Rising complexities and uncertainties have emphasized the need to employ scenario thinking in urban design and planning. While different scenario methods have been widely used across disciplines, a comprehensive review of scenario approaches in planning literature is limited. Thus, we provide an overview of scenarios and existing scenario approaches currently in practice. We also review a scenario building process to provide a guide for developing scenarios in the context of urban design and planning. The process highlights the different steps that contribute to adaptive planning and improve decision-making.

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.

Many years, urbanisation research has largely focused the development of urban agglomerations and megacity regions, whereas less attention was paid on the development of medium-sized cities, small towns, villages, or rural areas. Yet many interrelations and spatial linkages between urban and rural areas exist. In this paper, we present a novel method called ‘TOPOI’ for the integrated analysis and description of settlement units in an urban–rural setting. The TOPOI-method enhances the understanding of the built environment by clustering and describing settlement units of similar characteristics with view to their physical form, function, and connectivity. The method is built on known planning parameters, but does not limit the analysis of settlement units to their administrative boundaries. Based on 11 indicators, 13 TOPOI-classes were identified in two exemplary study regions revealing new insights into urban–rural settlement types. This allows a better understanding of urban–rural linkages and therefore opens up new pathways for a more sustainable development.

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.

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.