EFFICIENCY OF CONTEXT-BASED ATTRIBUTES FOR LAND-USE CLASSIFICATION OF URBAN ENVIRONMENTS

Abstract. We present a study for the evaluation of the efficiency of context features in object-based land-use classification of urban environments using aerial high spatial resolution imagery and LiDAR data. Objects were defined by means of cartographic boundaries derived from the cadastral geospatial database. Objects are exhaustively described through different types of image derived features (i.e. spectral and texture), three-dimensional features computed from LiDAR data, and geometrical features describing the shape of each object. Additionally, the context of each object is described considering several aspects: adjacency, urban morphology, vegetation, and geometry. Adjacency between objects was characterized by features computed using the graph theory. Urban morphology features are related to the shape and size of neighbouring buildings, and are often related to their socio- economic function. The presence and density of vegetation are strongly related to the different urban typologies. Many of the contextual features are related to buildings, which are obtained by means of automatic building detection techniques. The meaning of the defined features, and their contribution to the classification accuracy were analyzed. The results showed that the inclusion of contextual features had a positive effect on land use classification of urban environments, increasing the overall accuracy around 4%, compared of using only the rest of features. The classification efficiency particularly increased in some classes, such as different typologies of suburban buildings, planned urban areas and historical areas.