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hypotheses are not selected because of dark shadows and some of the correct hypotheses are interrupted because of 
cars. Dark regions can be detected by shadow prediction and verification using the respective context relation. Since 
we already use this kind of information in our road extraction system for rural areas, extracting the exact shadow re- 
gion in urban areas was not of our primary interest. We rather turned the focus of our work on the detection of 
vehicles which should provide the system an explanation why such inhomogeneities inside of lane segments occur. 
We currently work on the implementation of a model-based scheme 
for vehicle detection. It starts with the extraction of rectangular 
edge structures. Then, adjacent rectangles are iteratively concate- 
nated to set up 2D hypotheses for the outlines and center-lines of 
isolated vehicles or vehicle convoys. Basically, this restricts the hy- 
pothesis formation to approximate nadir views. However, since we 
generally have to select such views in urban areas due to occlud- 
ing objects, this seems to us being no true limitation. Figure 8 a) 
shows a particular part of a lane axis (in black) which was not ac- 
cepted by the homogeneity check. (In fact, it is the horizontal road, 
resp. lane, in the center of the image shown in 7 b) ). The result of 
hypothesis formation is visualized in Fig 8 b), extracted edge seg- 
ments are plotted in black. The generated car hypothesis (shown 
in white) consists of individual rectangles which complete the frag- 
mented edges resulting in a chain of rectangles. The medial axis of 
the rectangle chain corresponds to the center-line of the hypothe- 
sized vehicle. 
  
      
Once, we have generated a hypothesis for a vehicle's center-line, we ih: (b) 
are able to check for significant geometric and radiometric symme- Figure 7: (a) Detected groups of markings, (b) 
tries along and across the vehicle. In aerial imagery, most vehicles — lane segments with bright homogeneous interior 
are characterized by dark windows. Furthermore, their front, top, 
and rear, show similar reflectance. The use of color images could be a potential aid for the decision which rectangles 
belong to one single car and how to discriminate different cars within a convoy. With this information we should be 
able to select a few, rather generic 3D models from a database containing a limited set of vehicle models. Verification is 
carried out by projecting the selected models back to the image and matching, e.g., their wire-frame representation to the 
grayvalue gradients. A good match between the model and the image indicates that the system has selected a reasonable 
model. An additional and, in fact, strong verification for a vehicle is its shadow region. By illuminating the 3D model we 
can predict the corresponding shadow region on the road surface and search in the image for the respective features (i.e., 
dark region, corresponding edges, dark side of the edges against the sun) 
However, it should be noted that our primary goal is the extraction 
of lanes and not the detection of individual vehicles. It is sufficient 
for this purpose to know that a vehicle occludes the road, but not 
what kind of vehicle it is. 
S5 SUMMARY AND OUTLOOK 
We presented our concept for automatic road extraction in urban 
areas. It is based on a detailed model for roads, including lanes and 
road markings, and their context. By using image and DSM infor- 
mation, the extraction strategy first exploits global context knowl- 
edge, in order to reduce the inherently high complexity of urban 
scenes. Then, it focuses on image parts where initial lane and 
road hypotheses are rather easy to detect. Iteratively hypothesizing 
and verifying connections between already extracted road segments 
completes the network. So-called context relations help to analyze 
and explain abnormal changes in the expected appearance of the (D) 
road. Especially, the context relation that models vehicles on roads Figure 8: Rectangular car hypothesis 
is of great importance. The results of the currently implemented modules encourage us to further realize this concept. 
However, there are still many steps to go and there are still many questions to be answered. 
  
  
REFERENCES 
Airault, S., Ruskoné, R. and Jamet, O., 1994. Road detection from aerial images: a cooperation between local and global 
methods. In: J. Desachy (ed.), Image and Signal Processing for Remote Sensing, Proc. SPIE 2315, pp. 508-518. 
  
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B3. Amsterdam 2000. 411