Automated Road Extraction and Updating Using
the Atomi System - Performance Comparison
Between Aerial Film, ADS40, IKONOS and
Quickbird Orthoimagery
Chunsun Zhang a , Emmanual Baltsavias b
a Department of Geomatics, The University of Melbourne,
VIC 3010, Australia –
E-mail: chunsun@unilb.edu.au
b Institute of Geodesy and Photogrammetry,
Swiss Federal Institute of Technology (ETH)
Zurich, ETH-Hoenggerberg, CH-8093 Zurich, Switzerland
E-mail: manos@geod.baug.ethz.ch
ABSTRACT Automated road extraction from digital images has drawn considerable attention
due to the need for the efficient acquisition and updating of road data for geodatabases. The
development of new digital aerial sensors and high-resolution satellite sensors signifies a
revolutionary change in image acquisition and the possibility of fully digital processing from
image acquisition to the generation of value-added products for various applications. At ETH
Zurich, we have developed an operational system for the automated extraction of 3D road
networks from imagery that integrates the processing of colour image data and existing digital
spatial databases. The system has been extensively tested on areas with diverse terrain relief and
landcover types using different resolution stereo and orthoimages with good results. Recently,
tests have been performed using ADS40, IKONOS and Quickbird data. This paper reports on
the performance comparison of the ATOMI system using different sensor data in two varying
test sites. Visual analysis and quantitative measures of accuracy, correctness and completeness
are presented, with typical completeness and correctness values of over 90% and planimetric
accuracy of 0.4 m to 1 m. The advantages and disadvantages using different sensor data for road
network updating are also discussed.
1. INTRODUCTION
In modern map production, a shift has taken place from maps stored in analogue form on paper
or film to a digital database containing topographic information. Recently, National Mapping
Agencies, especially in Europe, wish to generate digital landscape/topographic models that
conform to reality and do not include map generation effects. In addition, various existing and
emerging applications require up-to-date, accurate and sufficiently attributed digital data,
especially of roads and buildings. To cope with higher product demands, increase the
productivity and cut cost and time requirements, automation tools in the production should be
employed. As aerial images are a major source of primary data, it is obvious that automated
aerial image analysis can lead to significant benefits. In addition, the development of new digital
aerial sensors and high-resolution satellite (HRS) sensors signifies a revolutionary change in
image acquisition and the possibility of fully digital processing from image acquisition to the
generation of value-added products for various applications. At ETH Zurich, in cooperation with
the Swiss Federal Office of Topography (swisstopo), we have developed a practical system for
the automatic extraction of 3D road networks from imagery that integrates processing of colour
images and existing digital spatial databases, within the project ATOMI (Baltsavias and Zhang,
2003; Zhang, 2003b). This paper reports on the performance of the ATOMI system using
extensive areas with varying relief and landcover and images from different sensors.
