Paper
25 October 2012 Integrated data processing of remotely sensed and vector data for building change detection
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Abstract
In recent years natural disasters have had an increasing impact leading to tremendous economic and human losses. Remote sensing technologies are being used more often for rapid detection and visualization of changes in the affected areas, providing essential information for damage assessment, planning and coordination of recovery activities. This study presents a GIS-based approach for the detection of damaged buildings. The methodology is based on the integrated analysis of vector data containing information about the original urban layout and remotely sensed images obtained after a catastrophic event. For the classification of building integrity a new ‘Detected Part of Contour’ (DPC) feature was developed. The DPC feature defines a part of the building contour that can be detected in the related remotely sensed image. It reaches maximum value (100%) if the investigated building contour is intact. Next, several features based on the analysis of textural information of the remotely sensed image are considered. Finally, a binary classification of building conditions concludes the change detection analysis. The proposed method was applied to the 2010 earthquake in Qinghai (China). The results indicate that a GIS-based analysis can markedly improve the accuracy of change detection analysis. The proposed methodology has been developed solely within the Open Source Software environment (GRASS GIS, Python, Orange). The employment of Open Source Software provides the way for an innovative, flexible and costeffective implementation of change detection operations.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
N. Sofina, M. Ehlers, and U. Michel "Integrated data processing of remotely sensed and vector data for building change detection", Proc. SPIE 8538, Earth Resources and Environmental Remote Sensing/GIS Applications III, 85380F (25 October 2012); https://doi.org/10.1117/12.975163
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CITATIONS
Cited by 1 scholarly publication.
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KEYWORDS
Geographic information systems

Satellite imaging

Satellites

Earth observing sensors

Raster graphics

Chlorine

Feature extraction

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