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contextual relations to background objects. Vice-versa, context helps to guide the interpretation, e.g. “easiest first”, 
making the extraction easier to handle and more robust. This can be supported by considering the function of roads 
connecting different sites and thus forming a fairly dense and sometimes even regular network. Data from different 
and complementary sources, especially overlapping images and accurate height information, is very useful to address 
occlusions and shadows in urban environments. Furthermore, all of these knowledge sources need to be integrated into a 
single system. 
3 MODELING URBAN ROADS 
3.1 Appearance of Roads 
Figure 1 visualizes two examples of urban roads. It is obvious that these images exhibit a more complex content than 
scenes showing rural areas since the number of different objects and their heterogeneity is much bigger. 
Generally, this implies that more details of the road and context model must be 
exploited for road extraction. In dense urban areas, for instance, some of the roads 
comprise several lanes that are linked by complex road crossings. What is more, 
by the increase of the number of objects the complexity of their relations grows, 
too. In Fig. la), for example, some parts of the streets are occluded by vehicles, 
especially at the road sides. Hence in this particular case, a road is mainly defined 
by groups of (parking) cars but not by parallel road sides or homogeneous surface. 
A similar relation is the occurrence of shadows cast by high buildings. A road 
generally appears bright in open areas, but in the case of shadows two problems 
for the extraction arise: (1) the surface is darkened significantly, and (2) at the 
margin of the shadow regions, strong gray value edges in almost any direction 
may occur on the road disturbing the usually homogeneous reflectance. 
Figure 1b) shows a different kind of problem: The roof of the rectangular building 
in the center of the image could be wrongly identified as a parking lot because 
its shape and reflectance properties match the ones of a road-like object almost 
perfectly. Only the combination with height data as given by a DSM (Digital 
Surface Model) or, as in this case, implicitly given by a corresponding shadow 
region provides enough information for avoiding this misdetection. 
What follows is, that on one hand those features of a road ought to be selected 
on which the influence of the above mentioned phenomena is minimal. On the 
other hand, it is very important to consider context objects, in particular different 
kinds of vehicles, in order to explain abnormal changes in the appearance of a 
road. Hence, the model described below consists of two parts: The first part de- 
scribes characteristic properties of roads in the real world and in aerial imagery, 
and represents a road model derived from these properties. The second part de- 
fines different local contexts and assigns those to the global contexts. 
  
“®) 
3.2 Road Model 
Figure 1: Roads in urban areas 
Besides an (at least partially) homogeneous surface and more or less densely ar- 
ranged vehicles, one obvious feature of roads in urban areas are road markings 
separating a road into different lanes. To make use of them, we extended the model of our previous work and now model 
roads and complex junctions as a combination of several lanes consisting of one or more lane segments. Dashed or solid 
linear markings define the border of a lane segment. The road model condensed from the findings of the previous section 
is illustrated in Fig. 2 in form of a hierarchical semantic network. 
The model describes objects by means of “concepts,” and is split into three levels defining different points of view. 
The real world level comprises the objects to be extracted and their relations. On this level the road network con- 
sists of junctions and road links that connect junctions. Road links are constructed from road segments. In fine scale, 
road segments are aggregated by lanes, which consist of pavement and markings. For markings there are two spe- 
cializations: Symbols and line-shaped markings. The concepts of the real world are connected to the concepts of the 
geometry and material level via concrete relations (Tónjes, 1997), which connect concepts representing the same ob- 
ject on different levels. The geometry and material level is an intermediate level which represents the 3D-shape of an 
object as well as its material (Clément et al., 1993). The idea behind this level is that in contrast to the image level it 
describes objects independently from sensor characteristics and viewpoint. Road segments are linked to the "straight 
bright lines" of the image level in coarse scale. In contrast to this, the pavement as a part of a road segment in fine 
scale is linked to the “elongated bright region” of the image level via the “elongated, flat concrete or asphalt region.” 
  
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B3. Amsterdam 2000. 407