Auclair Fortier, Marie-Flavie 
  
AUTOMATED CORRECTION AND UPDATING OF ROADS FROM AERIAL ORTHO-IMAGES 
Marie-Flavie AUCLAIR FORTIER!, Djemel ZIOU!, Costas ARMENAKIS? and Shengrui WANG! 
! Département de mathématiques ? Center for 
et informatique Topographic Information 
Université de Sherbrooke Geomatics Canada 
2500 Blv Université, Sherbrooke 615 Booth Str, Ottawa 
Québec, Canada, JIK 2R1 Ontario, Canada, K1A OE9 
auclair ? dmi.usherb.ca armenaki 9 NRCan.gc.ca 
KEY WORDS: Road Database Correction, Aerial Image Analysis, Snakes, Line Junctions, Road Following. 
ABSTRACT 
. Our work addresses the correction and update of road map data from georeferenced aerial images. To positionally correct 
the existing road network location from the imagery, we use an active contour ("snakes") optimization approach, with 
a line enhancement function. The initialization of the snakes is based on the existing vector road data coming from the 
National Topographic Database of Geomatics Canada, and from line junctions computed from the image by a new detector 
developed for this application. To generate hypothesis for new roads, a road following algorithm is applied, starting from 
the line intersections, which are already in the existing road network. Experimental results are presented to validate the 
approach and to demonstrate the interest of using line junctions in this kind of applications. 
1 INTRODUCTION 
Road detection is a time consuming operation when performed manually. Fortunately, aerial imagery tends to facilitate 
this process, but complete automatization is still far off with the methods used to date. Road detection is a type of feature 
extraction which is almost always context-dependent. In our case, high-resolution georeferenced images, that is, the 
geographic position of each pixel is known, and the existing road topographic network from the National Topographic 
Database of Geomatics Canada are used. There are two problems with this database: 1) it is not accurate enough (Figure 
2a) and 2) new roads are not included. The aim of our work is to automatically correct and update this road network. 
Much works has been done on automating road detection (Auclair-Fortier et al., 2000, Guindon, 1998, Heipke et al., 
1997). Among the methods developed, we can mention particularly the work of Klang (Klang, 1998). He developed a 
method for detecting changes between an existing road database and a satellite image. First he used the road database to 
initialize an optimization process, using a snake approach to correct road location. Then, he ran a line-following process 
using a statistical approach to detect new roads, starting from the existing network. It should be noticed that none of 
the reviewed methods make use of junctions in the image. These junctions are generally reliable information and since 
roads form networks, they are very relevant in this context. There are significant differences between our approach and 
Klang's. First, we add line junctions to his correction scheme to improve the matching between the road database and the 
image. Second, we generate hypothesis for new roads by following lines starting from line junctions near the known road 
network. We describe our approach in Section 2. In Section 3, we present line and line-junction detection. In Section 
4, we describe the correction of road location. In Section 5, we present a hypothesis generation scheme for new roads. 
In Section 6, we present results obtained from a georeferenced image and a road database, both supplied by Geomatics 
Canada. Finally, the conclusions are discussed in Section 7. 
2 APPROACH 
Geometric correction of the database is the first part in updating road maps. Because the road database has been obtained 
from various sources including scanning road maps, the location of roads is not precise. However, this information allows 
a good distinction between image features which are roads and those which are not. In aerial images, roads are generally 
long, thin features, built in networks. In the case where the resolution is not low enough to have roads as lines, the image 
is resampled to reduce its resolution. Then, we want to find lines which are near the initialization location provided by 
the road database. This prompts us to use active contour models (snakes), which are optimization curves requiring an 
initialization close to the solution. To bring this initialization closer to the solution, we match each intersection in the road 
database with at most one junction found in the image. Thus, we re-localize road segments according to the difference 
between database intersections and image junctions. After re-localization, we apply a line optimization process, using the 
  
90 International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000.