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

Limits: Space as Resource

If space was considered a resource, then LIMITS should guide the development of cities. Space—defined as the livable surface layer of the earth— is a finite resource. The idea that nonrenewable resources must not be wasted is widely accepted. Yet, the notion of space as a resource seems underestimated in an urban and architectural sustainability discourse that largely evolved around the use of energy, material, and immaterial resources. This book supports the idea that developmental models and strategies of spatial containment (or restraint) should, and will, gain in importance, in contrast to models of outward spatial expansion, like suburbanization and sprawl, which are ultimately infringing on the landscape surrounding cities. It is important not only how we build, but also where, and most importantly where not. This is, of course, not a new idea—cities worldwide have imposed urban growth boundaries to control their outward expansion. In light of the global call for a more sustainable urban development, spatial containment strategies are on the forefront of urban discourse all over the world, independent of political systems or the state of economic development. As population rates rise and increasingly urbanize, spatial containment strategies will gain further importance. This book explores the spatial component of the complex question of sustainability. What happens if a city stops physically expanding? Is such a city with limits really more sustainable, as is so often claimed? What spatial practices or cultural innovations emerge, and what kind of urban spaces result

Growing a City for 1,000,000: Master Plan for the City between the Forest and the Ocean, Senegal

Much of the world is currently witnessing urbanization of a hitherto unprecedented pace and scale. While the phenomenon of urbanization in the African context is much discussed, less attention has been paid to what kind of built environments are being produced. In many cases, the pressure to build entire cities very rapidly from scratch has resulted in unethical developments, often by foreign contractors, resulting in places without proper provisions for basic infrastructure such as water, sanitation, or electricity, and built without involving the local communities or proper consideration of the local context. Greater Dakar, the capital of Senegal in West Africa, is facing a boom in its urban population. This project investigates strategies to “grow” a new city for up to 1 million inhabitants, based on five design principles: the City for Everyone; the City of Sustainable Mobility; the 5-Minute City; the Blue, Green and Healthy City; and the City of Distinct Identity. The central research question that is being addressed by design is how a very large city can function in a highly sustainable manner, from ecological, social and economic perspectives. This includes the question of how such new settlements can grow and mature in a controlled way, while also maintaining and developing its own distinct identity, and how a new settlement for a population of 1 million can be provided with water

Limits—Urban Density and Mobility Networks in West Berlin during the Period of Containment

If space may be conceptualized as a natural resource, much like gas, oil, or minerals, then its production and use can also be thought of as something to be properly managed, taken care of, and not wasted. Limiting the expansion of the footprint of built-up land in urban areas forces this particular resource (space) to be used more efficiently—in a sense, compelling it to be more creative and productive. These spatial constraints on urban areas generate different kinds of densification processes within the existing city, propagating densification, and with it new patterns and uses in urban development, as well as novel approaches to mitigating the hazards of dense urban environments. This paper examines the case of how spatial containment in West Berlin during the period of the Berlin Wall (1961–1989) produced such outcomes. West Berlin during this period can be considered a unique case of spatial containment, where a relatively large and vibrant modern city had to work around a clear and indelible limit to its physical expansion. This paper will discuss ways in which the containment influenced patterns of development in West Berlin toward densification and connectivity, focusing on the expansion of its infrastructural networks, and discuss the development of a new building culture around transformation and densification, including hybrid architectures and mitigation devices to deal with difficult sites produced by the densification

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