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Automatic detection of damaged buildings from aerial and satellite images is an important problem for rescue planners and military personnel. In this study, we present a novel approach for automatic detection of damaged buildings in color aerial images. Our method is based on color invariants for building rooftop segmentation. Then, we benefit from grayscale histogram to extract shadow segments. After building verification using shadow information, we define a new damage measure for each building. Experimentally, we show that using our damage measure it is possible to discriminate nearby damaged and undamaged buildings. We present our experimental results on aerial images.