An Approach for the Semantically Correct Integration of a Dtm and 2d Gis Vector Data

The most commonly used topographic vector data, the core data of a geographic information system (GIS) are currently twodimensional. The topography is modelled by different objects which are represented by single points, lines and areas with additional attributes containing information, for example on function and size of the object. In contrast, a digital terrain model (DTM) in most cases is a 2.5D representation of the earth’s surface. The integration of the two data sets leads to an augmentation of the dimension of the topographic objects. However, inconsistencies between the data may cause a semantically incorrect result of the integration process. This paper presents an approach for a semantically correct integration of a DTM and 2D GIS vector data. The algorithm is based on a constrained Delaunay triangulation. The DTM and the bounding polygons of the topographic objects are first integrated without considering the semantics of the objects. Then, those objects which contain implicit height information are further utilized: object representations are formulated and the semantics of the objects is considered within an optimization process using equality and inequality constraints. The algorithm is based on an inequality constrained least squares adjustment formulated as the linear complementary problem (LCP). The algorithm results in a semantically correct integrated 2.5D GIS data set. First results are presented using simulated and real data. Lakes represented by horizontal planes with increasing terrain outside the lake and roads which are composed of several tilted planes were investigated. The algorithm shows first satisfying results: the constraints are fulfilled and the visualization of the integrated data set corresponds to the human view of the topography.