International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
elevation of 4000 metres and provide a ground sample distance 
of 18 cm respectively. The data-preparation step was completed 
fully automatically at the DLR. 
An additional airborne three-line scanner data set was available 
depicting Seattle (USA). It was taken from the Demo data set of 
the ERDAS Imagine 8.6 package and was used for algorithm 
testing. 
In Figure 7 the results of a case study are visualized. 
  
  
Figure 7. Example Nimes-subset. (above: DSM with draped 
orthophoto, below: extracted DCM with draped orthophoto) 
7. CONCLUSION AND FUTURE WORK 
A method was presented for creating, change detection and 
updating small-scale city models. The major processing steps 
are building recognition, edge determination and corners 
computation of the building candidates. 
Further research is needed for building recognition in order to 
accelerate the existing algorithms. 
Future work will not only focus on the implementation of the 
updating module, but also on the assessment of reliability and 
quality parameters of the results. 
8. ACKNOWLEDGEMENT 
This research is supported by the OEAW — Austrian Academy 
of Sciences through a DOC scholarship. 
9. REFERENCES 
Adams, R. and Bischof, L., 1994. Seeded Region Growing. 
IEEE — Transactions on Pattern Analysis and Machine 
Intelligence, 16(6), pp. 641-647 
Brenner, C., 2000. Dreidimensionale Gebaeuderekonstruktion 
aus digitalen Oberflaechenmodellen und Grundrissen. PhD 
Thesis. Institute of Photogrammetry, Stuttgart University, 
DGK-C 530. 
Eidenbenz, C., Kaeser, C. and Baltsavias, E., 2000. ATOMI — 
Automated Reconstruction of Topographic Objects from Aerial 
Images Using Vectorized Map Information. /APRS, Vol. 
XXXIII, pp. 462-471 
Fricker, P., 2001. ADS40 — Progress in digital aerial data 
collection. Photogrammetric Week, Wichmann Verlag, 
Heidelberg, pp. 105-116 
Gonzalez, R.C., and Woods, R.E. 1992. Digital Image 
Processing. Reading, MA: Addison Wesley. 
Haala, N., Brenner, C. and Anders, K.-H., 1998. Urban GIS 
from Laser Altimeter and 2D Map Data. /4PRS, 32 (3/1), pp. 
339-346. 
Hanley, H., Yamakawa, T. and Fraser, C.. 2001." Sensor 
Orientation for High-Resolution Satellite Imagery. [APRS 
volume, commission I, wg 1/5, Denver 
Jain, A.K., 1989. Fundamentals of Digital Image Processing. 
Englewood Cliffs, NJ: Prentice-Hall. 
Knudsen, T. and Olsen, B., 2003. Automated Change Detection 
for Updates of Digital Map Databases. Photogrammetric 
Engineering & Remote Sensing, 69(11), pp. 1289-1296 
Kraus, K., 2000. Photogrammetrie Band3. Topographische 
Informationssysteme. 1* ed., Duemmler Verlag, Koeln. 
Niederoest, M., 2000. Reliable Reconstruction of Buildings for 
Digital Map Revision. /APRS, Vol. XXXIII, pp. 635-642. 
Peternell, M. and Steiner, T., 2004. Reconstruction of piecewise 
planar objects from point clouds. Computer-Aided Design, 36, 
pp. 333-342. 
Rottensteiner, F., 2003. Automatic generation of high-quality 
models from lidar data. Computer Graphics and Applications, 
IEEE, 23(6), pp. 42-50. 
Scholten, F. and Gwinner, K., 2003. Band 12 "Publikationen 
der | Deutschen Gesellschaft für | Photogrammetrie. 
Fernerkundung und Geoinformation", Bochum, pp. 419-426. 
Vosselman, G. and Dijkman, S., 2001. 3D Building Model 
Reconstruction from Point Clouds and Ground Plans. [APRS 
34 (3W4), pp. 37-43. 
Vozikis, G., Jansa, J. and Fraser, C., 2003. Alternative Sensor 
Orientation Models for High-Resolution Satellite Imagery. 
Band 12 "Publikationen der Deutschen Gesellschaft für 
Photogrammetrie, Fernerkundung und Geoinformation", 
Bochum , pp. 179- 186. 
1038 
KEY 
ABS] 
Recer 
attem, 
or tra 
neede 
image 
appro: 
appro 
consic 
were 
index 
index, 
access 
transp 
AOI ( 
bound 
be eas 
urban 
transp 
AS 
geo-sp 
satellit 
on gec 
imaget 
which 
netwou 
approa 
Miller 
networ 
the trai 
Lo. 
groups 
Is to e: 
or topc 
or opt 
most 
networ 
extract 
structu 
some « 
spatial 
tentati 
Mair 
basic « 
alpha i