Full text: CMRT09

ata with 
lham at 
an from 
ings of 
. Clas- 
, Mon- 
. Data 
rigs and 
ry and 
S. and 
tic ap- 
a. Pro- 
lm and 
n Arti- 
>f Fea- 
ng de 
on for 
ars 24, 
Cooperative Research Center for Spatial Information, Department of Geomatics 
University of Melbourne VIC 3010, Australia 
[m.ravanbakhsh, c.fraser]@unimelb.edu.au 
KEY WORDS: roundabout detection, feature extraction, topographic database, high resolution imagery, snake model, level sets 
Road roundabouts, as a class of road junctions, are generally not explicitly modelled in existing road extraction approaches. This 
paper presents a new approach for the automatic extraction of roundabouts from aerial imagery through the use of prior knowledge 
from an existing topographic database. The proposed snake-based approach makes use of ziplock snakes. The external force of the 
ziplock snake, which is a combination of the Gradient Vector Flow force and the Balloon force, is modified based on the shape of 
the roundabout central island to enable the roundabout border to be delineated. Fixed boundary conditions for the proposed snake are 
provided by the existing road arms. A level set framework employing a hybrid evolution strategy is then exploited to extract the 
central island. Black-and-white aerial images of 0.1 m ground resolution taken over suburban and rural areas have been used in 
experimental tests which have demonstrated the validity of the proposed approach. 
The need for accurate spatial databases and their automatic 
updating is increasing rapidly. Road networks form key 
information layers in topographic databases since they are used 
in such a wide variety of applications. As the extraction of 
roads from images is still generally manual, costly and time- 
consuming, there is a growing imperative to automate the 
process. However, such a feature extraction task has longed 
proved difficult to automate. The problem for automatic road 
extraction lies mostly in the complex content of aerial images. 
To ease the complexity of the image interpretation task, prior 
information can be used (Gerke, 2006; Boichis et al., 2000; 
Boichis et al., 1998; De Gunst, 1996). This often includes the 
provision of data from an external topographic database. 
Roundabouts, as a class of road junctions, are important 
components of a road network and if modelled well can 
improve the quality of road network extraction (Boichis et al., 
1998). However, there are only few approaches which are 
dedicated to this task. Boichis et al. (2000) presented a 
knowledge based system for the extraction of road junctions 
and roundabouts. The method assumed that the description of 
simple road junctions and roundabouts is the same in the 
external database, so a previous detector has to certify the 
presence of the circular form. A parametric Hough Transform is 
used for this purpose. The roundabout is reconstructed after 
straight parts of the connecting roads, curved parts including 
splitter islands, and the circulating road are extracted. 
These elements are connected using geometric and radiometric 
continuities. In the approach, roads are treated as linear objects. 
Thus, elements such as the central island and the roundabout 
outline are not extracted, so kind of modelling does not always 
reflect the required degree of detail. In Fig. 1, vector data is 
superimposed on sample images to illustrate the problem. The 
image resolution is such that the roundabout’s central area 
covers the central island and the circulating roadway. In Fig. 
lb, the roundabout is represented as point object neglecting the 
central island and the circulating roadway. Thus, a detailed 
modelling of roundabouts is needed for data acquisition 
purposes at large scales. 
The detailed modelling of road roundabouts area objects is 
discussed in this paper, and an approach for their automatic 
extraction is proposed. This uses an existing topographic 
database leading to the extraction of refined roundabout data. In 
the following section, a model for roundabouts is first 
introduced. The stages of the proposed strategy are then 
illustrated in Sect. 3. Results from the implementation of the 
proposed approach using aerial imagery of 0.1 m ground 
resolution are presented and discussed in Sect. 4, together with 
an evaluation of their quality. Finally concluding remarks are 
Figure 1. Superimposition of vector data on high resolution 
aerial images of road roundabouts. 
Illustrated in Fig. 2a is the conceptual two-part model of a 
roundabout, the parts being the roundabout itself and the road 
arms. The roundabout, where road arms are connected, is in 
turn composed of the roundabout border and its central area 
where a central island is located. A road arm is a rectilinear 
object which is represented as a ribbon with a constant width 
and two parallel road edges. Disturbances such as occlusions 
and shadows are not explicitly included in the model.

Note to user

Dear user,

In response to current developments in the web technology used by the Goobi viewer, the software no longer supports your browser.

Please use one of the following browsers to display this page correctly.

Thank you.