Abstract
Abstract. High-resolution 3D mesh models are an inexpensive and increasingly available data source for 3D models of cities and landscapes of high visual quality and rich geometric detail. However, because of their simple data structure, their analytic capabilites are limited. Semantic 3D city model contain rich thematic information and are well suited for analytics due to their deeply structured semantic data model. In this work an approach for the integration of semantic 3D city models with 3D mesh models is presented. The method is based on geometric distance measures between mesh triangles and semantic surfaces and a region growing approach using plane fitting. The resulting semantic segmentation of mesh triangles is stored in a CityGML data set, to enrich the semantic model with an additional detailed geometric representation of its surfaces and a broad range of unrepresented features like technical building installations, balconies, dormers, chimneys, and vegetation. The potential of the approach is demonstrated on the example of a solar potential analysis, which estimation quality is significantly improved due to the mesh integration. The impact of the method is quantified on a case study using open data from the city of Helsinki.
Highlights
Today, 3D city models are available in different representations
The international standard CityGML is an open data model and encoding specification for representing and exchanging semantic 3D city models adopted by the Open Geospatial Consortium (OGC) in 2012 (Kolbe, 2009, Open Geospatial Consortium, 2012)
We show how the integration of semantic 3D city models with 3D mesh models can help in significantly improving the accuracy of solar potential analysis
Summary
3D city models are available in different representations. Both semantic 3D city models and 3D mesh models are established tools for digitally describing the physical environment.Their characteristics, usage scenarios, and acquisition methods, are different.
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