CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 
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extraction of buildings (Figure 7). The area was calculated for 
each building after conversion to vector format and used as a 
threshold for separating main buildings from smaller structures 
in front or behind the main buildings. These areas also include 
trucks and vans on the roads or parked near to buildings and are 
detected as buildings. These areas were retained and classified 
later into buildings, vegetation or other objects. 
Figure 7: Extracted Buildings 
To assign each building an individual height, building centroids 
were determined. For each centroid, heights were determined 
from the DSM by bilinear interpolation and the same process 
was used for determining vegetation height. 
For the purpose of vegetation extraction, the final building data 
was subtracted from the NDSM. This resulted in vegetation 
present in the NDSM layer that is higher than 2.5m. However, 
the filtered multiple echo data (Figure 5) was also processed 
further. First, intermediate and last echoes represent reflections 
from the edges of buildings and trees. Once buildings were 
classified, building boundaries were used as an input to remove 
all multiple echo points that belong to building edges. Multiple 
reflections from large trees, together with compactness 
(area/perimeter 2 ) were used to classify large single trees and 
groves (Figure 8). 
The remaining objects to be classified were roads which are 
part of the generated DTM. NDVI data below a threshold of 
0.1 and the previously classified objects were used. Using the 
threshold eliminates most of the area having vegetation but 
does not help much in the areas with barren land. Their spectral 
signature value is also very close to the roads. Even for the 
roads the reflection value is not constant. It varies with age and 
type of material used in the road surface. Previously extracted 
buildings and vegetation were subtracted from the NDVI to 
extract road candidates. 
Gaps are present in the extracted roads and this is because of 
the trees and building shadows. This section of the road is not 
visible in aerial photos and LiDAR and needs further research 
for its successful extraction. Finally all the extracted objects 
were combined together and integrated into a specialized GIS 
system (Figures 10 and 11). 
4. CLASSIFICATION ASSESSMENT 
Results of the workflow were evaluated after the method of 
Heipke et al. (1997). Using this method, three different states 
for any feature can be identified (Hatger, 2006). 
True positive (TP) - A phenomenon that is present within the 
input data and that has successfully been identified within the 
output data. 
False positive (FP) - A phenomenon that is not present within 
the input data but that has falsely been found to be a 
phenomenon by the algorithm. Thus it is written to output data. 
False negative (FN) - A phenomenon that is present within the 
input data but that has not been identified by the algorithm and 
therefore has been omitted from the output data. 
Then we define Completeness, Correctness and Quality by 
Completeness = TP / (TP + FN) 
Correctness = TP/ (TP + FP) 
Quality = TP / (TP + FP + FN) 
Figure 8: Extracted Trees and Groves