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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