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With complex building composition and imaging condition, urban areas show versatile characteristics in remote sensing optical images. It demonstrates that multiple features should be utilized to characterize urban areas. On the other hand, since levels of development in neighboring areas are not statistically independent, the features of each urban area site depend on those of neighboring sites. In this paper, we present a multiple conditional random fields (CRFs) ensemble model to incorporate multiple features and learn their contextual information. This model involves two aspects: one is to use a CRF as the base classifier to automatically generate a set of CRFs by changing input features, and the other is to integrate the set of CRFs by defining a conditional distribution. The model has some distinct merits: each CRF component models a kind of feature, so that the ensemble model can learn different aspects of training data. Moreover, it lets the ensemble model search in a wide solution space. The ensemble model can also avoid the well-known overfitting problem of a single CRF, i.e., the many features may cause the redundancy of irrelevant information and result in counter-effect. Experiments on a wide range of images show that our ensemble model produces higher detection accuracy than single CRF and is also competitive with recent results in urban area detection.