Abstract
Abstract. The paper presents a new data-driven approach to generate CityGML building models from airborne laser scanning data. The approach is based on image processing methods applied to an interpolated height map and avoids shortcomings of established methods for plane detection like Hough transform or RANSAC algorithms on point clouds. The improvement originates in an interpolation algorithm that generates a height map from sparse point cloud data by preserving ridge lines and step edges of roofs. Roof planes then are detected by clustering the height map’s gradient angles, parameterizations of planes are estimated and used to filter out noise around ridge lines. On that basis, a raster representation of roof facets is generated. Then roof polygons are determined from region outlines, connected to a roof boundary graph, and simplified. Whereas the method is not limited to churches, the method’s performance is primarily tested for church roofs of the German city of Krefeld because of their complexity. To eliminate inaccuracies of spires, contours of towers are detected additionally, and spires are rendered as solids of revolution. In our experiments, the new data-driven method lead to significantly better building models than the previously applied model-driven approach.
Highlights
The German state of North Rhine-Westphalia has published a country-wide city model in CityGML data format, see (Groger et al, 2012, Kolbe, 2009)
Roofs have been reconstructed from sparse point clouds obtained from airborne laser scanning data (LiDAR) by using a model-driven approach, see (Oestereich, 2014)
The goal of this paper is to improve the quality of this city model with a special focus on buildings with complex roof structures like churches
Summary
The German state of North Rhine-Westphalia has published a country-wide city model in CityGML data format, see (Groger et al, 2012, Kolbe, 2009). The method appeared to be very sensitive to noise To overcome these problems, the algorithm of (Lafarge and Mallet, 2012) tests the quality of the region growing’s outcome and rejects poor quality planes and other geometric structures. Guided by the disadvantages observed for these established methods, we search for roof facets on a height map that is interpolated from the point cloud instead of directly using sparse cloud data. The algorithm is described that covers the detection of facets by clustering similar gradient values of the interpolated height map, as well as the computation of Hesse normal forms of the facets’ planes.
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