E MERGING TRENDS IN T ERRAIN V ISUALIZATION - Suchitra Manepalli.
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Transcript of E MERGING TRENDS IN T ERRAIN V ISUALIZATION - Suchitra Manepalli.
EMERGING TRENDS IN TERRAIN VISUALIZATION
- Suchitra Manepalli
OUTLINE
GIS Evolution VRML Terrain Modeling Data and Compression Algorithms Implications Conclusion
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TERMS
Georeference: existence in a physical space Polygons, lines, points
Map projection: mathematical means of transferring information from a model of earth (3D) to a flat surface (2D)
Pixel: smallest individual unit of an image Resolution: accuracy of depicting the location
and shape of map features Geodatabase: database with extension for
storing, creating, editing spatial and geographic information
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EXAMPLE
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GIS HISTORY
Geographical referenced information analyze spatial information
Cartography, Resource Management, Environmental Impact Assessment
GIS started by Ontario, Canada Federal department of Forestry and Rural
Development Canada Land Inventory
Tomlinson: Father of GIS
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VRML Virtual Reality Modeling Language, an ISO
Standard and web standard High level object oriented language VRML world can be accessed using standard
browsers Text file format where vertices and edges are
specified with following properties Surface color Mapped textures Shininess Transparency
Widely used as a file format for interchange of 3D models
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VRML
LOD level for deep level hierarchies Low resolution can be traded for higher
resolution VRML with XML demonstrated GeoVRML UTM coordinate system can add scientific
content 3D terrain on the web
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GIS BASICS
Data creation – digitized from a hard copy of a map or a survey
Different types of information and sources in many different forms
Represents real world objects with digital data
Two types of data: Raster: any type of digital image, rows and
columns of cells, a color value Vector: geographical features as vectors using
geometrical shapes
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RASTER
Matrix of cells in a continuous space Cell size direct correlation with map Impact the ordering of spatial data Three types of raster data:
thematic data – digital elevation model spectral data – aerial or satellite imagery
vegetation geologic information pictures – normal day to day
Vectorization: raster to vector, GIS
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VECTOR
Positional data in form of coordinates
Vector data basic units of spatial information
Best used to store discrete and well defined data
Vector operations are allowed Rotation, movement, mirroring, sketching, affine,
z-order Shading algorithms: Phong, Gouraud Rasterization: Conversion of vector data to
raster data
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WHICH TYPE OF DATA?
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Pixel size defines resolution
Efficient for dense data – elevation
Large amounts of storage space
Easy creation from image data
Precision of coordinates defines resolution
Efficient for sparse data – hospitals or highways
Processing requires lots of computer time
High resolution
Raster Vector
TERRAIN MODELING HISTORY
First models for military Topographic maps were rare, problem to
create the third dimensional model to a two dimensional map
World War II – allied produced terrain models using different techniques
Several techniques on cardboard existed Plaster models, pantograph, wenschow,
vaccumforming, terrain emboss, computer based techiques
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TERRAIN MODELING BASICS
3D GIS models use terrain modeling 3D terrain model consists of two main things:
Digital Elevation Model: topography of the terrain Geographical information: satellite images, aerial
photographs etc Connecting the points – a polygon based 3D
representation of terrain Texture mapping a model Interpolation technique such as Gourarud
Shading
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TERRAIN MODELING TECHNOLOGIES Google Earth and Microsoft Virtual Earth
Organizational framework Digital terrain modeling: 3D surface computed
from a set of points Representations: Regular Grid and Triangular Irregular
Network 3D Terrain Visualization: Earth observation image
data on digital elevation models RealityMap: Visualize unlimited spatial extents
incorporating gigabytes of imagery, modeling and elevation
TerraVista: Standalone, modeler independent, database for 3D creation
Flooding, earthquake disasters are modeled
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TERRAIN MODELING TRENDS
Improvements in scientific and commercial purposes
Hydrology – water flows accuracy, flood damage and flood extent
Marine observations – coastal change and storm impacts
Geological land observations – landslides, avalanches
Accurate elevation data – automated driver assistance by responding to their surroundings
Terrain model in war and space
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DIGITAL ELEVATION MODEL
Developed and maintained by NIMA File content – uniform 2D array of values Raster type – cell represents 30m pixel size
with the elevation value assigned to the cell 5 different levels: level increases resolution
increases Extract slope and aspect DEM – topographic elevations sampled on a
uniform grid spacing DEM will produce a better model than
digitized contours
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DATA COMPRESSION TECHNIQUES
Gigabytes of storage Transmission of data DEM Data
Wavelet transformation Lossy compression of elevation data using SPIHT
DEM Compression Linear prediction Statistical encoding
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JPEG2000 - IMAGE COMPRESSION
JPEG2000 – new standard for compression of data
Supports 38 bits per band Suitable for DTED
Significant changes in intensity and/or color Kakadu
DTED Compression ROI capability within JPEG2000 High level compression Perfect reproduction of DTED file along an
arbitrary path
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JPEG2000 - REVIEW
Resending unnecessary information eliminated
No numerical loss along the defined region Allows XML data to be included in the file
header File header in form SVG – read and overlaid
on image during decoding Reduced storage and dissemination resource
requirements
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FUTURE GROWTH
Keyhole, TerraServer, TerraFly – visualize whole earth with specific regions with different resolutions Keyhole – 3D visualization
Extends to Mars Space Spin – New digital terrain model for
Mars
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IMPLICATIONS
Google sued for invasion of privacy – street view
Google talking to Canada How much detail is too much detail?
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CONCLUSION
GIS evolution has improved over the years Terrain modeling started off as a military
project Many compression techniques for data and
image Visual analysis is always the better way!
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QUESTIONS
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