Abstract
Abstract. Semantic 3D city models play an important role in solving complex real-world problems and are being adopted by many cities around the world. A wide range of application and simulation scenarios directly benefit from the adoption of international standards such as CityGML. However, most of the simulations involve properties, whose values vary with respect to time, and the current generation semantic 3D city models do not support time-dependent properties explicitly. In this paper, the details of solar potential simulations are provided operating on the CityGML standard, assessing and estimating solar energy production for the roofs and facades of the 3D building objects in different ways. Furthermore, the paper demonstrates how the time-dependent simulation results are better-represented inline within 3D city models utilizing the so-called Dynamizer concept. This concept not only allows representing the simulation results in standardized ways, but also delivers a method to enhance static city models by such dynamic property values making the city models truly dynamic. The dynamizer concept has been implemented as an Application Domain Extension of the CityGML standard within the OGC Future City Pilot Phase 1. The results are given in this paper.
Highlights
1.1 Semantic 3D City ModelsSemantic 3D city models describe spatial, graphical and thematic aspects of the cityscapes by decomposing and classifying the occupied physical space according to a semantic data model
The only input data required is a 3D city model according to the CityGML standard in Level of Detail 2 (LoD2), having roof and wall surfaces represented as thematic surfaces
The simulation tool operates on 3D models structured according to the CityGML standard and generates the monthly and yearly estimates of direct, diffuse, and global irradiation values for the building surfaces
Summary
Semantic 3D city models describe spatial, graphical and thematic aspects of the cityscapes by decomposing and classifying the occupied physical space according to a semantic data model. The major advantage of semantic information models in comparison to visualization models such as Google Earth and Apple Maps is that they make it possible for machines/algorithms to distinguish urban objects like buildings and use their rich thematic and geometric information for queries, statistical computation, simulation, and visualization. For this reason, today, more and more cities worldwide such as Berlin, Singapore, Paris, Zurich, Vienna, London, New York, Vancouver, Montreal, and Helsinki are
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