Data Model for Hybrid 3D Geoinformation Systems

Recent trends in the area of geoinformatics attest to the considerable change in data acquisition and data processing that can currently be recognized: rapidly advancing developments of new sensor technologies for both highly specialized measuring systems and low-cost devices account for the tremendous increase in the availability of 3D geodata. However, there is still a lack of appropriate tools for integrated data management and analysis solutions that can cope with the great diversity of 3D geodata. Thus, we deal with the development of a hybrid 3D geoinformation system which is able to combine and analyse heterogeneous 3D geodata in an efficient and consistent way.

The huge diversity of geodata becomes obvious when considering existing data models. While the 2D world mainly distinguishes between raster and vector representations, much more modelling concepts are in use for data of higher dimensions. Typical data models for 2.5D surfaces are grids or TINs; 3D solids can be described by voxel and boundary representations (BRep) as well as by mathematical definitions like parametric instancing or half-space modelling. Constructive solid geometry (CSG) and cell decomposition specify different modelling strategies for generating complex 3D objects through the combination of several basic 3D primitives, which can be represented in any of the aforementioned data models for solids. Beyond that, geodata can also be heterogeneous with respect to its quality properties (e.g. accuracy, density, completeness). Considering all these aspects, a meaningful and effective usage of geodata necessarily requires geoinformation systems which are hybrid in the sense of data model, dimension and quality.

We developed a data model which is meant to provide an application-independent conceptual basis for smart geoinformation systems. The data model is hybrid with respect to structural and geometric aspects. Through targeted extensions of the widely accepted standard ISO 19107, our concept is able to bridge the gap between 2D, 2.5D and 3D data, and break down barriers between various modelling strategies. Ignoring performance issues, our data model is based on a working hypothesis which states that all modelling types considered so far can be transferred to BRep. By internally creating boundary representations for all data sets, even inhomogeneous geodata can be reduced to a common hybrid core comprising nodes, edges, faces and solids. For a start, we assume an ideal world (Figure 1, left) in which coordinates of corresponding object representations coincide exactly. In this case, consistency for multi-representations is ensured, and geometric correspondences between different object representations (so-called hybrid identities) are given implicitly through incident geometries.

In practice, we usually face geodata which is geometrically and topologically heterogeneous due to inaccuracies, generalization processes or incomplete data acquisition. As a consequence, multiple object representations derived thereof show significant discrepancies between corresponding geometries (Figure 1, right). Thus, knowledge about hybrid identities is not given implicitly any more, but has to be added explicitly instead. The concept we developed for the explicit modelling of hybrid identities allows not only for the connection of objects or object parts given in different types, geometric data models, dimensions and quality levels; it also supports consistency analyses and updating measures which is an important aspect considering the frequently occurring changes in geodata. The system supports multi-representations which can be based on either the same or differing data models. Additionally, it is also possible to model parts of a single object using different modelling concepts. While, for example, the main body of a building can efficiently be represented by cell decomposition, decorative elements such as 2.5D reliefs could be added as fine surface meshes.