Exploration of latent space of LOD2 GML dataset to identify similar buildings

Explainable numerical representations of otherwise complex datasets are vital as they extract relevant information, which is more convenient to analyze and study. These latent representations help identify clusters and outliers and assess the similarity between data points. The 3-D model of buildings is one dataset that possesses inherent complexity given the variety in footprint shape, distinct roof types, walls, height, and volume. Traditionally, comparing building shapes requires matching their known properties and shape metrics with each other. However, this requires obtaining a plethora of such properties to calculate similarity. In contrast, this study utilizes an autoencoder-based method to compute the shape information in a fixed-size vector form that can be compared and grouped with the help of distance metrics. This study uses "FoldingNet," a 3D autoencoder, to generate the latent representation of each building from the obtained LOD2 GML dataset of German cities and villages. The Cosine distance is calculated for each latent vector to determine the locations of similar buildings in the city. Further, a set of geospatial tools is utilized to iteratively find the geographical clusters of buildings with similar forms. The state of Brandenburg in Germany is taken as an example to test the methodology. The study introduces a novel approach to finding similar buildings and their geographical location, which can define the neighborhood's character, history, and social setting. Further, the process can be scaled to include multiple settlements where more regional insights can be made.Comment: 10 pages, 6 figure

TOPOI – Urban Rural Settlement Types – Version 1.0

Based on eleven indicators, thirteen TOPOI, here understood as settlement types of similar characteristics, were found in two exemplary study regions in Lower Saxony, Germany revea-ling new insights into the interrelation of settlement units in an urban-rural context. The data is provided as a file geodata-base (.gdb) including two components, a file geodatabase table and a file geodatabase feature class. File geodatabase feature class contains shapes of the settlement units, the table cont-ains the classification in settlement types with the correspon-ding indicator values

The “Hidden Urbanization”: Trends of Impervious Surface in Low-Density Housing Developments and Resulting Impacts on the Water Balance

Impervious surface is an important factor for the ecological performance of the built environment, in particular for the water balance. Therefore, the rainwater drainage infrastructure of new housing developments is planned according to the expected amount of impervious surface and the resulting surface runoff. Drainage infrastructure could be overwhelmed, however, due to small, dispersed, and often overlooked increases in impervious surface cover, a process we refer to as “hidden urbanization.” There is some evidence that impervious surface cover in housing areas has increased significantly over decades, but is there also a gap between planning and implementation? In order to find out, we compared eight development plans (i.e., the legally binding documents that steer building in Germany) of low-density (single-family) housing with the actual status-quo extracted from 2016 orthophotos. All sites are located in Lower Saxony, Germany; four are close to major urban centers and four are in small municipalities. We then modeled the local water balance for the plans and status-quo and compared results. All sites but one showed a relative increase between 8 and 56% of impervious surface, comparing plans with status-quo. For all sites with an increase of impervious cover, infiltration rates decreased by 4–19%, evaporation rates increased by 0.2–1% and surface runoff increased by 4–18%. In general, the more impervious surface, the stronger the effect. Our results point to a gap between planning and implementation and they underline the environmental consequences, illustrated by effects on the water balance. In order to prevent “hidden urbanization,” we suggest that more emphasis should be put on integrated design of housing areas and monitoring of impervious surface cover

Green Densities – Urban core area and associated bulding stock, population and vegetation in the urban regions of Berlin (Germany) and Qingdao (China).

Recreational green spaces are associated with human thriving and well-being. During the COVID-19 pandemic a spotlight has been shed on the importance of these spaces such as recreational green in close proximity to the place of residence. In a novel approach, we apply a multiscale analysis using different density measurements, correlations between density and green space, as well as the influence of architectural form and spatial structures to understand the accessibility of recreational green on the micro-scale of a building block. For this purpose, we use geospatial-data analysis and in-depth neighborhood studies to compare two cities in Asia and Europe revealing different ways of organizing density in the built environment and identifying a derivation of approaches for sustainable development in dense urban regions. The geodatabase includes information on building footprints, derived settlement units and core urban areas for the Berlin-Brandenburg urban region and Qingdao urban region and population grid. Vegetation has been isolated from satellite images using NDVI for both study regions, neighborhood site borders, neighborhood site buildings, neighborhood site vegetation and calculated green densities. The research was done by SpACE Lab at ISU (TU Braunschweig) in collaboration with the College of Urban Planning (CAUP) at Tongji University Shanghai (China)

Identifying Streetscape Features Using VHR Imagery and Deep Learning Applications

Deep Learning (DL) based identification and detection of elements in urban spaces through Earth Observation (EO) datasets have been widely researched and discussed. Such studies have developed state-of-the-art methods to map urban features like building footprint or roads in detail. This study delves deeper into combining multiple such studies to identify fine-grained urban features which define streetscapes. Specifically, the research focuses on employing object detection and semantic segmentation models and other computer vision methods to identify ten streetscape features such as movement corridors, roadways, sidewalks, bike paths, on-street parking, vehicles, trees, vegetation, road markings, and buildings. The training data for identifying and classifying all the elements except road markings are collected from open sources and finetuned to fit the study’s context. The training dataset is manually created and employed to delineate road markings. Apart from the model-specific evaluation on the test-set of the data, the study creates its own test dataset from the study area to analyze these models’ performance. The outputs from these models are further integrated to develop a geospatial dataset, which is additionally utilized to generate 3D views and street cross-sections for the city. The trained models and data sources are discussed in the research and are made available for urban researchers to exploit

Data-driven street segments categorization based on topological properties in urban street networks

The function-based classification (FCS) classifies streets according to their respective requirements in the pre-defined hierarchy of the urban street network (USN). However, a mismatch between the planned and actual performance can often be observed because extensive data-collection or prior local knowledge of the real performance are not always available or are often cost- and resource-consuming. This study proposes a machine learning approach for network-based categorization of street segments (NSC). Measurements derived from network science are computed for each street segment and then clustered based on their topological importance. NSC is then compared with the FCS in order to explore the fine variations in spatial-structural properties of the segments within the existing FCS scheme and to offer opportunities for better planning

Green Densities: Accessible Green Spaces in Highly Dense Urban Regions - A Comparison of Berlin and Qingdao

Recreational green spaces are associated with human thriving and well-being. During the ongoing COVID-19 pandemic a spotlight has been shed on the interconnection between access to these spaces, human well-being and social equity. Containment measures enacted in many cities effectively precluded people from reaching distant recreational areas during the pandemic and consequently, recreational areas close to home became particularly important. Urban density is often associated with building or population density with the assumption that if either parameter has a high value, the availability of open (green) space is low. Certain densities are associated with specific spatial qualities. Addressing challenges of sustainable development, a detailed evaluation of density is necessary to allow evidence-based arguments, planning and decision-making. In this study we develop a multi-scale analysis method for quantifying and assessing green infrastructures from settlement unit to building level to reach a differentiated view on density, arguing that density can be organized in different ways achieving very different qualities. For this purpose, we use geospatial-data analysis and in-depth neighborhood studies to compare two cities in Asia and Europe, revealing different ways of organizing density in the built environment and identifying a derivation of approaches for sustainable development in dense urban regions

Understanding land take in small and medium-sized cities through scenarios of shrinkage and growth using autoregressive models

Rapid transitions induced by migration flows and socio-economic developments brought about massive changes in urbanization processes and resulted in increasingly uncertain futures. The implications and complexities of the ensuing urbanization patterns are difficult to predict and project into the future. While most studies are focused on large cities and major urban centers, urbanization processes in small and medium-sized cities have garnered little scholarly and political attention. To understand future urbanization patterns, we used the TOPOI method, a novel approach for classifying territorial settlements, and spatial autoregressive models to examine contrasting futures of population growth and shrinkage in one small and one medium-sized city in Lower Saxony, Germany. Results revealed that despite planning frameworks, high population density and functional mix, respectively, were insufficient mechanisms to reduce land take. Contrary to current assumptions on the functional mix of small and medium-sized towns, our findings showed that more than half of the settlements across the study area accommodated three or more functions. Since the share of residential buildings and functional mix strongly influenced land take, further research is needed to understand their implications on sustainable urban planning. Shrinking towns in Lower Saxony continue to present multidimensional challenges and emphasize the need for transforming local planning cultures and institutional frameworks to sustainably manage and repurpose these potentially vacant areas

DataSheet1_A computational approach for categorizing street segments in urban street networks based on topological properties.pdf

Street classification is fundamental to transportation planning and design. Urban transportation planning is mostly based on function-based classification schemes (FCS), which classifies streets according to their respective requirements in the pre-defined hierarchy of the urban street network (USN). This study proposes a computational approach for a network-based categorization of street segments (NSC). The main objectives are, first, to identify and describe NSC categories, second, to examine the spatial distribution of street segments from FCS and NSC within a city, and third, to compare FCS and NSC to identify similarities and differences between the two. Centrality measures derived from network science are computed for each street segment and then clustered based on their topological importance. The adaption of clustering, which is a numerical categorization technique, potentially facilitates the integration with other analytical processes in planning and design. The quantitative description of street characteristics obtained by this method is suitable for development of new knowledge-based planning approaches. When extensive data or knowledge of the real performance of streets are not available or costly, this method provides an objective categorization from those data sets that are readily available. The method can also assign the segments that are categorized as “unclassified” in FCS to the categories in the NSC scheme. Since centrality metrics are associated with the functioning of USNs, the comparison between FCS and NSC not only contributes to the understanding and description of the fine variations in topological properties of the segments within each FCS class but also supports the identification of the mismatched segments, where reassessment and adjustment is required, for example, in terms of planning and design.</p

Building stock and associated resource consumption in three neighbourhoods of Qingdao (China) – Version 1.0

In this study, focusing on the city of Qingdao as a case study, life cycle resource use and carbon emission were investigated on the basis of typified buildings. A matrix of building carbon intensity was established, which can be used as a benchmark in design practice. It simplifies the process of estimation especially on large scale such as neighbourhood or city, and still remain the sensibility to design changes. The data contains building footprints of three neighbourhoods in Qingdao containing information on building function, “r“ or “c“ (“residential“ and “commercial“ respectively), building height, number of stories, building area, and resource usage. Based on satellite imagery by ESRI and validated through field research. A second part describes the borders for the three neighbourhoods. The data is provided as a file geodatabase (.gdb) including two components, a file geodatabase table and a file geodatabase feature class. File geodatabase feature class contains shapes of the building footprints, site borders, and data on energy usage