Integration of Semantic 3D City Models and 3D Mesh Models for Accuracy Improvements of Solar Potential Analyses

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.