Building Change Detection Using High Resolution Remotely Sensed Data and GIS

Remote sensing technology is increasingly being used for rapid detection and visualization of changes caused by catastrophic events. This paper presents a semi-automated feature-based approach to the identification of building conditions especially in affected areas using geographic information systems (GIS) and remote sensing information. For image analysis, a new “detected part of contour” (DPC) feature is developed for the assessment of building integrity. The DPC calculates a part of the building contour that can be detected in the remotely sensed image. Additional texture features provide information about the area inside the buildings. The effectiveness of the proposed method is proved by high overall classification accuracy for two different study cases. The results demonstrate that the “map-to-image” strategy enables extracting valuable information from the remotely sensed image for each individual vector object, thereby being a better choice for change detection within urban areas.