Large Housing Estates – Analysing the Morphologic Similarities and Differences of a Specific Town Planning Concept

Urban Landscapes show different urban structures. The physical face of cities is the result of complex city planning and general principles of spatial planning. And this physical face can be seen as the theater of life influencing life quality, social justice, mobility patterns, etc. In this work we focus on a specific phenomenon in post-war Germany: the town planning concept of large housing estates and their physical realizations. Same principles seem to lead to very similar urban structures and morphologies. However, over time different principles of spatial planning directions were applied for large housing states in the 1950/60s (the principle of the ‘structured and low dense city’) and the 1970/80s (the principle of ‘urbanity by density’) in Western Germany and for the entire time period until 1990 in the German Democratic Republic (the principle of the ‘socialistic city’). In this stuy we analyze whether large housing estates resulted in similar or different urban morphologies. And, whether different urban morphologies developed across variations of the specific town planning concept applied. To do so, we base our work on spatial data capturing the large housing estates in Level of Detail-1 (LoD-1) 3D building models and the street network. These geoinformation are derived from multi-sensoral Earth observation data as well as from Volunteered Geographic Information (VGI) (in our case from OpenStreetMap). For the measurements and analyses of the morphologies of large housing estates we develop and apply spatial features such as building density, floor space index, orientation of buildings, orientations of streets, among others. We reveal that different directions of the same town planning concepts for large housing estates generally create physical variabilities of the urban morphologies within a relatively small range. A closer look, however, reveals that variations do exist and that specific town planning principles had de facto influence on the resulting morphologies

The Individual Walkable Neighborhood - Evaluating people-centered spatial units focusing on urban density

Urban planners are concerned to design the city in a way that supports quality of life. To catch how the settings of elements in space influence our subjective perception is difficult to evaluate, especially since objective measures are normally calculated at arbitrary scales. To better focus on the actual surrounding of individuals, peoplecentered reference areas are needed. The current study presents a comparison of three different peoplecentered reference areas which vary in their generalization of space: the Buffer, the Convex Hull of a routing network, and the 'Individual Walkable Neighborhood'. The latter reference areas are based on the streets an individual can reach within a certain amount of time. We compare the 3D-density of these three different reference areas and of arbitrary reference areas like city blocks in a quantitative and geographical way for the city of Munich. With this we can clearly show that it is crucial to focus on such people-centered reference areas, and that even at this very small scale big differences in density values can occur. Using navigational principles, a much more lifelike and realistic representation of the subjective neighborhood can be achieved, which should provide a basis for urban practitioners when combining objective variables to the subjective perception

Spatial parameters for transportation: A multi-modal approach for modelling the urban spatial structure using deep learning and remote sensing

A significant increase in global urban population affects the efficiency of urban transportation systems. Remarkable urban growth rates are observed in developing or newly industrialized countries where researchers, planners, and authorities face scarcity of relevant official data or geo-data. In this study, we explore remote sensing and open geo-data as alternative sources to generate missing data for transportation models in urban planning and research. We propose a multi-modal approach capable of assessing three essential parameters of the urban spatial structure: buildings, land use, and intra-urban population distribution. Therefore, we first create a very high-resolution (VHR) 3D city model for estimating the building floors. Second, we add detailed land-use information retrieved from OpenStreetMap (OSM). Third, we test and evaluate five experiments to estimate population at a single building level. In our experimental set-up for the mega-city of Santiago de Chile, we find that the multi-modal approach allows generating missing data for transportation independently from official data for any area across the globe. Beyond that, we find the high-level 3D city model is the most accurate for determining population on small scales, and thus evaluate that the integration of land use is an inevitable step to obtain fine-scale intra-urban population distribution

Toward enhancement of deep learning techniques using fuzzy logic: a survey

Deep learning has emerged recently as a type of artificial intelligence (AI) and machine learning (ML), it usually imitates the human way in gaining a particular knowledge type. Deep learning is considered an essential data science element, which comprises predictive modeling and statistics. Deep learning makes the processes of collecting, interpreting, and analyzing big data easier and faster. Deep neural networks are kind of ML models, where the non-linear processing units are layered for the purpose of extracting particular features from the inputs. Actually, the training process of similar networks is very expensive and it also depends on the used optimization method, hence optimal results may not be provided. The techniques of deep learning are also vulnerable to data noise. For these reasons, fuzzy systems are used to improve the performance of deep learning algorithms, especially in combination with neural networks. Fuzzy systems are used to improve the representation accuracy of deep learning models. This survey paper reviews some of the deep learning based fuzzy logic models and techniques that were presented and proposed in the previous studies, where fuzzy logic is used to improve deep learning performance. The approaches are divided into two categories based on how both of the samples are combined. Furthermore, the models' practicality in the actual world is revealed

Investigating the Applicability of Cartosat-1 DEMs and Topographic Maps to Localize Large-Area Urban Mass concentrations

Building models are a valuable information source for urban studies and in particular for analyses of urban mass concentrations. Most commonly, LiDAR is used for their generation. The trade-off for the high geometric detail of these data is the low spatial coverage, comparably high costs and low actualization rates. Space-borne stereo data from Cartosat-1 is able to cover large areas on the one hand, but hold a lower geometric resolution on the other hand. In this paper we investigate to which extent the geometric shortcomings of Cartosat-1 can be overcome integrating building footprints from topographic maps for the derivation of large-area building models. Therefore, we describe the methodology to derive digital surface models from Cartosat-1 data and the derivation of building footprints from topographic maps at 1:25,000 (DTK-25). Both data is fused to generate building block models for four metropolitan regions in Germany with an area of ~16,000km². Building block models are further aggregated to 1 x 1 km grid cells and volume densities are computed. Volume densities are classified to various levels of urban mass concentrations (UMCs). Performance evaluation of the building block models reveals that building footprints are larger in the DTK-25 and building heights are lower with a mean absolute error of 3.21 m. Both factors influence the building volume which is linearly lower than the reference. However, this error does not affect the classification of UMC which can be classified with accuracies between 77-97 %

TOWARDS FINE SCALE CHARACTERIZATION OF GLOBAL URBAN EXTENT, CHANGE AND STRUCTURE

Urbanization is a global phenomenon with far-reaching environmental impacts. Monitoring, understanding, and modeling its trends and impacts require accurate, spatially detailed and updatable information on urban extent, change, and structure. In this dissertation, new methods have been developed to map urban extent, sub-pixel impervious surface change (ISC), and vertical structure at national to global scales. First, an innovative multi-level object-based texture classification approach was adopted to overcome spectral confusion between urban and nonurban land cover types. It was designed to be robust and computationally affordable. This method was applied to the 2010 Global Land Survey Landsat data archive to produce a global urban extent map. An initial assessment of this product yielded over 90% overall accuracy and good agreement with other global urban products for the European continent. Second, for sub-pixel ISC mapping, the uncertainty caused by seasonal and phenological variations is one of the greatest challenges. To solve this issue, I developed an iterative training and prediction (ITP) approach and used it to map the ISC of entire India between 2000 and 2010. At 95% confidence, the total ISC for India between 2000 and 2010 was estimated to be 2274.62±7.84 km2. Finally, using an object-based feature extraction approach and the synergy of Landsat and freely available elevation datasets, I produced 30m building height and volume maps for England, which for the first time characterized urban vertical structure at the scale of a country. Overall, the height RMSE was only ±1.61 m for average building height at 30m resolution. And the building volume RMSE was ±1142.3 m3. In summary, based on innovative data processing and information extraction methods, this dissertation seeks to fill in the knowledge gaps in urban science by advancing the fine scale characterization of global urban extent, change, and structure. The methods developed in this dissertation have great potentials for automated monitoring of global urbanization and have broad implications for assessing the environmental impact, disaster vulnerability, and long-term sustainability of urbanization

衛星光学センサによる数値標高モデルのノイズ低減手法

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 岩崎 晃, 東京大学教授 六川 修一, 東京大学教授 中村 尚, 東京大学准教授 沖 一雄, 東京大学准教授 矢入 健久University of Tokyo(東京大学