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Stefan Hinz
ROAD EXTRACTION IN URBAN AREAS SUPPORTED BY CONTEXT OBJECTS
Stefan Hinz, Albert Baumgartner
Technische Universitát München, D-80290 Munich, Germany
Tel: +49-89-2892-3880
Fax: +49-89-280-9573
E-mail: {hinz | albert} @photo.verm.tu-muenchen.de
KEY WORDS: Image Understanding, Road Extraction, Context
ABSTRACT
In this contribution, we introduce our concept for automatic road extraction in urban areas using high resolution aerial
imagery and dense height information. For facilitating the extraction we use a hierachical road model comprising different
road elements (markings, lanes, junctions, road network) at different scales as well as context objects of roads like build-
ings and vehicles. Global contextual knowledge about roads in residential areas helps us to focus on certain image parts,
and thus, to cope with the inherently high complexity of urban scenes. Road extraction is then performed in a mainly data
driven fashion starting with the extraction of the basic road elements at the lowest level of the road model and reaching
the top level, i.e., a topologically consistent road network, with the final steps of processing. On each step in between,
hypotheses for the respective road elements are generated and validated, and thus, evidence about roads is collected and
transfered to the next step. Since urban roads are often characterized by rather dense traffic the detection of vehicles and
vehicle convoys plays a major role. The results of the currently implemented modules give us rise to further realize this
concept.
1 INTRODUCTION
In the past, the automation of road extraction from digital imagery has received considerable attention. Research on this
issue is mainly motivated by the increasing importance of geographic information systems (GIS) and the need for data
acquisition and update for GIS. Especially in the field of urban planning, there is a high demand for actual, accurate
and detailed information about the road network as required for applications like traffic flow analysis and simulation,
estimation of air and noise pollution, street maintenance, etc.. The OEEPE Survey on 3D City Models by the European
Organization for Experimental Photogrammetric Research (OEEPE) confirmed this demand yielding about 85% of the
participants mentioning that information about the road network is of their greatest interest (see (Fuchs et al., 1998)).
For a number of cities in Europe and Northern America, larger parts of the road network are already digitally mapped,
most important in commercial and military navigation and route planning systems. In practice, however, the level of detail
of the data acquisition, i.e., which elements of a road are thought to be important and which are negligible, is (naturally)
influenced by the application for which the data is collected. Since, for navigation purposes, the topology of the road
network is on principle more important than the number of lanes (except traffic-dependent route planning), most existing
area-wide road databases provide road information in form of the road axis attached with the road class or the road width.
They thus lack of the proper level of detail mandatory for the above-mentioned applications (though, in many cases, the
underlying data models would allow for a more detailed road description). Since a high percentage of the relevant urban
features, including roads and their sub-structures, i.e., lanes and road markings, can be extracted from aerial images with
high accuracy (Englisch and Heipke, 1998), the interpretation of aerial images is despite of existing GIS data a crucial
task for the acquisition of detailed road data. E.g., (Bogenberger et al., 1999) use aerial images in order to count lanes
and vehicles, measure queue lengths, and estimate vehicle velocities for calculating and simulating traffic flow conditions
on high capacity roads. The time- and cost intensive manual procedure necessary for extracting the desired information
constitutes the main bottleneck, which needs to be overcome.
In this paper, we present our concept for the automatic extraction of roads and their sub-structures from aerial imagery
taken over urban areas. Besides multiple overlapping images, the approach relies on accurate height information as, e.g.,
derived from airborne laser data. The approach is designed to include additional information sources, without depending
on it. Such sources are most important color channels, external information provided by GIS and results of semi- or
fully-automatic building extraction. As in our previous work (Baumgartner et al., 1999, Baumgartner et al., 1997), we
distinguish three "global contexts" called context regions: rural, forest, and urban. For road extraction in urban areas we
use a model that describes roads and their sub-structures at different scales for different sensors. The extraction starts
with the segmentation of Regions of Interest (Rol) based on height and image data at coarse scale. Then, the basic road
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B3. Amsterdam 2000. 405
