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
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IEEE — Transactions on Pattern Analysis and Machine
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Fricker, P., 2001. ADS40 — Progress in digital aerial data
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Rottensteiner, F., 2003. Automatic generation of high-quality
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Scholten, F. and Gwinner, K., 2003. Band 12 "Publikationen
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Vosselman, G. and Dijkman, S., 2001. 3D Building Model
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