AdvisorsMitra, Niloy J.
Permanent link to this recordhttp://hdl.handle.net/10754/611332
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AbstractDigitization and characterization of urban spaces are essential components as we move to an ever-growing ’always connected’ world. Accurate analysis of such digital urban spaces has become more important as we continue to get spatial and social context-aware feedback and recommendations in our daily activities. Modeling and reconstruction of urban environments have thus gained unprecedented importance in the last few years. Such analysis typically spans multiple disciplines, such as computer graphics, and computer vision as well as architecture, geoscience, and remote sensing. Reconstructing an urban environment usually requires an entire pipeline consisting of different tasks. In such a pipeline, data analysis plays a strong role in acquiring meaningful insights from the raw data. This dissertation primarily focuses on the analysis of various forms of urban data and proposes a set of techniques to extract useful information, which is then used for different applications. The first part of this dissertation presents a semi-automatic framework to analyze facade images to recover individual windows along with their functional configurations such as open or (partially) closed states. The main advantage of recovering both the repetition patterns of windows and their individual deformation parameters is to produce a factored facade representation. Such a factored representation enables a range of applications including interactive facade images, improved multi-view stereo reconstruction, facade-level change detection, and novel image editing possibilities. The second part of this dissertation demonstrates the importance of a layout configuration on its performance. As a specific application scenario, I investigate the interior layout of warehouses wherein the goal is to assign items to their storage locations while reducing flow congestion and enhancing the speed of order picking processes. The third part of the dissertation proposes a method to classify cities based on their functional behavior. Commonly used computational approaches concentrate on geometric descriptors, for both images and laser scans. Instead, I analyze street networks, both their topology (i.e., connectivity) and geometry (i.e., layout), in an attempt to understand the factors that play dominant roles in determining the characteristic of cities. A set of street network descriptors is proposed to capture the essence of city layouts and used, in a supervised setting, to classify and categorize various cities across the world. Each part of the dissertation shows the utility of the proposed methods through describing a variety of applications on different examples.
CitationAlHalawani, S. (2016). Techniques and Applications of Urban Data Analysis. KAUST Research Repository. https://doi.org/10.25781/KAUST-1G64B