Built-up area detection is very important for applications such as urban planning, urban growth detection and land use monitoring. In this paper, we address the problem of buil-tup area detection from the perspective of visual saliency computation. Generally, areas containing buildings attract more attentions than forests, lands and other backgrounds. This paper explores a Bayesian saliency model to automatically detect urban areas. Firstly, prior probability is computed by using fast multi-scale edge distribution. Then the likelihood is obtained by modeling the distributions of color and orientation. Built-up areas are further detected by segmenting the final saliency map using Graph Cut algorithm. Experimental results demonstrate that the proposed method can extract built-up area efficiently and accurately.