The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics

Chen, Jun

Bibliographic data

Monograph

Persistent identifier:: 856566209

Author:: Chen, Jun

Title:: The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics

Sub title:: May 23 - 25, 2001, Bangkok, Thailand

Scope:: VI, 434 Seiten

Year of publication:: 2001

Place of publication:: Pathumthani, Thailand

Publisher of the original:: AIT

Identifier (digital):: 856566209

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: MULTIRESOLUTION TERRIAN MODEL. Jin ZHANG

Document type:: Monograph

Structure type:: Chapter

ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS", Bangkok, May 23-25, 2001 364 MULTIRESOLUTION TERRIAN MODEL Jin ZHANG Dep. Of Surveying and Mapping Taiyuan University of Technology (Middle Campus) 030024 Zhangjingis@yeah.net KEY WORDS: DEM Multiresolution LOD GIS ABSTRACT: DEM have been studied in the GIS literature for a long time. They have become a major constituent part of geographic information processing in the earth and engineering sciences. We use DEM to represent the terrain in GIS. The more data are available, the better representations of a terrain can be built. But not all tasks in the framework of a given application necessarily require the same accuracy, and even a single task may need different levels of accuracy in different areas of the domain. For instance, in landscape visualization, or in flight simulation, the terrain must be rendered at different degrees of resolution depending on their distance from the viewpoint. Multiresolution models, such as LOD, offer the possibility of representing and analyzing a terrain at a range of different levels of detail. Many papers have published in LOD studying. But in GIS, the most important issue or the key problem, such as combining different multiresolution spatial data into single representation and for tiled terrain how to solve tile to tile edge matching in multiresolution model and how to fully consider the terrain feature in multiresolution TIN model, has not been solved so good till today. The situation in LOD or Multiresolution model first overviewed in this paper. Then we pay more attention in how to fully consider the terrain feature in multiresolution TIN model and how to solve the tile-to -tile edge matching. 1. INTRODUCTION (DTMs), sometimes called (DEMs), have been studied in the GIS literature for a long time. They have become a major constituent part of geographic information processing in the earth and engineering sciences. DTMs require specific input data, data models and algorithms that are different from those needed to represent and process 2D data. On the other hand, the activity of modeling and processing digital terrain data must be regarded as a component of a GIS which needs to be clearly linked to other processing functions of a spatial database. Elevation data are acquired either directly on a terrain through sampling techniques, or through the digitization of existing elevation maps (contours). Raw data come in the form of points either regularly distributed or scattered on a two dimensional domain; sometimes points are arranged to form polygonal lines, approximating either lineal features of the terrain, or contour lines. On the basis of such data, different models can be built, depending on the needs of specific applications: contour maps, raster data models on dense grids, approximations of the surface over the whole domain. Surface models can be defined either piecewise on a partition of the domain (either regular or irregular), or through a single polynomial function of high order. A topographical surface (or terrian) T can be defines as the image of a real bivariate function f defined over a domain D in the Euclidean plane, i.e., T={( x,y,f(x,y ))| ( x,y)e D}. Given a real value q, the set C„ (q ) = {(x,y ) e D | f(x,y )=q} is the set of contours T of at elevation q. If q is not an extreme value of f, then Co (q ) is a set of simple nonintersecting lines, which are either closed or open with endpoints on the boundary of domain D . The more data are available, the better representations of a terrain can be built. Modern acquisition techniques provide huge datasets that permit to achieve high accuracy, but this is paid high costs for storage and processing. On the other hand, not all tasks in the framework of a given application necessarily require the same accuracy, and even a single task may need different levels of accuracy in different areas of the domain. For instance, in landscape visualization, or in flight simulation, the terrain must be rendered at different degrees of resolution depending on their distance from the viewpoint. The concept of multiresolution terrain model is generically related to the possibility of using different representations of a geometric object (e.g., a terrain), having different levels of accuracy and complexity in order to optimize the tradeoff between accuracy, and cost of data storage and processing. Approximate models represent a terrain at a predefined reduced LOD; multiresolution models offer the possibility of representing and analyzing a terrain at a range of different levels of detail. So what is needed is a hierarchy of representations at various levels of detail; this makes it possible for a given view point to render the terrain at an adequate level of detail. Subsequent levels of the hierarchy should not differ too much in appearance, switching to more and more detailed representations when zooming in (this happens for instance in flight simulation, when one is landing) should not cause disturbing “jumps" in the image. On the other hand, the reduction of the number of triangles in subsequent levels should not be too small, otherwise too many levels would be needed, resulting in an unacceptable increase in storage. It is in general insufficient for rendering purposes to use only one level of detail at a time; although some part of the terrain may be close to the view point, another part (the horizon, for example) can still be far away. Hence, one would like to combine parts from different levels into a single representation of the terrain, such that each part of the terrain is rendered with appropriate detail. This means that the levels cannot be completely independent, as it should be possible to glue them together smoothly. The author studys these issue in the following. We first overview the situation of multiresolution terrain model or LOD. Then we primary study three main methods, including: Hierarchical TIN, Adding or deleting point based Delaunay rules and Hierarchical Dynamic Simplification methods. These methods have its advantage and disadvantage. Main problems may be consistency between adjancet edges and tile edges in HTIN model. The other problems are how to divide spatial area and code it and how to fully consider the terrain feature in adding and deleting points based on Delaunay rule retriangulating. We introduce the HOOM and technology of tile-to-tile edge match solve these key problem. 2. THE SITUATION OF SURFACE SIMPLIFICATION: A SURVEY OF TRADITIONAL LEVEL-OF-DETAIL ALGORITHMS Polygonal simplification is a very old topic in computer graphics. As early as 1976 James Clark described the benefits of representing objects within a scene at several resolutions, and flight simulators have long used hand-crafted multi-resolution models of airplanes to guarantee a constant frame rate [Clark, 76 and Cosman, 81]. Recent years have seen a flurry of research into generating such multi-resolution representations of objects automatically by simplifying the polygonal geometry of the object. Note that terrains, or tessellated height fields, are a special category of polygonal models. A bewildering variety of simplification techniques have appeared in the recent literature; this section attempts to classify the important similarities and differences among these techniques.

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