CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 
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2 METHODOLOGY 
Building and road extraction, as mentioned above, require com 
plex elaborations of multi-source data; we followed a multi-step 
procedure. The procedure here proposed consists of four sequen 
tial steps; the output of each module is the input for the following. 
Step 1 - Feature generation. It calculates LiDAR and radio- 
metric additional features for the classification stage; a total 
of seven mixed-features are currently adopted. 
Step 2 - Classification. Using AdaBoost with a tree classifer 
as weak learner, it distinguishes among four main classes; a 
simple training set is adopted to train the classifer. 
Step 3 - Object Extraction. It extracts buildings and/or roads 
from the classified data; in this paper we focus on road ex 
traction and pre-filtering techniques; 
Step 4 - Clustering. It is fundamental to model the extracted 
objects. 
A graphical representation of discussed methodology is shown in 
Fig-1- 
Figure 1: Methodology. The object extraction procedure has a hi 
erarchical structure that simplifies the phase of result evaluation; 
different approaches can be easily tested without compromising 
the overall methodology 
In the following sections, the results of each stage are presented; 
for completeness a deep results evaluation of building extraction 
is reported to evidence the quality of classification process; stan 
dard metrics are used to make in evidence the performance of 
AdaBoost classifier. 
3 CLASSIFICATION 
3.1 Dataset 
The methodology presented in previous section, was validated in 
an urban area: LiDAR and multi-spectral data refer to the centre 
of the German city of Mannheim. This area is characterized with 
large buildings, mostly attached forming building blocks of dif 
ferent heights, many cars and little vegetation. Mannheim dataset 
has a resolution of 0.25m for the images and 0.5m for the range 
data; the total grid dimension is 1808 x 1452 (width x height). 
The aerial images are orthorectified and four spectral bands are 
available: Red, Green, Blue, and Near InfraRed; laser range data 
consist of first and last pulse recordings acquired by an airborne 
laser scanner. Additional features were added to expand the fea 
ture space; main motivation is that using a feature weighting al 
gorithm, is easy to find the best feature combination. Normal 
ized Difference Vegetation Index (NDVI) and Green Normalized 
Difference Vegetation Index (GNDVI) were calculated. These 
indexes are useful to distinguish between some critical classes 
which LiDAR data cannot easily distinguish. Two pairs are criti 
cal: building/tree and land/grass. NDVI is a compact index which 
allows to better discriminate inside each cited pair. It is well 
known that canopies and grass have a NDVI value usually greater 
than 0.15, while for building and land classes is usually around or 
below zero. As introduced in the previous sections, we identified 
four main classes; for each class, we selected eight representative 
polygons. The total area of training set is below the 0.5%; it is 
useful to remark that the selection of these polygons is a low-time 
consuming activity that can be easily performed using a web-GIS 
or photo-interpretation (easy owing to the reduced number and 
kind of classes). The training set and a 3D view of the input data 
set are shown in Figures 2 and 3. 
Figure 2: Data and Training set. Red stands for building, yellow 
for land, blue for grass and green for tree 
Figure 3: A 3D view of dataset; height of objects are obtained 
using the first pulse laser range data 
The selected features used for classification are: 
LiDAR: Ah is the height difference between the last pulse DSM 
and the DTM and Ap is the height difference between the 
first pulse and the last pulse DSM 
Spectrals: R,G,B,NIR and NDVI (GNDVI is omitted because 
the weight associated to this feature was low)