NETWORK MODELLING AND SEMANTIC 3D CITY MODELS: TESTING THE MATURITY OF THE UTILITY NETWORK ADE FOR CITYGML WITH A WATER NETWORK TEST CASE

<p><strong>Abstract.</strong> Recent advances in semantic 3D city modelling and a demand from utility network operators for multi-utility data models integration have contributed to the emergence of an open Application Domain Extension (ADE) of the CityGML data model tailored to multiple types of utility networks. This extension, called the Utility Network ADE, is still in active development. However, work is already well underway to create data samples and to develop methods of modelling thereupon. In this paper, a mapping of the Utility Network ADE data model to a relational database schema is introduced. A sample of a freshwater network using the Utility Network ADE and based on data from the city of Nanaimo, Canada, is also presented. This sample has also been imported into a relational database schema built upon the 3DCityDB (a database implementation of CityGML) extended with a schema of the Utility Network ADE. Further to this, a series of basic network analysis functions have been defined and implemented in SQL to interact with the database so as to carry out sample atomic processes involved in network modelling, such as reading semantic properties of elements, calculating composite physical parameters of the network as a whole, and performing simple topological routing to serve as a guiding example for further and more complex development. A brief outlook is also presented, suggesting areas with high potential for future research and development of this nascent data model.</p>

Topologically Consistent Space, Time, Version, and Scale Using Alexandrov Topologies

A novel approach to higher dimensional spatial database design is introduced by replacing the canonical solid–face–edge–vertex schema of topological data by a common type SpatialEntity, and the individual “bounded-by” relations between two consecutive classes by one separate binary relation BoundedBy on SpatialEntity defining an Alexandrov topology. This exposes mathematical principles of spatial data design. The first consequence is a mathematical definition of topological “dimension” for spatial data. Another is that every topology for spatial data is an Alexandrov topology. Also, version histories have a canonical Alexandrov topology, and generalizations can be consistently modeled by continuous foreign keys between LoDs. The result is a relational database schema for spatial data of dimension 6 and more, seamlessly integrating space-time, LoDs, and version history. Topological constructions enable queries across these different aspects. Giving points coordinates amounts can give rise to topological inconsistencies which can be measured with topological invariants.