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Christian Wiedemann 
  
AUTOMATIC COMPLETION AND EVALUATION OF ROAD NETWORKS 
Christian WIEDEMANN, Heinrich EBNER 
Chair for Photogrammetry and Remote Sensing 
Technische Universität München, D-80290 Munich, Germany 
Tel: +49-89-289-22572, Fax: +49-89-2809573 
E-mail: wied|ebn@photo.verm.tu-muenchen.de 
Working Group III/3 
KEY WORDS: Networks, topology, evaluation, computer vision. 
ABSTRACT 
Road networks automatically extracted from digital imagery are in general incomplete and fragmented. Completeness 
and topology of the extracted network can be improved by the use of the global network structure which is a result of the 
function of roads as part of the transport network. This is especially — but not exclusively — important for the extraction 
of roads from imagery with low resolution (e.g., ground pixel size > 1 m) because only little local evidence for roads can 
be extracted from those images. 
In this paper, an approach is described for the completion of incompletely extracted road networks. The completion is 
done by generating link hypotheses between points on the network which are likely to be connected based on the network 
characteristics. The proposed link hypotheses are verified based on the image data. A quantitative evaluation of the 
achieved improvements is given. 
New developments presented in this paper are the generation of link hypotheses between different connected components 
of the extracted road network and the introduction of measures for the evaluation of the network topology and connectivity. 
Results of the improved completion scheme are presented and evaluated based on the introduced measures. The results 
show the feasibility of the presented completion approach as well as its limitations. Major advantages of the completion 
of road networks are the improved network topology and connectivity of the extraction result. The new measures prove 
to be very useful for the evaluation of network topology and connectivity. 
1 INTRODUCTION 
One of the major problems of image interpretation systems, in particular for road extraction, is that the results of the 
extraction of primitives are incomplete (Steger et al., 1997). In order to overcome this problem, perceptual grouping 
algorithms are used to extract meaningful entities from segmentation results. In systems for automatic road extraction, 
hypotheses for the links between different parts of the segmentation result are often generated based on geometric criteria 
like proximity and collinearity (cf., e.g., (Vasudevan et al., 1988, Ton et al., 1989, Mayer et al., 1997, Steger et al., 
1997, Baumgartner, 1998)). The hypotheses are then checked, e.g., based on the image data. Despite all these efforts, 
until now, no fully automatic approach is able to extract road networks complete and correct from imagery. But some 
approaches reach a completeness of 80% and more in open rural areas with about 95% of the extracted roads being correct 
(Wiedemann et al., 1998). 
Completeness and topology of the extracted network can be improved by the use of the global network structure which is 
a result of the function of roads (Mayer, 1998). In this paper, an approach is presented for the completion of incompletely 
extracted road networks. In general, grouping deals with the addition of new links as well as with the deletion of old 
parts. The presented approach is only able to add new links. The approach is based on the function of roads as part of the 
transport system. 
An evaluation based on quantitative measures like completeness and correctness only, does not capture the improve- 
ments of topology appropriately. Therefore, new measures for the evaluation are proposed, namely mean detour factor 
and connectivity. These measures are calculated — similar to completeness and correctness — based on the com- 
parison of the extraction result with reference data. They are intended to capture the topology of the extracted road 
network. 
In the following section a strategy for the generation of link hypotheses is proposed which makes use of the function of 
roads. Section 3 deals with image based checking of the link hypotheses. In Sect. 4, new measures for the evaluation of 
the achieved results are introduced. Results are presented, evaluated, and discussed in Sect. 5. The paper concludes with 
a summary and an outlook. 
  
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B3. Amsterdam 2000. 979