EuroSDR Projects - Host Ireland

assumption is made: the two longest parallel sides of the building each have one roof plane orientated
towards the side of the building, meaning a surface normal on the roof plane is perpendicular in the
xy-plane to the side of the building. This means that every point is examined regarding its surface
normal, and surface normals perpendicular to the side of the building and with approximately the
same orientation are grouped. The result is two groups of surface normals – each group representing a
roof plane.
The points of each roofplane are now adjusted by the principles of Least Squares adjustment and a
method called "the Danish method" (Juhl 1980). The Danish method is a method used in adjustments
and is used to automatically weight down points with large residuals compared to the determined roof
plane from the Least Squares adjustment. This means that points not belonging to the roof plane, e.g.
points on chimneys, are sorted out of the group automatically and will not have influence on the final
determination of the parameters of the roof planes (Juhl 1980).
Gable roofs at different levels will also show only two groups of points with approximately similar
surface normals, because the method cannot distinguish between roof planes at different levels. To
find the border between the buildings a gradient filter is applied to the grid and large gradients are
grouped by means of a connected components analysis. Groups are adjusted by Least Squares
adjustment to a line and thus a building with gable roofs at different levels is divided into rectangular
buildings with two roof planes.
3.1.2.12 Dresden University of Technology
The Dresden method (Hofmann, 2004) only uses point clouds obtained by a pre-segmentation of
airborne laser scanner data. All work was carried out by an experienced person. It is a plane-based
approach that presumes that buildings are characterized by planes. It utilizes a TIN-structure that is
calculated into the point cloud. The method only uses point clouds of the laser scanner data that
contain one building. In order to get such point clouds polygons coarsely framing the building can be
used to extract the points (e.g.in ArcGIS). The polygons can be created manually or map or ground
plan information can be used. See Figure 3-22 to Figure 3-25.
Figure 3-22: Building model algorithm by Dresden.
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