin Charaniya,Srikumar Ramalingam, and Arthur
  Keller, "Consistent Visualization and Querying of
  Geospatial Databases by a Location-Aware Mobile
  Agent,submitted to ACM GIS Conference, November
  2002.
• Suresh K. Lodha, Nikolai M. Faaland, and Jose
  Renteria,Hierarchical Line Preserving
  Simplification of Terrain Data, In Preparation.

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Publications - contd

• Suresh K. Lodha, Nikolai M. Faaland, and Jose
  Renteria,Hierarchical Topology Preserving
  Simplification of Vector Fields using Bintrees
  and Triangular Quadtrees'', Submitted for
  publication to IEEE Transactions on Visualization
  and Computer Graphics.
• Lilly Spirkovska and Suresh K. Lodha,AWE
  Aviation Weather Data Visualization
  Environment'', \em Computers and Graphics,
  Volume 26, No.1, February 2002, pp.169--191.
• Suresh K. Lodha, Krishna M. Roskin, and Jose
  Renteria,Hierarchical Topology Preserving
  Compression of 2D Terrains'',Submitted for
  publication to Computer Graphics Forum.
• Suresh K. Lodha and Arvind Verma,
  Spatio-Temporal Visualization of Urban Crimes
  on a GIS Grid'',Proceedings of the ACM GIS
  Conference, November 2000, ACM Press, pages
  174--179.
• Amin P. Charaniya, Srikumar Ramalingam, Suresh
  Lodha, Zach Wartell, Tony Wasilewski, Nick Faust,
  Bill Ribarsky, Real-Time Mobile Uncertainty
  Visualization in VGIS, to be Submitted to IEEE
  Visualization, 2002