digital terrain representations(last)
DESCRIPTION
Introduction to basic digital terrain representationsTRANSCRIPT
GROUP MEMBERS
Agasha Ochneva, Biniyam Tilahun
Gülendam Baysal, Roya Olyazadeh
Muhammad Maimaiti, Shiuli Pervin
David González Sánchez
Roberto Mediero Martí
DIGITAL TERRAIN REPRESENTATIONS
GENEREAL EXPLANATION OF DIGITAL TERRAIN REPRESENTATIONS
A digital terrain model is a topographic model of the bare earth –terrain relief - that can be manipulated by computer programs. The data files contain the spatial elevation
data of the terrain in a digital format
DATA SOURCE:
Ground survey, Digitizing contours,Digital Photogrammetry,Direct image grid DEM,LiDAR, RADAR, SONAR;
The pattern of DTM data could be:
regular or irregular
Regular: square or rectangular grid Irregular: may be based on triangular
network of irregular size, shape and orientation
These DIM data could be structured in different ways such as grid/raster, quadtree, and triangular irregular data structure.
irregular
regular
CLASSIFICATION
TIN
DEM
QUADRATREE
MULTIRESOLUTION
MOST IMPORTANT REPRESENTATIONS
Grid DEMMain description
DEM: A digital representation of a topographic surface
They are based on the values of the elevation at the sampling points- one height per pixel (grid cell)
The grid representation is the consequence of sampling elevation values in regular intervals of latitude and longitude.
Grid DEMMain description
The result is a matrix whose indices are the coordinates and values are the elevation value at each point (raster representation)
From this sample representation it is possible to get a representation of the relief
Grid DEMMain description
The steps to build a grid DEM are:
Obtaining the data: Sampling elevation values in a regular grid pattern; process and filtered of the acquired data
Model building: Data structures building and storage
Optimization and visualization of the model
SAMPLES
SAMPLES
ADVANTAGES of GRID
Regular sample pattern --> Simple data storage structures and algorithm
Multisource possibility --> Compatible with many sources, even satellite, and easy to combine with imagery
Allow a high resolution visualization with a relatively simple process
It is easy to use to generate other models, and to deduce from other models
DISADVANTAGES of GRID
Regular sample pattern --> Possibility of oversampling orundersampling and redundant data points. Uniform pixel size. Large amount of storage memory for large resolutions
Multisource possibility --> Large mathematical process to combine them, heavy computation processes
For very high resolutions, a too large collection of points torender in a short time
Transformation into/from other models involves a heavy computational mathematic process
TIN
Vector based modelMade up of Irreguarly distributed points and lines with three dimenion
Vertices are connected with the edges to form a network of triangles
TIN (TRIANGULAR IRREGULAR NETWORK)Different methods of Interpolation of TIN:
Delaunay triangulationDistance ordering
ArcGIS use Delaunay triangulation
The edge of the TINs forms continuous non overlapping triangular facets
Nodes and edgeNodes, edge and facet of TIN
TIN (TRIANGULAR IRREGULAR NETWORK)Delaunay triangulation
Delaunay triangulation is a proximal method that satisfies the requirement that a circle
drawn through the three nodes of a triangle will contain no other node
TIN (TRIANGULAR IRREGULAR NETWORK)Distance ordering
compute the distance between all pairs of points
sort from lowest to highestconnect the closest pair of points until it covers all the points to form triangulation
this tends to produce many skinny triangles instead of the preferred "fat" triangles.
TIN (TRIANGULAR IRREGULAR NETWORK)Data Structure:
TIN applied for both regularly and irregularly located data
A regular grid network can be formed by interpolation from a triangular network
Delaunay triangulation use static data structure
The input feature used to form the dem remains in same position
TIN (TRIANGULAR IRREGULAR NETWORK)Data Structure:
It is possible to create a TIN surface from features,
such as points, line, and polygons that contain
elevation information
Acceptable data size:10 to 15 million nodes represents the largest size for Win32. The recommended size is to bound at a few million for the sake of usability and performance.
ADVANTAGES of TIN:
The position of input feature remain unchanged
Fewer points needed for the same accuracy
Less dik space is neededTIN preserves all the precision of
input dataPreisely located feature on a surfaceresolution adapts to terrainTypically used for high precision
modeling of smaller areas
DISADVANTAGES of TIN:
Usually TIN expects units to be in feet or meters, not decimal degrees
Delaunay Triangulation is not valid when triangulation constructed using angular coordinate from the geographic coordinate system.
More expensive to build and process less widely available than the raster surface
model TIN is seems to be less efficient than
processing raster data.
MULTI-RESOLUTION It provides an abstraction for representing,
manipulating, and visualizing large volumes of spatial data at multiple levels of detail and accuracy (LOD).
vertex removal, edge collapse, and triangle collapse. It shows topographic features: peak, pit, ridge
channel, pass, valley, concave or convex area.
MULTI-RESOLUTION
They have been improved and by using least square adjustment they can add or remove
details by changing resolution.
ALGORITHMS
ALGORITHMS
B-Spline algorithmMulti-TINRegular Triangle MeshSimplification
ADVANTAGES
- Easy analysis of topographic parameters at different resolutions.
- This model can be used for huge data with level of detail (LOD) in online form.
- It may remove noise and errors in the input data and
- Maintainance of the topology of the isolines of the TIN at full resolution at differents LODs.
DISADVANTAGES- This method is so
complicated and using different algorithms in different level and sometimes least square adjustment for unique answer
- There is no technique for simplification and multi-resolution modeling of tetrahedral meshes.
- Irregularities caused by real small scale landforms in the landscape.
USAGE AND APPLICATION
Multi-Resolution method can be fundamental for applications involving geometric navigation and computations on the mesh.
For example: contour line extraction, drainage network computation, path planning, etc.
SAMPLES
QUADTREE
Quadtree is a grid-based structure and has variable resolution.
A quadtree have tree data structure in which each internal node has exactly four children.
STEPS
TYPES
The restricted quadtree for regularly-sampled surface data
PMR quadtree for irregularly-sampled data.
RQT QUADTREE
RQT is like decomposition of quadtree which employs only shared vertices.
PMR QUADTREE
Uses probabilistic splitting rule
SAMPLES
ADVANTAGES of QUADTREE
Need less storage space
Compact representation of the terrain
Fast LOD triangulation and rendering, and are easier to implement as well.
DISADVANTAGES of QUADTREE
Not very efficient structure to represent grid DTM data, continuous surface, and unclassified imagery data.
Difficult to modify any changes to the pattern of the data, requires recalculation of the quadtree.
SAMPLES
FINAL COMPARISON
DEM TIN MR QTRegular Sample Pattern √ √ X √ X √ X
Data Storage X √ √ √Multisource Possibility √ √ √ √
Visualisation √ √ √ √
Conversion √ √ √ XSpeed of Performance X √ √ √Level of Details (LOD) X √ √ √
RESOURSES:Book and Research Paper Resources Emanuele Danovaro, Leila De Floriani, Enrico Puppo1, and Hanan Samet, Out-of-core
Multi-resolution Terrain Modeling, Department of Computer and Information Science University of Genoa - Via Dodecaneso, 35, 16146 Genoa, Italy
Zhi Wanga, Qingquan Lia, Besheng Yanga, Multi Resolution Representation of Digital Terrain Models with topographical features presentation, State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University,
Emanuele Danovaro, Leila De Floriani, Paola Magillo, Mohammed Mostefa Memoudi,Enrico Puppo, MorphologyDriven Simplification and Multiresolution Modeling of Terrains, Dipartimento di Informatica e Scienze dell’Informazione Universit `a di Genva
Jan Rasmus SULEBAK and Øyvind HJELLE,2003, Multiresolution Spline Models and Their Applications in Geomorphology, SINTEF Applied Mathematics, P.O. Box 124, Blindern, N-0314 Oslo, Norway
HÉLIO PEDRINI, 2001, Multi-Resolution Terrain Modeling based on Triangulated Irregular Networks, Revista Brasileira de Geociências 31(2):117-122, 2001
Leila De Floriani , Paola Magillo, Regular and Irregular MultiResolution Terrain Models: a Comparison Dept. of Computer Science University of Genova
Web Resourceshttp://www.etsimo.uniovi.es http://www.etsimo.uniovi.es http://www.gtbi.net http://en.wikipedia.org http://www.technion.ac.il http://www.wiley.com http://eprints.utm.my http://www.earsel.org http://www.igp-data.ethz.ch http://www.cs.cmu.edu http://www.sciencedirect.com http://www.aquadoc.fr
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