Urban area is the main and important space of human activities with a large number of population. Compared with rural and other natural areas, the dense buildings and high-intensity land use are the most different features of urban areas. Therefore, the urban area has obvious texture in remote sensing images. Effective and accurate identification of urban area can play an important role in urban study, urban planning and other urban-related fields. In this paper, a new method based on urban and non-urban scene classification using Convolutional Neural Network (CNN) technique is developed to identify the boundary of urban areas and is applied in Beijing as an example. An acceptable result of the urban area identification was obtained, indicating a great potential of deep learning method in urban related studies.