International Axchives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 
139 
AUTOMATIC RECONSTRUCTION OF ROOFS FROM MAPS AND ELEVATION DATA 
U. Stilla, K. Jurkiewicz 
Research Institute of Optronics and Pattem Recognition (FGAN-FOM), Eisenstockstr. 12, D-76275 Ettlingen, Germany, 
usti@gate.fim.fgan.de 
KEYWORDS: Airborne Laser Data, Digital Vector Maps, 3D-City Model, Roof Reconstruction. 
ABSTRACT 
In this paper, we describe a procedure for generating building models from large scale vector maps and laser altimeter data. In 
contrast to other approaches, the map information is not used to form hypotheses about the roofs. Our approach permits the 
recognition of additional structures as superstructures, which can not be derived from building outlines shown in the map. First, the 
vector map is analyzed to group the outlines of buildings and to obtain a hierarchical description of buildings or building complexes. 
The base area is used to mask the elevation data of single buildings and to derive a coarse 3D-description by prismatic models. 
Afterwards, details of the roof are analyzed. Based on the histogram of heights, flat roofs and sloped roofs are discriminated. For 
reconstructing flat roofs with superstructures, peaks are searched in the histogram and used to segment the height data. Compact 
segments are examined for a regular shape and approximated by additional prismatic objects. For reconstructing sloped roofs, the 
gradient field of the elevation data is calculated and a histogram of orientations is determined. Major orientations in the histogram are 
detected and used to segment the elevation image. For each segment containing homogeneous orientations and slopes, a spatial plane 
is fitted and 3D-contours are constructed. In order to obtain a polygonal description, adjacent planes are intersected and common 
vertices are calculated. 
1. MOTIVATION 
Three-dimensional city models find more and more interest in 
city and regional planning. They are used for visualization, e.g. 
to demonstrate the influence of a planned building on its 
surroundings. Although there is a great demand for such 
models in mission planning and as basis for simulation, e.g. in 
the fields of environmental engineering for microclimate 
investigations (Adrian and Fiedler, 1991) or 
telecommunications for transmitter placement (Kiimer et al., 
1993), current methods for their generation are expensive and 
very time-consuming, involving extensive manual work. 
In developed countries during the last years, many maps have 
been stored digitally and are additionally available in vector 
form. Large scale topographic maps or cadastral maps show 
ground plans with no information on height of buildings or 
shape of the roof. So far, information on height was derived 
from ground surveys or from stereo pairs of aerial images. 
Nowadays, elevation data are commercially available from 
airborne laser scanners (e.g. Lohr, 1998). Knowing the precise 
position and orientation of the airborne platform from 
differential Ground Positioning System (dGPS) and Laser 
Inertial Navigation System (LINS), 3D measurements of the 
geographic position of surface points can be determined with 
dm accuracy. The sampled surface points, distributed over a 
strip of 250-500m width, allow the generation of a geocoded 2D 
array with elevation data in each cell (elevation image). Single 
flight strips are merged to a consistent digital elevation model 
(DEM) of the whole survey area. 
Fig. 1. 3D view of elevation data (Karlsruhe). 
Fig. 2. 3D elevation data textured using an aerial image. 
The reflected signal can be recorded and analysed in first pulse 
and/or last pulse mode. While first pulse registration is the 
optimum choice when surveying the top of objects (e.g.