Multi-resolution region-based clustering for urban analysis

Abstract

In the domain of urban planning and management, it may be necessary to map the territory at different scales, each corresponding to a semantic level. Three semantic levels are identified: (1) the object level, for mapping urban elements (buildings, etc.), (2) the block level, for mapping homogeneous patterns of urban elements, and (3) the area level, for mapping urban fabrics defined as sets of homogeneous patterns. Some of these levels are directly linked to specific satellite images presenting ad hoc resolutions (namely, medium spatial resolution (MSR) images for the area level and high spatial resolution (HSR) images for the object level); in such cases, a straightforward mapping can be performed by clustering the data. Conversely, classical clustering techniques do not enable the intermediate semantic level to be extracted directly. The purpose of this article is to propose a methodology enabling a clustering at this level to be generated. The proposed approach is, in particular, based on the segmentation and unsupervised, region-based and joined clustering of two images representing a same scene at MSR and HSR. The method has been applied to different and heterogeneous datasets composed of HSR images at 2.5 m and MSR images at 10 m and 20 m. Qualitative validations by an expert, and quantitative ones by comparison to other existing methods, tend to emphasize the soundness and efficiency of this methodology, thus justifying further developments.