In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 
13 
AUTOMATIC EXTRACTION OF URBAN OBJECTS FROM MULTI-SOURCE AERIAL 
DATA 
Adriano Mancini, Emanuele Frontoni and Primo Zingaretti 
Dipartimento di Ingegneria Informatica Gestionale e dell’Automazione 
Università Politecnica delle Marche 
Ancona, ITALY 
{mancini.frontoni,zinga}@diiga.univpm.it 
KEY WORDS: LiDAR, buildings, road extraction, automated classification, city models 
ABSTRACT: 
Today, one of the main applications of multi-source aerial data is the city modelling. The capability to automatically detect objects of 
interest starting from LiDAR and multi-spectral data is a complex and an open problem. The information obtained can be also used for 
city planning, change detection, road graph update, land cover/use. In this paper we present an automatic approach to object extraction 
in urban area; the proposed approach is based on different sequential stages. The first stage basically solves a multi-class supervised 
pixel based classification problem (building, grass, land and tree) using a boosting algorithm; after classification, the next step provides 
to extract and filter land areas from classified data; the last step extracts roundabouts by the Hough transform and linear roads by a novel 
approach, which is robust to noise (sparse pixels); the final representation of extracted roads is a graph where each node represents a 
cross between two or more roads. Results on a real dataset of Mannheim area (Germany) using both LiDAR (first - last pulses) and 
multi-spectral high resolution data (Red - Green - Blue - Near Infrared) are presented. 
1 INTRODUCTION 
T ODAY the availability of high spatial resolution LiDAR and 
multi-spectral data collected by aerial vehicles (manned or 
unmanned) traces new ways for the possible applications. City 
modeling, object extraction (e.g., buildings, roads, bridges, ...), 
urban growth analysis, land use/cover, developing 3D models, 
are the main studied applications. Usually the analysis of data is 
made by a human operator; traditional photo-interpretati on is a 
slow and expensive process that requires specialized experts; ac 
curacies similar to those of man-made maps can now be reached 
by automatic object extraction and classification approaches, but 
with considerably less wasted time and money, thus allowing high 
update rates. 
The ability to automatically classify data starting from a set of 
heterogeneous features is fundamental to design an automatic ap 
proach. One of the first method used to classify LiDAR data was 
the height threshold to a normalized DSM (nDSM) (Weidner and 
Forstner, 1995); using this method it is possible to extract objects 
as buildings, but its has a lot of well-known drawbacks: high- 
density canopy can be classified as building and it is not possible 
to distinguish low height objects as lands or roads. Multi-spectral 
data allow to extend the set of classified objects producing higher 
accuracy. Many machine learning approaches were adopted to 
solve the problem of object extraction from multi-source data; 
Bayesian maximum likelihood method (Walter, 2004), Dempster- 
Shafer (Lu et al., 2006), boosting using AdaBoost (Frontoni et ah, 
2008). 
Common objects as buildings or roads are the main interesting 
features that can be extracted from the classified data; road ex 
traction is a classical problem of remote sensing, but not com 
pletely solved. A really interesting overview (updated to 2003) 
can be found here (Mena, 2003). Using only multi-spectral data 
(Bacher and Mayer, 2005), road extraction is an extremely diffi 
cult task especially in urban area also using high-resolution im 
agery as IKONOS or SPOT. Problems as occlusion (due to the 
presence of trees), noise inducted by vehicles or object shadows, 
influence the quality of road extraction; moreover, spectral sepa 
rability of road respects to other objects (e.g. bituminous roofs) 
is not always guaranteed. Snakes/active contours are classical 
methodological tools; different version of standard snake (Kass et 
ah, 1987) were developed to solve the problem of road extraction 
especially in not urban area (Marikhu et ah, 2006). Moreover this 
approach requires a wide set of good seed points, which are often 
user defined. The fusion of LiDAR and multi-spectral data is a 
powerful tool for road extraction; LiDAR helps to distinguish be 
tween high objects as buildings or canopies, while multi-spectral 
data allow to distinguish between land/road and grass or other 
low profile objects (Clode et ah, 2005). SAR imagery can be 
also useful for road extraction with results comparable with Li 
DAR (Guo et ah, 2007). However the goodness of LiDAR and 
multi-spectral data fusion approaches allows to obtain interesting 
results in building / road extraction. 
In this paper, a classification approach, using boosting classifier 
to fuse LiDAR and multi-spectral data, is presented. The Ada 
Boost technique with CART classifier as weak learner, classifies 
data distinguishing among four classes: building, grass, land and 
tree; the ReliefF (Liu and Motoda, 2008) feature selection algo 
rithm allows to consider only meaningful features to minimize the 
misclassification. The result of classification stage is then used to 
extract buildings, roads and roundabouts; the approach here pro 
posed extracts and clusters a set of linear roads using a pyramidal 
representation to reduce time and memory usage. The procedure 
is totally automatic and requires only a minimum interaction with 
user; a user-defined training set is necessary to train the classifier 
and control the learning accuracy; the training set often can be di 
rectly accessible by a web-GIS or a photo-interpretation process 
over a very small portion of global area; we use a training set that 
covers less than 0.5% of total area. 
The paper is organized as follows. Section 2 introduces the method 
ology for classification and object extraction; Section 3 explains 
the data set used for experiments, the adopted classifer and the 
classification results on a four class problem. Section 4 presents 
the method and obtained results in road extraction; in Section 5 
conclusions and future work are outlined.