Detecting earthquake damage levels using adaptive boosting

When an earthquake happens, the image-based techniques are influential tools for detection and classification of damaged buildings. Obtaining precise and exhaustive information about the condition and state of damaged buildings after an earthquake is basis of disaster management. Today's using satellite imageries such Quickbird is becoming more significant data for disaster management. In this paper, a method for detecting and classifying of damaged buildings using satellite imageries and digital map is proposed. In this method after extracting buildings position from digital map, they are located in the pre-event and post-event images of Bam earthquake. After generating features, genetic algorithm applied for obtaining optimal features. For classification, Adaptive boosting is used and compared with neural networks. Experimental results show that total accuracy of adaptive boosting for detecting and classifying of collapsed buildings is about 84 percent.