The Radial Topology Algorithm—A New Approach for Deriving 2.5D GIS Data Models

Abstract

In this paper a new method for the combination of 2D GIS vector data and 2.5D DTM represented by triangulated irregular networks (TIN) to derive integrated triangular 2.5D object-based landscape models (also known as 2.5D-GIS-TIN) is presented. The algorithm takes into account special geometric constellations and fully exploits existing topologies of both input data sets, it "sews the 2D data into the TIN like a sewing-machine" while traversing the latter along the 2D data. The new algorithm is called radial topology algorithm. We discuss its advantages and limitations, and describe ways to eliminate redundant nodes generated during the integration process. With the help of four examples from practical work we show that it is feasible to compute and work with such integrated data sets. We also discuss the integrated data models in the light of various general requirements and conclude that the integration based on triangulations has a number of distinct advantages.

Semantically correct 2.5D GIS data — The integration of a DTM and topographic vector data

“…This merging process, i.e. the introduction of the 2D geometry into the TIN, has been investigated later by several authors (Lenk and Heipke, 2006;Lenk, 2001;Klötzer, 1997;Pilouk, 1996). The approaches differ in the sequence of introducing the 2D geometry, the amount of change of the terrain morphology and the number of vertices after the integration process.…”

“…The advantage of Lenk's approach (Lenk and Heipke, 2006;Lenk, 2001) compared to others is that the surface shape represented by the integrated TIN is identical to the one of the initial DTM TIN. The disadvantage is that the approach results in a large amount of Steiner points which lead to additional observation equations and/ or inequality constraints (see Section 3.2).…”

DTM Correction in Areas of Steep Slopes

“…This process is completed after all meshes M Label have been inserted. Based on all vertices and deleting duplicate vertices, Delaunay Triangulation is applied producing a waterproofed mesh (Lenk, Heipke, 2006). In Figure 11, the final DTM based on OSM data and L1 spline interpolation is displayed.…”

Network Snakes for Adapting Gis Roads to Height Data of Different Data Sources – Performance Analysis Using Als Data and Stereo Images

“…ATKIS roads typically have an accuracy of 3-5 m, with local deviations that may reach 10 m. Roads from OSM have a very inhomogeneous accuracy due to the patchwork characteristic of this data set, which may disturb the local geometry of the road network and makes the adaptation process more challenging. Pilouk (1996) as well as Lenk and Heipke (2006) investigated the incorporation of the 2D geometry of the vector objects into a DTM modelled by a TIN, but the inconsistencies between the vector data and the DTM were not considered. Rousseaux and Bonin (2003) model 2D linear objects such as roads and dikes as 2.5D surfaces by using attributes of the GIS data base and the DTM heights with the goal of generating an improved DTM.…”