This paper provides the innovative approach of using a spatial extract, transform, load (ETL) solution for 3D building modelling, based on an unmanned aerial vehicle (UAV) photogrammetric point cloud. The main objective of the paper is to present the holistic workflow for 3D building modelling, emphasising the benefits of using spatial ETL solutions for this purpose. Namely, despite the increasing demands for 3D city models and their geospatial applications, the generation of 3D city models is still challenging in the geospatial domain. Advanced geospatial technologies provide various possibilities for the mass acquisition of geospatial data that is further used for 3D city modelling, but there is a huge difference in the cost and quality of input data. While aerial photogrammetry and airborne laser scanning involve high costs, UAV photogrammetry has brought new opportunities, including for small and medium-sized companies, by providing a more flexible and low-cost source of spatial data for 3D modelling. In our data-driven approach, we use a spatial ETL solution to reconstruct a 3D building model from a dense image matching point cloud which was obtained beforehand from UAV imagery. The results are 3D building models in a semantic vector format consistent with the OGC CityGML standard, Level of Detail 2 (LOD2). The approach has been tested on selected buildings in a simple semi-urban area. We conclude that spatial ETL solutions can be efficiently used for 3D building modelling from UAV data, where the data process model developed allows the developer to easily control and manipulate each processing step.

According to the Köppen-Geiger climate classification, Europe is under the influence of at least ten different climate types. Thus, various climates can be found, from the polar tundra and cold climate in the Alps and northern European regions, to hot-arid climate in southern parts of Spain. This level of climate diversity makes the European territory interesting for the analysis from the bioclimatic building design perspective. Therefore, the purpose of the research was to assess the bioclimatic potential of selected European locations. The calculation of bioclimatic potential was done by acquiring the typical meteorological year (TMY) data comprised of climate characteristics, such as air temperature, air relative humidity and received solar irradiance, which was later processed by BcChart tool. In order to make bioclimatic potential maps of Europe, the points with uniform point sampling were generated. Furthermore, several additional locations of great interest were selected based on population density. The bioclimatic potential was used to define the prevailing passive building design strategies and measures at the analysed locations. At the same time, the in-depth analysis was conducted using the geospatial data and GIS tools, where the bioclimatic potential results at the selected locations were additionally analysed in relation to Köppen-Geiger climate types. The resulting bioclimatic potential maps can be used as a relevant onset for the policy makers in order to improve regional development strategies for building design.

Abstract. Recently, building outline extraction from point cloud has gained momentum in particular in the context of 3D building modelling based on a data-driven approach, which has also been our motivation. For an accurate building outline extraction from a point cloud, various factors affecting the quality should be considered. In this research, we analysed the influence of point cloud density on the quality of the extracted building outlines. The input data was a classified photogrammetric point cloud, obtained from the dense image matching of images acquired by an optical sensor mounted on the unmanned aerial vehicle (UAV). For outline extraction, we selected two procedures, namely the direct approach and the raster approach. In the direct approach, building outlines are extracted directly from the points that have been classified as buildings. First, a convex hull with the alpha algorithm is estimated, which is further generalised with the Douglas-Peucker algorithm. This is followed by the shape regularisation to ensure perpendicular angles of the outline. In the raster approach, we first rasterised the building points and then extracted the building outlines using the Hough transform. In both approaches, the result is a roof outline in a 2D plane representing the maximum extent of the building above the surface. The building outlines were extracted from point clouds with five different densities. For both approaches, the quality assessment has shown that point cloud density has an impact on the building outline extraction, especially on the completeness of the outlines.