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# Digital terrain representations(last)

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Introduction to basic digital terrain representations

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### Digital terrain representations(last)

1. 1. DIGITAL TERRAIN REPRESENTATIONSGROUP MEMBERSAgasha Ochneva, BiniyamTilahunGülendam Baysal, RoyaOlyazadehMuhammad Maimaiti, ShiuliPervinDavid González SánchezRoberto Mediero Martí
2. 2. GENEREAL EXPLANATION OF DIGITAL TERRAIN REPRESENTATIONS A digital terrain model is a topographicmodel of the bare earth –terrain relief - thatcan be manipulated by computer programs. The data files contain the spatial elevation data of the terrain in a digital format
3. 3. DATA SOURCE: Ground survey, Digitizing contours, Digital Photogrammetry, Direct image grid DEM, LiDAR, RADAR, SONAR;
4. 4. CLASSIFICATION The pattern of DTM data could be: regula regular irregular or irregular r 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,
6. 6. Grid DEM Main 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.
7. 7. Grid DEM Main 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
8. 8. Grid DEM Main 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
9. 9. SAMPLES
10. 10. SAMPLES
11. 11. 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
12. 12. DISADVANTAGES of GRID Regular sample pattern --> Possibility of oversampling or undersampling 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 to render in a short time Transformation into/from other models involves a heavy computational mathematic process
13. 13. TINVector based modelMade up of Irreguarly distributed points and lines with three dimenionVertices are connected with the edges to form a network of triangles
14. 14. TIN (TRIANGULAR IRREGULAR NETWORK) Different methods of Interpolation of TIN: Delaunay triangulation Distance ordering  ArcGIS use Delaunay triangulation  The edge of the TINs forms continuous non overlapping triangular facetsNodes and edge Nodes, edge and facet of TIN
15. 15. TIN (TRIANGULAR IRREGULAR NETWORK) Delaunay triangulation Delaunay triangulation is a proximal method thatsatisfies the requirement that a circle drawn through the three nodes of a triangle will contain no other node
16. 16. TIN (TRIANGULAR IRREGULAR NETWORK) Distance orderingcompute the distance between all pairs of pointssort from lowest to highestconnect 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.
17. 17. 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
18. 18. 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 informationAcceptable data size:10 to 15 million nodes represents the largest size for Win32. Therecommended size is to bound at a few million for the sake ofusability and performance.
19. 19. ADVANTAGES of TIN: The position of input feature remain unchanged Fewer points needed for the same accuracy Less dik space is needed TIN preserves all the precision of input data Preisely located feature on a surface resolution adapts to terrain Typically used for high precision modeling of smaller areas
20. 20. 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.
21. 21. 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
22. 22. ALGORITHMSThey have been improved and by using least square adjustment they can add or remove details by changing resolution.
23. 23. ALGORITHMS B-Spline algorithm Multi-TIN Regular Triangle Mesh Simplification
24. 24. ADVANTAGES DISADVANTAGES- Easy analysis of - This method is so topographic parameters at complicated and using different resolutions. different algorithms in different level and- This model can be used for sometimes least square huge data with level of adjustment for unique detail (LOD) in online form. answer - There is no technique for- It may remove noise and simplification and multi- errors in the input data and resolution modeling of tetrahedral meshes.- Maintainance of the topology of the isolines of the TIN at full resolution at - Irregularities caused by differents LODs. real small scale landforms in the landscape.
25. 25. USAGE AND APPLICATION Multi-Resolution method can be fundamental for applicationsinvolving geometric navigation and computations on the mesh. For example: contour line extraction, drainage networkcomputation, path planning, etc.
26. 26. SAMPLES
27. 27. 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.
28. 28. STEPS
29. 29. TYPES The restricted quadtree for regularly- sampled surface data PMR quadtree for irregularly-sampled data.
30. 30. RQT QUADTREERQT is like decomposition of quadtree which employs only shared vertices.
31. 31. PMR QUADTREEUses probabilistic splitting rule
32. 32. SAMPLES
33. 33. ADVANTAGES of QUADTREE Need less storage space Compact representation of the terrain Fast LOD triangulation and rendering, and are easier to implement as well.
34. 34. 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.
35. 35. SAMPLES
36. 36. FINAL COMPARISON DEM TIN MR QTRegular SamplePattern √ √ X √ X √ XData Storage X √ √ √MultisourcePossibility √ √ √ √Visualisation √ √ √ √Conversion √ √ √ XSpeed ofPerformance X √ √ √Level of Details(LOD) X √ √ √
37. 37. 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 GenovaWeb Resources http://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
38. 38. Obrigado por sua atenção!Gracias por su atención! Thank you for your attention! Þakka þér fyrir athygli þína! Gràcies per la seva atenció!