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What we understand by the simple term "city" is in fact describing highly diverse domains: different economies, demographics, ways of living, land uses, built-up morphologies, among other things. The built landscape alone ranges from low-density, one-storey suburban settlements to high-density accumulations of skyscrapers. Models have repeatedly attempted to describe these various 'city' manifestations and to understand the processes that shape these spatial appearances and patterns. In this paper we analyze the morphological-spatial configurations of urban landscapes. We empirically examine 110 cities distributed around the globe. By using the Local Climate Zones (LCZs) classification scheme, we quantitatively describe morphologic variances of the built landscape within cities. We find seven city types (clusters) that capture the global diversity of spatial urban configurations. These seven types testify in parts to common geographic-cultural spaces. Some are largely congruent with wellknown spatial units such as Europe or the Islamic world. In contrast to theoretical city models, however, we also find clusters that are more spatially complex such as African-American or Asian-African clusters. On the one hand, the study confirms that similar cultural, socio-economic, demographic or political conditions in fact do produce similar morphologic-spatial urban configurations. On the other hand, it also shows that there exist similar morphological configurations across geographic-cultural spaces.

Publisher's version (útgefin grein) ; So far, a large number of advanced techniques have been developed to enhance and extract the spatially semantic information in hyperspectral image processing and analysis. However, locally semantic change, such as scene composition, relative position between objects, spectral variability caused by illumination, atmospheric effects, and material mixture, has been less frequently investigated in modeling spatial information. Consequently, identifying the same materials from spatially different scenes or positions can be difficult. In this article, we propose a solution to address this issue by locally extracting invariant features from hyperspectral imagery (HSI) in both spatial and frequency domains, using a method called invariant attribute profiles (IAPs). IAPs extract the spatial invariant features by exploiting isotropic filter banks or convolutional kernels on HSI and spatial aggregation techniques (e.g., superpixel segmentation) in the Cartesian coordinate system. Furthermore, they model invariant behaviors (e.g., shift, rotation) by the means of a continuous histogram of oriented gradients constructed in a Fourier polar coordinate. This yields a combinatorial representation of spatial-frequency invariant features with application to HSI classification. Extensive experiments conducted on three promising hyperspectral data sets (Houston2013 and Houston2018) to demonstrate the superiority and effectiveness of the proposed IAP method in comparison with several state-of-the-art profile-related techniques. ; This work was supported in part by the German Research Foundation (DFG) under Grant ZH 498/7-2, in part by the Helmholtz Association through the Framework of the Young Investigators Group SiPEO under Grant VH-NG-1018, and in part by the European Research Council (ERC) through the European Union's Horizon 2020 Research and Innovation Programme (Acronym: So2Sat) under Grant ERC-2016-StG-714087. The work of Naoto Yokoya was supported by the Japan Society for the Promotion of Science under Grant KAKENHI 18K18067. (Corresponding author: Xiao Xiang Zhu.) Danfeng Hong and Xiao Xiang Zhu are with the Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Weßling, Germany, and also with the Signal Processing in Earth Observation (SiPEO), Technical University of Munich (TUM), 80333 Munich, Germany (e-mail: danfeng.hong@dlr.de; xiaoxiang.zhu@dlr.de). ; Peer Reviewed