An approach for detection of buildings and changes in buildings using orthophotos and point clouds: A case study of Van Ercis earthquake

SARP, Gülcan
Duzgun, Sebnem
Sahin, Kemal
This paper presents an image analysis of the Van Ercis earthquake, and demonstrates how efficiently the orthophoto images and point clouds from stereo matching data can be used for automatic detection of buildings and changes in buildings. The proposed method contains three basic steps. The first step is to classify the high-resolution pre and post event Red-Green-Blue (RGB) orthophoto images (orthoRGB) using Support Vector Machine (SVM) classification procedure to extract the building areas. In the second step, normalized Digital Surface Model (nDSM) band derived from point clouds and Digital Terrain Model (DTM) is integrated with the SVM classification (nDSM+orthoRGB). In the last step, building damage assessment is performed through a comparison between two independent classification results from pre- and post-event data. It was observed that using the nDSM band in the classification process as additional bands the accuracy of classification increases significantly.


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Citation Formats
G. SARP, A. ERENER, S. Duzgun, and K. Sahin, “An approach for detection of buildings and changes in buildings using orthophotos and point clouds: A case study of Van Ercis earthquake,” EUROPEAN JOURNAL OF REMOTE SENSING, pp. 627–642, 2014, Accessed: 00, 2020. [Online]. Available: