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

Suresh 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

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

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 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)

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 Uncertainty

• Probability-based uncertain particle movement

• 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

Parameters

• 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

Visualization

Visualization

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.)

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 preserving

Line Preserving Compression

Original Unconstrained Coastline preserving

Hierarchical Line Simplification

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

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.