Integration of Street Networks and LIDAR for
Modelling and Visualisation of Terrain Data
2. DATA SOURCES
2.1 ATKIS
Especially the wide spread of car navigation applications has lead to a growing availability of
road data in different formats. One example is the Geographic Data File (GDF), which is a
European standard that is used to describe road networks and related data especially for car
navigation purposes. Similar information is available from the so-called ATKIS-DLM. ATKIS
(Authoritative Topographic Cartographic Information System) has been developed by survey
offices of the federal states of Germany (AdV, 1998). It is structured into seven functional
classes like e.g. settlement, vegetation and traffic. Each functional class is again subdivided into
object groups. Concerning traffic, its object groups are road traffic, railway traffic, air traffic,
etc. An object group includes several object types. Object types specify the geometrical repre-sentation
and the attributes of real world objects. In case of road traffic, available object types
are for example roads, squares or footpaths. Both formats are acquired in an approximate scale
of 1:25.000 with a positional accuracy below three meters. The street regions are defined by
street axis and width as attributes.
2.2 DTM and LIDAR
The Digital Terrain Model (DTM) used in our research was already available from terrestrial
and photogrammetric measurement. With a ground resolution of approximately 10m the DTM is
rather coarse with the exception of some isolated spots that have been manually captured at a
higher resolution. As the resolution is generally good enough for a real-time visualisation of
large-scale landscapes, the streets are not visible in the geometry itself. If an aerial or satellite
image is superposed, the roads look tilted especially around hills. Figure 2 shows the DTM with
overlaid ATKIS street network data. The LIDAR data set is from the state of Baden-Württemberg
and is pre-processed with a morphological operator as described in (Schleyer,
2001). Small objects like buildings and trees were eliminated approximately. The point distance
of the data is around 1.5m.
Figure 3. Original and filtered longitudinal street axis.
3. FILTERING OF STREETS
The street data found in ATKIS is initially a 2D network of longitudinal street axes. By combin-
ing these lineal objects with the LIDAR height values, the result is a 3D polyline. Depending on
the resolution of the LIDAR data, a large number of line segments are created in the process. In
order to reduce the number of line points and at the same time smooth the height profile, the
polyline is filtered by a line estimation approach which bears resemblance to the algorithm of
(Briese and Pfeifer, 2001). The assumption is an unsymmetrical error distribution, meaning that
points that lie above an approximated line are presumed to be erroneous due to points measured
on cars or other obstacles. These points are given a small weight in an iterative estimation proc-
ess.
Our algorithm works by creating a new polyline for each street with small line segments to pre-
serve the original street characteristics. Whereas the horizontal positions are taken from the
given polyline, the height values are newly computed. For this purpose, the heights of all line
points within a given horizontal distance are taken into a least squares adjustment to approxi-
mate a straight line. The functional model of the line is a piecewise approximation by a simple
linear polynomial of the form
h
i = al
i + b
where a and b are the unknown line parameters, li the distance between consecutive line points
and hi the observed height values. Initially, the observations are equally weighed, but the impact
of points that lie above the resulting line is then lowered for the next iteration. In this way, the
recomputed line moves towards the lower points. The iteration stops when the line does not
change significantly. The height of the point can be computed so that it lies on the resulting
straight line. See Figure 3 for an example where a street segment is filtered with a maximum of
5 iterations.
The next step is the broadening of the streets from the longitudinal axes to the actual street
width, which is an attribute that is stored in the ATKIS dataset. For this purpose, both roadsides
are created by computing new points that are half the street width from the middle axes. Before
these points are inserted into the DTM, the points that lie in the same area are identified by a
buffer operation and subsequently removed. Another application is the replacement of street
points in the LIDAR data set with the smoothened street regions in order to filter the roughness
of the data points.
