E MERGING TRENDS IN T ERRAIN V ISUALIZATION - Suchitra Manepalli

O UTLINE GIS Evolution VRML Terrain Modeling Data and Compression Algorithms Implications Conclusion 5/7/ Visualization II

T ERMS 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 5/7/ Visualization II

E XAMPLE 5/7/ Visualization II

GIS H ISTORY 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 5/7/ Visualization II

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 5/7/ Visualization II

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 5/7/ Visualization II

GIS B ASICS 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 5/7/ Visualization II

R ASTER 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 5/7/ Visualization II

V ECTOR 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 5/7/ Visualization II

W HICH TYPE OF DATA ? 5/7/2008 Visualization II 11 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 RasterVector

T ERRAIN M ODELING H ISTORY 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 5/7/ Visualization II

T ERRAIN M ODELING B ASICS 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 5/7/ Visualization II

T ERRAIN M ODELING T ECHNOLOGIES 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 5/7/ Visualization II

T ERRAIN M ODELING T RENDS 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 5/7/ Visualization II

D IGITAL E LEVATION M ODEL 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 5/7/ Visualization II

D ATA C OMPRESSION TECHNIQUES Gigabytes of storage Transmission of data DEM Data Wavelet transformation Lossy compression of elevation data using SPIHT DEM Compression Linear prediction Statistical encoding 5/7/ Visualization II

JPEG I MAGE C OMPRESSION 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 5/7/ Visualization II

JPEG R EVIEW 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 5/7/ Visualization II

F UTURE G ROWTH 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 5/7/ Visualization II

I MPLICATIONS Google sued for invasion of privacy – street view Google talking to Canada How much detail is too much detail? 5/7/ Visualization II

C ONCLUSION 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! 5/7/ Visualization II

Q UESTIONS 5/7/ Visualization II