! 2004 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
a that 3. CONCLUSION
jyygon
mation In this study by using digital map information and aerial
photographs, the condition of the damaged buildings are
determined after earthquake. Usually the histograms which are
used as a source of image contrast in digital image processing
applications; in this study, they are used in texture analysis too.
(1) Especially, the study can supported with addition studies in the
regions that have maximum earthquake risk. The obtained
building ID's should combined with the addresses and identity
information in GIS. Thus, the damaged building addresses and
(2) the number of influenced people in that region can be known by
the algorithm.
If two GPS and high-precision gyroscope used for exterior
orientation during the fly after the earthquake, the orientation
problem can be solved real-time. Then the evaluation of the
point algorithm can be done automatically in short time.
In our country, after the earthquake especially disaster
management centers sent rescue and support teams to the
uilding earthquake region. If our developed algorithm is used five hours
un de after the earthquake, it can provide useful information for
eas are disaster management systems.
ling is
mn “the 4. REFERENCES
Ig, grey : ;
in pixel Bayram, B., Alkis Di 2001. The Coastal Line Change Detection
tandard in Gölcük Region Before and After Earthquake. The 3rd
initial National Turkey's Coastal and Sea Areas Conference,
Proceeding 1, pp. 489-495, Istanbul.
Gündüz, A., 1996. Probability, Statistic, Risk and Reliability in
Engineering, Kure, Istanbul, pp. 8-17.
Kharki, O., Sadgal, M., Ouahman A., Himdy A., Belaid M. A.
2000, Automatic Cartography From Aerial Images (Site Of
Sale, Morocco). International Archives of Photogrammetry and
Remote Sensing, Amsterdam, Vol. XXXIII, Part B2, pp. 179-
183.
Kim T., Muller, J. P., 1999. Development of a graph-based
approach for building detection, Image and Vision Computing,
vol.17, pp. 3-14.
Lammni, J., 1999. Automatic Building Extraction Using a
combination of Spatial Data and Digital Photogrammetry. Acta
Polytechnica Scandinavica, Civil Engineering and Building
Construction Series, No.117 pp. 223-230.
Letournel, V., Sankur, B., Pradeilles, F., H. Maitre, 2002.
Feature Extraction For Quality Assessment Of Aerial Image
Segmentation. Photogrammetric Computer Vision
lapped u
Bp ISPRS Commission III, Symposium pp. A-199 ff.
1 values Raafat, H. M., Gauthier, D. A., 1991. An Extended Relational
Tore dnd Database For Remotely Sensed Image Data Management
che stage Within GIS. IEEE Transactions on Geoscience And Remote
fore and Sensing, Vol. 29(4)
> ratio of Seresht, M. S., Azizi, A., 2000. Automatic Building
g are 94 Recognition From Digital Aerial Images. International Archives
of Photogrammetry and Remote Sensing, Amsterdam, Vol.
XXXIII Part B3, pp. 792-798.
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