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Title
CMRT09
Author
Stilla, Uwe

CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation
LIDAR instead of programmetry. In many areas of the inner
city, Cologne has an extreme building density, which
complicated a clean separation of building geometry and roof
forms, even though the building outlines contained in the
ground cadastre map were examined beforehand for their
accuracy.
In addition, there were many special building structures such as
churches that had to be extracted from the airborne LiDAR
data. Pre-processing efforts were further complicated by the fact
that Cologne’s ground plan data was outdated or incomplete as
several new buildings that had been erected and still others had
been tom down since the last update made to the ground
cadastre map.
Figure 14. 3D city model of Cologne.
representation of the building heights. Because the inner city
buildings will receive realistic façade textures, highly accurate
building heights and roof structures as well as building details
were a key project requirement.
The entire model will be used a decision making tool for urban
planning and serves as a visualisation tool and complement to
Cologne’s Master Plan. The amount of overall manual post
editing required with the software has been reduced since
working on the East Berlin model to 15 percent.
4. CONCLUSION AND FUTURE WORK
We have presented an approach for the automatic
reconstruction of 3D building models from LIDAR data and
existing ground plans. It is based on an algorithm to decompose
given footprints into sets of nonintersecting cells, for which
roof shapes are then determined from the normal directions of
the LIDAR points. The validity of this approach has been
proven effective, as can be judged by the 3D city models of East
Berlin and Cologne.
The next step is to increase the amount of detail by loosening
some of the restrictions of our shapes and by making them more
flexible. This is already possible in manual editing. However, to
increase both the richness in detail and the automation, we plan
to integrate a segmentation of the roof points to selectively
decompose the footprints without generating more cells.
After a careful study of the digital ground map it was
determined that first several adjustments had to be made, for
example removing underground buildings and structures such
as parking garages and identify tom down buildings. This
required examining the discrepancies between the DTM, DSM
and building outlines to create the final 3D city model.
Finally, many larger buildings appeared in several different
attribute tables containing sometimes conflicting information,
therefore presented a challenge for both the client as well as the
operators because these buildings still needed to be
reconstructed without altering their original building footprints.
Figure 15. 3D city model of Cologne.
We have completed a wide-area 3D city model in LOD2 for the
whole of Cologne by the end of September 2008. This new
model will be integrated into the existing model, thereby
replacing the GIS data with a much more accurate
5. REFERENCES
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Detail in 3D Building Reconstruction from LIDAR Data. In:
International Archives of Photogrammetiy, Remote Sensing
and Spatial Information Sciences, Vol. XXXVIl-B3b.
Berlin 3D, 2009. 3D-Stadtmodell Berlin, http://www.3d-
stadtmodell-berlin.de (accessed 6 April 2009)
Brenner. C., 2005. Building Reconstruction from Images and
Laser Scanning. In: International Journal of Applied Earth
Observation and Geoinformation (Theme Issue ‘Data Quality
in Earth Observation Techniques), Vol. 6 (3-4), p. 187-198.
Kada, M. 2007. Scale-Dependent Simplification of 3D Building
Models Based on Cell Decomposition and Primitive Instancing.
In: Spatial Information Theory: 8 th International Conference,
COSIT 2007, pp. 222-237.
Kolbe, T.H., 2009. Representing and Exchanging 3D City
Models with CityGML. In: Lee, Zlatanova (Eds.): 3D Geo-
Information Sciences, Springer-Verlag Berlin Heidelberg, pp.
15-32.
Moser, S., Wahl, R. and Klein, R., 2009. Out-Of-Core
Topologically Constrained Simplification for City Modeling
from Digital Surface Models. In: International Archives of
Photogrammetry’, Remote Sensing and Spatial Information
Sciences, Vol. XXXVIII-5/W1.
Sohn, G., Huang, X. and Tao, V., 2008. Using a Binary Space
Partitioning Tree for Reconstructing Polyhedral Building
Models from Airborne Lidar Data. In: Photogrammetric
Engineering & Remote Sensing, Vol. 74 No. 11, pp. 1425-1438.