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									      Uncertainty Quantification and Visualization:
   Geo-Spatially Registered Terrains and Mobile Targets

                         Suresh Lodha
      Computer 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
      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)
     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)
      Accomplishments - III
• Integration of data and uncertainty within a global
  geospatial system (collaboration with Georgia
• 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
        GIS Infrastructure

• Aerial Imagery (DOQQs)
• Elevation Data
  – Digital Elevation Models (DEMs)
  – LIDAR Data
• Architectural Drawings
• Street Maps
• Schematic Diagrams
        GIS Images:
Aerial Imagery and LIDAR
  GIS Images:
DEM and AutoCAD
          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
GPS Receiver Equipment
    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
Common Consistent Representation: Multiple Views of Terrains

    Aerial Imagery      AutoCAD Drawing       LiDAR Data

• Common Coordinate System
• Geo-Spatial Registration
• Accuracy
Common Consistent Representation: Multiple Views of Terrains
   Modeling and Visualizing
• Probability-based uncertain particle
• GPS-based spatio-temporal uncertainty in
  particle movement
• Low uncertainty compression algorithms
  preserving line features within terrains
   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
Computation of Probabilistic
  Locational Uncertainty
Uncertain Probabilistic Shapes
 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
• Mode
  – Standalone / Differential
• Environment
  – Urban / Foliage
• Movement
  – Stationary
  – Moving (Constant Velocity, Random)
     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
Satellite Availability
       Dilution of Precision
• Satellite Geometry and Orientation

  Good satellite geometry   Poor satellite geometry
SNR Modeling
Observations and Analysis:
 Constant Velocity Data
       Terrain Uncertainty
• Point feature preserving compression
  algorithms (last year MURI)
• Line feature preserving compression
  – EMD (earth movers distance) concept extended
    to line features
  – More efficient local algorithm
  – Line preservation (coastlines etc.)
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
Line EMD Error
Line Preserving Compression

 Unconstrained      Coastline
Line Preserving Compression

Original   Unconstrained   Coastline
Line Simplification
   Integration of Data and
   Uncertainty within VGIS
• Hierarchical zooming from the globe into
  the UCSC Campus (1/2 foot resolution
• Real-time visualization of GPS-tracked
  objects and associated uncertainty within
Hierarchical Zooming into
     UCSC Campus
Real-Time Mobile Uncertainty
 Visualization within VGIS
    Uncertainty Quantification, Visualization and
        Communication: Continuing Work

•    Heterogeneous Geo-Spatial Uncertainty
•    Mobile Temporal Uncertainty
•    Multi-Sensor Data Fusion (Images, LIDAR)
•    Multi-modal Interaction (speech)
•    Database Querying
•    Wireless Networks
         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
        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
    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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