Revealing the context of a scene from low-level features representation , is a challenging task for quite a long time. The classification of landscapes scenes to urban and rural categories is a preliminary task for landscapes scenes understanding. Having a global idea about the scene context (rural or urban) before investigating its details, would be an interesting way to predict the content of that scene. In this paper, we propose a novel features representation based on skyline, colour and texture, transformed by a sparse coding using Stacked Auto-Encoder. To evaluate our proposed approach; we construct a new database called SKYLINEScene Database containing 2000 images of rural and urban landscapes with a high degree of diversity. Many experiments were carried out using this database. Our approach shows it robustness in landscapes scenes classification.