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Climate change can contribute to the increase in the prevalence of cardiovascular (CV) diseases, the leading cause of global deaths. Accordingly, many big cities are interested in increasing their residents’ resilience against heat waves. With these issues in mind, the aim of our study was to identify salient features of urban areas and to analyze the effect of such features on population vulnerability to heat (VtoH) as it relates to CV health. This approach was developed and tested in the city of Milan, Italy, considering the summer periods (May–September) of 2017–2022. Milan was divided into 86 districts and 11 features were considered. K-means was applied for clustering, and both spatial and non-spatial regression were used to study the VtoH, defined as the percentage of CV emergencies on a given heat day and on the day after, compared to the total number of CV emergencies. Socio-urban features were spatially non-stationary and three different clusters of districts were identified. In the whole city, regression analysis depicted a spatial relationship between the focal features and the VtoH, with the model estimating a significant effect in five variables: mean summer temperature, density of drinking water fountains and percentages of elderly, female and graduate residents. Three additional features were found to be significant in only some of the cities’ clusters. Our spatial analysis of CV health emergencies applied to the entire geographical area, rather than at the patient level, represents a relatively underexplored approach in public health-related research. The results of our study and future research taking this approach can inform solutions to equitably protect cities’ residents, which is important in the context of ongoing urbanization and climate change. [ABSTRACT FROM AUTHOR]

The vitality of a city is shaped by its social structure, environmental quality, and spatial form, with child-friendliness being an essential component of urban vitality. While there are numerous qualitative studies on the relationship between child-friendliness and various indicators of urban vitality, quantitative research remains relatively scarce, leading to a lack of sufficient objective and trustworthy data to guide urban planning and the development of child-friendly cities. This paper presents an analytical framework, using Heping District in Tianjin, China, as a case study. It defines four main indicators—social vitality, environmental vitality, spatial vitality, and urban scene perception—for a trustworthy and transparent quantitative evaluation. The study integrates multi-source data, including primary education (POI) data, street view image (SVI) data, spatiotemporal big data, normalized difference vegetation index (NDVI), and large visual language models (LVLMs) for the trustworthy analysis. These data are visualized using corresponding big data and weighted analysis methods, ensuring transparent and accurate assessments of the child-friendliness of urban blocks. This research introduces an innovative and trustworthy method for evaluating the child-friendliness of urban blocks, addressing gaps in the quantitative theory of child-friendliness in urban planning. It also provides a practical and reliable tool for urban planners, offering a solid theoretical foundation to create environments that better meet the needs of children in a trustworthy manner. [ABSTRACT FROM AUTHOR]

This article examines the potential of fuzzy set theory for analysing gradual changes in land use patterns within peri-urban areas. The primary objective of the study was to propose a methodology based on fuzzy set theory for the precise delineation of city boundaries and the identification and spatial localisation of the urban–rural transition zone. The analysis focused on elucidating the defining parameters of this area and the scope of land use changes within the urban–rural transition zone. The analysis employed data from four discrete time points. The data were collected in 2005, 2010, 2017, and 2022. The characteristics of the urban–rural transition zone were evaluated through an examination of historical data and the current land use patterns in regions experiencing direct urbanization pressure. The study demonstrated that, although spatial barriers remain, the city's development has continued at a consistent pace. Between 2005 and 2010, the area of land classified as urban exhibited a 10% increase, with a further 7% increase observed in the subsequent period, spanning 2010 to 2017. In the most recent period under examination, the urban land area increased by 9%, a figure that is consistent with the rates observed in previous years. These results indicate the stability of urbanization processes in the analysed city, while also revealing significant changes in the limits of urban development and in the intensity of land use. The research project concentrated on the city of Olsztyn and the neighbouring suburban areas, which are subject to direct influence from the city's expansion. The area under study encompasses 202.4 km2 within an eight-km radius of the city centre. The authors of the study emphasized the necessity for systematic monitoring of changes in the transition zone between urban and rural areas. This is to ensure effective control of spatial development and ongoing adjustment of planning tools to effectively prevent uncontrolled expansion. The methodology used enabled the precise delimitation of urban development and the transition zone. This allowed for an in-depth analysis of changes in land use intensity. [ABSTRACT FROM AUTHOR]

The article author reflects on Juval Portugali's Bohmian-inspired approach to unifying urban studies through a conceptual framework that bridges social theory-hermeneutics and analytic-positivist science. Topics include the deep epistemological and political divides within urban studies, challenges in reconciling ontological differences between opposing schools of thought, and the internal fragmentation of analytic and hermeneutic camps, for cohesive theoretical foundation.

Two key building characteristics, namely the number of stories and typology, is vital across various domains such as construction management and architectural design. These aspects are particularly critical for disaster risk assessment and infrastructure planning. Although deep learning models are adept at extracting this information from Street view images (SVIs), their success is contingent upon the availability of large and diverse data sets with high accuracy. Image augmentation presents an alternative method to artificially broaden data set diversity. However, the impact of image augmentation techniques on identifying building stories and typologies from SVIs has not been adequately explored. This study proposes a methodology employing eight distinct image augmentation techniques—brightness, contrast, perspective, rotation, scale, shearing, and translation augmentations—as well as a combined approach using all these methods. The study evaluates the efficacy of models trained with these techniques by comparing the accuracy of different classes and architectures for each task, both with and without the application of augmentation. The findings revealed that while most augmentation methods enhance model accuracy, their effectiveness is task-dependent. Furthermore, it was observed that the most effective augmentation techniques differ among building classes and architectures within each task. This suggests that augmentation strategies need to be custom-designed to align with the unique features of each class and architectures for precise estimation of the number of stories and building typologies. While the focus of this research is on specific tasks, the evaluated augmentation techniques could also extend to related areas, such as ascertaining the age of buildings or identifying window types. Practical Applications: In this study, the efficacy of augmentation techniques is explored within the framework of identifying the number of stories and building typologies. The models were assessed for average accuracy and class-specific accuracy across various architectures, comparing outcomes with and without the implementation of the proposed augmentation methods. A key finding is that the most effective augmentation method varies between architectures and individual classes. Contrary to common practice in deep learning, where applying multiple augmentation techniques is standard for accuracy enhancement, this study observed that such a strategy did not uniformly improve performance. Specifically, while combining augmentation methods generally resulted in higher average accuracy, this was not the case for some classes within MobileNetV3 when detecting the number of stories. Similarly, for ResNet-152, employing all augmentation techniques together led to the lowest accuracy in certain classes for building typology classification. These results indicate that augmentation strategies may require customization to cater to the distinct characteristics of each class and architecture for accurate estimation of number of stories and building typologies. [ABSTRACT FROM AUTHOR]

The global dilemma of informal settlements persists alongside the fast process of urbanization. Various methods for analyzing remotely sensed images to identify informal settlements using semantic segmentation have been extensively researched, resulting in the development of numerous supervised and unsupervised algorithms. Texture-based analysis is a topic extensively studied in the literature. However, it is important to note that approaches that do not utilize a multiresolution strategy are unable to take advantage of the fact that texture exists at different spatial scales. The capacity to do online mapping and precise segmentation on a vast scale while considering the diverse characteristics present in remotely sensed images carries significant consequences. This research presents a novel approach for identifying informal settlements using multiresolution analysis and self-attention techniques. The technique shows potential for being resilient in the presence of inherent variability in remotely sensed images due to its capacity to extract characteristics at many scales and prioritize areas that contain significant information. Segmented pictures underwent an accuracy assessment, where a comparison analysis was conducted based on metrics such as mean intersection over union, precision, recall, F-score, and overall accuracy. The proposed method's robustness is demonstrated by comparing it to various state-of-the-art techniques. This comparison is conducted using remotely sensed images that have different spatial resolutions and informal settlement characteristics. The proposed method achieves a higher accuracy of approximately 95%, even when dealing with significantly different image characteristics. [ABSTRACT FROM AUTHOR]

Authorities have long proved the utility of bioindicators in monitoring the state of environmental pollution. Some biological indicators can measure environmental pollutant levels, and many tree species have been tested for suitability for monitoring purposes. The differences in morphological characteristics in the trees have demonstrated the effects of human activities on different materials. Measuring bark and wood biomass from contaminated sites was identified and directly compared with those from a clean site or areas characterized by distinct contamination sources. However, preliminary results demonstrate the approach's potential in the realization of strategies for disease control and promoting health to reduce environmental and health inequalities in at-risk urban areas. Picea orientalis L. and Cedrus atlantica Endl., especially their bark, can be regarded as a more robust storage of Cu (37.95 mg/kg) and Mn (188.25 mg/kg) than Pinus pinaster, Cupressus arizonica, and Pseudotsuga menziesii, which and is therefore a better bioindicator for Cu and Mn pollution. Considering the total concentrations as a result of the study, the pollution is thought to be caused by environmental problems and traffic in the region. The deposition of Cu, Mn, Ni, and Zn elements was found P. menziesii (60, 443, 58, and 258 mg/kg) and P. orientalis (76, 1684, 41, and 378 mg/kg) and seems to reflect atmospheric quite clearly compared to P. pinaster, C. arizonica, and C. atlantica. Ni and Zn concentrations have significantly increased since 1983, and P. menziesii and P. orientalis can be potentially valuable bioindicators for emphasizing polluted fields. [ABSTRACT FROM AUTHOR]

This study reviews the significance of archaeological parks in the tourism sector, with a specific focus on how digital presence and visitor feedback impact their cultural and touristic appeal. Utilizing statistical analyses such as regression and cluster analysis on data sourced from both Hungarian and Western European parks from the Roman Period, the research scrutinizes TripAdvisor and Google ratings, alongside the volume of photographs and reviews. The findings highlight a modest correlation between digital visibility and overall visitor ratings, underscoring the complex relationship between online engagement and the quality of visitor experience. Notably, the cluster analysis identified two distinct groups, signifying diverse levels of visitor engagement and digital visibility across the parks. The conclusions drawn advocate for a comprehensive strategy to enhance visitor experiences, emphasizing the integration of digital engagement with the promotion of the parks cultural and historical values. By exploring the influence of digital presence on the appeal of archaeological parks, our study sheds light on the evolving interface between urban studies and digital heritage, highlighting how digital tools and online platforms are becoming increasingly integral in shaping the perceptions and experiences of urban cultural sites. [ABSTRACT FROM AUTHOR]

Urbanization is one of the most visible symptoms of global changes. This process has been driven by the evolution of life on Earth, and it gradually modifies the structure of land use. Urban development is the most apparent indicator and measure of urbanization. The demand for vacant land and sustainable spatial development plans require new methods that support decision-making in the process of changing land use in suburban areas. The aim of this study was to describe a methodology for identifying and localizing urban development boundaries with the use of fuzzy set theory, to evaluate the degree of urbanization, and to analyze the dynamics of land-use changes in areas subjected to direct urbanization pressure with the use of photogrammetric data for 2005, 2010, 2017, and 2022. A case study was conducted in the Polish city of Olsztyn. The study's results determined the degree of urbanization of the areas in the [0, 1] range, as well as the dynamics of change in each of the twenty-four adopted forms of land use. The results indicate that the proposed methods are useful for identifying the rate and direction of urbanization, and can be applied to optimize and counterbalance the development of settlements and infrastructure. [ABSTRACT FROM AUTHOR]

To manage the historic built heritage, it is of fundamental importance to fully understand the urban area under study, so that all its characteristics and critical issues related to historical conformation, stratification, and transformations can be better understood and described. Geometric surveying allows a deeper investigation of these characteristics through analytical investigation in support of urban planning theories as well. To date, geomatics provides various tools and techniques to meet the above-mentioned needs, and mobile mapping system (MMS) is a technology that can survey large areas in a short time, with good results in terms of density, accuracy, and coverage of the data. In this context, the article aims to verify whether this approach can also be useful in the complex and stratified reality of the historic urban context. The case analyzed—the historical center of Sabbioneta—presents some criticalities found in many urban centers of historical layout. Examples are narrow streets inserted in an urban context with multi-story buildings and consequent difficulty in receiving the GNSS signal and difficulty in following general MMS survey guidelines (trajectories with closed loops, wide radius curves). The analysis presented, relating to a survey carried out with Leica Pegasus:Two instrumentation, in addition to describing the strategies used to properly develop the survey, aims to analyze the resulting datum by discussing its possibilities for use in urban modeling, for cartographic or three-dimensional information modeling purposes. Particular attention is paid to assessing whether the quality of the data (accuracy, density) is suitable for the urban scale. Finally, an analysis of the data obtained from MMS was made with the geographic-topographic database (DBGT), in a GIS (Geographic Information System) environment, to check the possibilities of use and integration between the two models. [ABSTRACT FROM AUTHOR]

The documentation of urban morphology is linked to the complex operation of representing the city, which over the centuries has been undertaken using different methodologies, instruments, and purposes. The "IT revolution" has expanded the possibility of overlapping and relating multiple pieces of information in connection to the urban organism on the same support and, on the other hand, has opened up new scenarios linked to the use of urban digital twins to support the analysis and urban planning. The 21st century has marked a momentous turning point compared to the recent past: the advent of artificial intelligence has in fact allowed the introduction, alongside Urban Information Systems, of 'Predictive' Systems, capable of formulating new scenarios on the basis of the elements available and pictured on 3D models. At the same time, the technical and technological acquisitions of the last century have contributed to evident experimentation on the metaverse, which, although it still exists in a de-emphasised form, is currently a whole universe under construction and expansion. Its rules are written with every passing day, in which the individual can recreate a reality similar to, or absolutely antithetical to, the one they experience on a daily basis, populating virtual cities that elude the established urban dynamics of physical structures. [ABSTRACT FROM AUTHOR]

The article deals with community resilience-oriented urban renewal in a geographical periphery, and the characteristic patterns of public housing built in the 1950s and 1960s. When the existing fabric is well-established and effectively serves the residents, demolition and redevelopment may not be the most strategic approach if they undermine the internal resilience of public housing based on functionality. The article addresses the question of how to add new construction and a new population to these patterns of public housing in the periphery without affecting the community resilience of longtime residents and the sense of urban vitality and innovation of the new population. In order to address this question, we examined the built environment's qualities in relation to the population's resilience. Specifically, we conducted a quality analysis of the built environment focusing primarily on walkability and connectivity, diversity and land uses, open public spaces, and visibility to internal and external views. The findings of the analysis recommend developing a multiple urban spatial network relying on the longtime community's resilience and a new spatial network for the newcomers. This is a potentially win–win solution. The old neighborhoods remain, while at the same time an additional layer of housing and other land uses will be developed along the edges of existing neighborhoods. The proposed analysis will be demonstrated on the peripherical city of Kiryat Yam. [ABSTRACT FROM AUTHOR]

This article discusses the need to incorporate the graphic analysis of the evolution of urban environments into the study of buildings, sometimes covering periods of time far removed from the time of their construction. Knowledge of inherited urban conditions provides relevant information not only to define the relationship between public and built space, but also to understand the original configuration of buildings and their subsequent transformations. As an example, I analyse graphically and jointly both the buildings of the Royal Court and the Royal Prison of Seville and the evolution of their common location, confirming that the basic structure of the two buildings, with a central free space and a small construction facing the public thoroughfare, has its origin in the previous evolution of the urban fabric in which they were inserted. [ABSTRACT FROM AUTHOR]

Building extraction from remote sensing data is an important topic in urban studies and the deep learning methods have an increasing role due to their minimal requirements in training data to reach outstanding performance. We aimed to investigate the original U-Net architecture’s efficiency in building segmentation with different number of training images and the role of data augmentation based on multisource remote sensing data with varying spatial and spectral resolutions (WorldView-2 [WV2], WorldView-3 [WV3] images and an aerial orthophoto [ORTHO]). When the trainings and predictions were conducted on the same image, U-Net provided good results with very few training images (validation accuracies: 94-97%; 192 images). Combining the ORTHO’s and WV2’s training data for prediction on WV3 provided poor results with low F1-score (0.184). However, the inclusion of only 48 WV3 training images significantly improved the F1-score (0.693), thus, most buildings were correctly identified. Accordingly, using only independent reference data (other than the target image) is not enough to train an accurate model. In our case, the reference from WW2 and ORTHO images did not provide an acceptable basis to train a good model, but a minimal number of training images from the targeted WV3 improved the accuracy (F1-score: 69%).

ABSTRACTTechniques for the semantic segmentation of remotely sensed imageries for building footprint identification have been widely studied and several supervised and unsupervised techniques have been proposed. The ability to perform online mapping and accurate segmentation on a large scale by taking into account the multifariousness inherent in aerial images has important implications. In this paper we propose a new method for building footprint identification using multiresolution analysis-based self-attention technique. The scheme is promising to be robust in the face of variability inherent in remotely sensed images by virtue of the capability to extract features at multiple scales and focusing on areas containing meaningful information. We demonstrate the robustness of the proposed method by comparing it against several state-of-the-art techniques using aerial imagery with varying spatial resolution and building clutter and it achieves better accuracy around 95% even under widely disparate image characteristics. We also evaluate the ability for online mapping on an embedded graphic processing unit (GPU) and compare it against different compute engines and it is found that the proposed method on GPU outperforms the other methods in terms of accuracy and processing time.

The global dilemma of informal settlements persists alongside the fast process of urbanization. Various methods for analyzing remotely sensed images to identify informal settlements using semantic segmentation have been extensively researched, resulting in the development of numerous supervised and unsupervised algorithms. Texture-based analysis is a topic extensively studied in the literature. However, it is important to note that approaches that do not utilize a multiresolution strategy are unable to take advantage of the fact that texture exists at different spatial scales. The capacity to do online mapping and precise segmentation on a vast scale while considering the diverse characteristics present in remotely sensed images carries significant consequences. This research presents a novel approach for identifying informal settlements using multiresolution analysis and self-attention techniques. The technique shows potential for being resilient in the presence of inherent variability in remotely sensed images due to its capacity to extract characteristics at many scales and prioritize areas that contain significant information. Segmented pictures underwent an accuracy assessment, where a comparison analysis was conducted based on metrics such as mean intersection over union, precision, recall, F-score, and overall accuracy. The proposed method’s robustness is demonstrated by comparing it to various state-of-the-art techniques. This comparison is conducted using remotely sensed images that have different spatial resolutions and informal settlement characteristics. The proposed method achieves a higher accuracy of approximately 95%, even when dealing with significantly different image characteristics.

Urban analysis uses new data integration with computational methods to gain insight into urban methodologies. But the challenge is how to populate automatically from various urban documents. This paper proposes the ontology population problem by an ontology system for knowledge acquisition from textual resources and automatically populating the spatial urban ontology. Further, it aims to identify and extract useful textual terms and assign them to the predefined concepts (classes), instances (individuals), attributes (data properties) and relationships (object properties) of the urban ontology. The proposed ontology population system combines Natural language processing techniques and deep learning. Initially, the proposed work undergoes three major processes. They are the data acquisition, knowledge extraction, and ontology population processes. The deep learning model spiking equilibrium convolutional neural network (SECNN) is used to obtain high-quality information from text. The model's performance is evaluated on precision, recall and F1-score metrics. The proposed SECNN attained a better precision value of 96.18%, recall value of 91.42% and F1-score value of 97.52%, respectively. Thus, the proposed SECNN model shows improved effectiveness over other models on spatial ontology. [ABSTRACT FROM AUTHOR]

Assyria's desire for global domination was marked by the empire's ideological conception of a "superior" center and an "inferior" periphery. The creation of the Neo-Assyrian provincial system during the 8th–7th centuries b.c.e. was intended to bring order and stability to a chaotic frontier and to create new realities for colonizers and colonized alike. Nevertheless, upon conquering new regions imperial ambitions were reined in by considerations of ecology, demographics, and politics. While the "colonial geography" of the Assyrian empire is often studied from textual, visual, and historical perspectives, it is also marked by distinct material culture expressions that are often explored from a technical point of view. Being one of the main foci of Assyrian imperial expansion, the southern Levant constitutes an ideal case study for investigating material culture manifestations of Assyrian imperial influence on conquered territories. An extensively explored site, the rich archaeological evidence from Tel Dan in the Hula Valley (Stratum Ib) provides a unique opportunity to explore imperial policies of deportation and resettlement in the face of geopolitical forces, as well as Assyrian impact on the everyday life of indigenous populations and recently arrived deportees. [ABSTRACT FROM AUTHOR]

This research faces the theme of the actuality of the Urban Morphology discipline. Starting with the reconstruction of the history of urban studies in the schools of architecture in Italy in the last century, a personal approach to the theme is proposed as a result of an intense activity of academic cooperation of the authors (from the University of Naples Federico II) with the research group of Uwe Schröder (RWTH Aachen University in Germany). The main innovation, if referred to the Italian tradition, is that the city is now observed not only from the point of view of the 'form' but also considering the 'space': thus, the different qualities of the spaces that build every city and its wealth. The 'results' of this research are in the projects that the authors develop on different academic occasions; in fact, in Architecture, theory and practice are strictly connected. This is the reason why two projects are described at the end of the study in order to demonstrate that only a disciplinary knowledge of the city can produce its responsible modification, while more general 'Conclusions' define the innovation that the study proposes to the debate on Urban Morphology. [ABSTRACT FROM AUTHOR]

Recent improvements in remote sensing technologies have boosted building detection techniques from rough classifications using moderate resolution imagery to precise extraction from high-resolution imagery. Shadows frequently emerge in high-resolution urban images. To exploit shadow information, we developed a novel building detection and classification algorithm for images of urban areas with large-size shadows, employing only the visible spectral bands to determine the height levels of buildings. The proposed method, building general-classified by height (BGCH), calculates shadow orientation, detects buildings using seed-blocks, and classifies the buildings into different height groups. Our proposed approach was tested on complex urban scenes from Toronto and Beijing. The experimental results illustrate that our proposed method accurately and efficiently detects and classifies buildings by their height levels; the building detection rate exceeded 95%. The precision of classification by height levels was over 90%. This novel building-height-level detection method provides rich information at low cost and is suitable for further city scene analysis, flood disaster risk assessment, population estimation, and building change detection applications. [ABSTRACT FROM AUTHOR]

Copyright of Space & Form / Przestrzeń i FORMA is the property of West Pomeranian University of Technology, Department of Architecture & Urban Planning and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Decarbonisation policies are often implemented in cities through the promotion of rooftop solar resources. However, urban solar assessments need to identify favourable locations and appropriate sizing to effectively support these strategies. This research aims to estimate the potential for photovoltaic (PV) systems in a dense urban context, as a basis for future policy support. The downtown district of Toronto, Ontario (Canada) is examined as a case study using the 2030 online platform. This work adopts a multi-scalar methodology to model the potential of roof-mounted PV systems for the main residential archetypes. An urban-scale GIS-LiDAR assessment, informed by environmental criteria, is followed by a block-level optimization using URBANopt, which considers energy and economic parameters. The rooftop GIS-based analysis estimates that up to 20% of electricity consumption for detached houses could be satisfied, primarily in the summer, and 5% for apartment buildings. Optimization with URBANopt shows that solar collective configurations can provide significant benefits to users, primarily in terms of economics. When optimization is performed by clusters for each block, the benefits over single-building analysis are evident, particularly in reducing lifecycle costs. In the selected case study, polycrystalline panels with net metering can achieve self-sufficiency levels ranging from 18% to 41% for residential blocks. This study confirms that solar PV systems can increase local production, reduce grid energy dependency, and support energy communities. [ABSTRACT FROM AUTHOR]

The rise of Information and Communication Technology, the spread of sustainability and urbanisation are among the three most unprecedented global trends in their size, impact and evolution, and will likely radically change our world. The world is expected to become largely urban and computerised in just a few decades. The global drive to integrate technology with the city to improve the functioning of the city's various systems and urban areas is confirmed by numerous studies and research on the topic of information technology and urban communication ICT. Still, mostly in the context of smart cities (SC), as for SCC sustainable smart cities, there needs to be more research on this topic because these cities are emerging cities and pave the way for a new urban age. This paper will be one of the research on smart sustainable cities SSC explains the researcher's motivations for the need to produce smart, sustainable urban forms that achieve greater sustainability by using more innovative and powerful engineering solutions that support models of such forms using the advanced ICT key of Context-Aware computing applications (CAA) and Big Data Analytics (BDA). The study concluded that it is necessary to take advantage of the innovative engineering solutions provided by CAA and BDA and integrate them with sustainable urban forms to achieve greater sustainability while demonstrating how these forms are produced and The importance of joint and close collaboration between researchers, architects, urban planners and urban ICT companies in the context of smart sustainable cities. [ABSTRACT FROM AUTHOR]

Urban analysis strategies are effective in sectors where the gradual evolution of the urban fabric has preserved traces of the urbanisation process, thereby facilitating the definition and sequencing of structural units. In consolidated sectors the traumatic transformations caused by the replacement of singular buildings and the opening of large public spaces generate discontinuities in the evolution of the urban structure, obscuring our understanding of these traces. This difficulty can be overcome by supplementing graphical strategies with analyses of the subsoil and the buildings themselves. We have used this procedure to reconstruct the morphology of the southern sector of Seville in the 16th century. As proof of its effectiveness, we discuss the results obtained in the analysis of two of the blocks that form part of the sector: the ones on which the Santo Tomás College and Santa María de Jesús University were founded. [ABSTRACT FROM AUTHOR]

The documentation of urban morphology is linked to the complex operation of representing the city, which over the centuries has been undertaken using different methodologies, instruments, and purposes. The “IT revolution” has expanded the possibility of overlapping and relating multiple pieces of information in connection to the urban organism on the same support and, on the other hand, has opened up new scenarios linked to the use of urban digital twins to support the analysis and urban planning. The 21st century has marked a momentous turning point compared to the recent past: the advent of artificial intelligence has in fact allowed the introduction, alongside Urban Information Systems, of ‘Predictive’ Systems, capable of formulating new scenarios on the basis of the elements available and pictured on 3D models. At the same time, the technical and technological acquisitions of the last century have contributed to evident experimentation on the metaverse, which, although it still exists in a de-emphasised form, is currently a whole universe under construction and expansion. Its rules are written with every passing day, in which the individual can recreate a reality similar to, or absolutely antithetical to, the one they experience on a daily basis, populating virtual cities that elude the established urban dynamics of physical structures.

The urban renewal of deteriorated areas is a challenge for many city decision-makers. In this study, we aimed to understand the role and impact of the Israeli national outline plan, TAMA38, on urban renewal areas by examining three urban renewal strategies. This plan was developed to strengthen individual buildings against earthquakes, but it also serves as a catalyst for the renewal of deteriorated individual residential buildings in old neighborhoods, particularly in high-demand districts. TAMA38 focuses on the renovation of individual buildings, primarily residential, but neglects the comprehensive vision of the public and private needs of the neighborhood/site complex, of which the individual building is only one component. To understand which planning strategy will achieve better spatial results under TAMA38, a broader examination is required. The objective of this study was to assess the performance of three urban sites developed under the TAMA38 program in the city of Haifa using three main strategies: (1) one comprehensive plan led by one developer with a change in building locations (2) one comprehensive plan but led by diverse developers, while building locations remain unchanged and (3) individual building renewals with no comprehensive plan. The methodology for this analysis was based on the evaluation of various quantitative and quality parameters that influence the performance of the built environment. The results of the research emphasize the need to choose an urban renewal strategy tailored to a specific location, as well as the need for the authority to take responsibility for planning open public spaces throughout the process.

The new digital era brings increasingly massive and complex interdisciplinary projects in various fields. At the same time, the availability of an accurate and reliable database plays a crucial role in achieving project goals. Meanwhile, urban projects and issues usually need to be analyzed to support the objectives of sustainable development of the built environment. Furthermore, the volume and variety of spatial data used to describe urban elements and phenomena have grown exponentially in recent decades. The scope of this dataset is to process spatial data to provide input data for the urban heat island (UHI) assessment project in Tallinn, Estonia. The dataset builds the generative, predictive, and explainable machine learning UHI model. The dataset presented here consists of multi-scale urban data. It provides essential baseline information for (i) urban planners, researchers, and practitioners to incorporate urban data in their research activities, (ii) architects and urban planners to improve the features of buildings and the city, considering urban data and the UHI effect, (iii) stakeholders, policymakers and administration in cities implementing built environment projects, and supporting urban sustainability goals. The dataset is available for download as supplementary material to this article.

With the growth of residential rooftop PV adoption in recent decades, the problem of effective layout design has become increasingly important in recent years. Although a number of automated methods have been introduced, these tend to rely on simplifying assumptions and heuristics to improve computational tractability. We demonstrate a fully automated layout design pipeline that attempts to solve a more general formulation with greater geometric flexibility that accounts for shading losses. Our approach generates rooftop areas from satellite imagery and uses MINLP optimization to select panel positions, azimuth angles and tilt angles on an individual basis rather than imposing any predefined layouts. Our results demonstrate that shading plays a critical role in automated rooftop PV optimization and significantly changes the resulting layouts. Additionally, they suggest that, although several common heuristics are often effective, they may not be universally suitable due to complications resulting from geometric restrictions and shading losses. Finally, we evaluate a few specific heuristics from the literature and propose a potential new rule of thumb that may help improve rooftop solar energy potential when shading effects are considered. [ABSTRACT FROM AUTHOR]

Building extraction from remote sensing data is an important topic in urban studies and the deep learning methods have an increasing role due to their minimal requirements in training data to reach outstanding performance. We aimed to investigate the original U-Net architecture's efficiency in building segmentation with different number of training images and the role of data augmentation based on multisource remote sensing data with varying spatial and spectral resolutions (WorldView-2 [WV2], WorldView-3 [WV3] images and an aerial orthophoto [ORTHO]). When the trainings and predictions were conducted on the same image, U-Net provided good results with very few training images (validation accuracies: 94-97%; 192 images). Combining the ORTHO's and WV2's training data for prediction on WV3 provided poor results with low F1-score (0.184). However, the inclusion of only 48 WV3 training images significantly improved the F1-score (0.693), thus, most buildings were correctly identified. Accordingly, using only independent reference data (other than the target image) is not enough to train an accurate model. In our case, the reference from WW2 and ORTHO images did not provide an acceptable basis to train a good model, but a minimal number of training images from the targeted WV3 improved the accuracy (F1-score: 69%). [ABSTRACT FROM AUTHOR]

Techniques for the semantic segmentation of remotely sensed imageries for building footprint identification have been widely studied and several supervised and unsupervised techniques have been proposed. The ability to perform online mapping and accurate segmentation on a large scale by taking into account the multifariousness inherent in aerial images has important implications. In this paper we propose a new method for building footprint identification using multiresolution analysis-based self-attention technique. The scheme is promising to be robust in the face of variability inherent in remotely sensed images by virtue of the capability to extract features at multiple scales and focusing on areas containing meaningful information. We demonstrate the robustness of the proposed method by comparing it against several state-of-the-art techniques using aerial imagery with varying spatial resolution and building clutter and it achieves better accuracy around 95% even under widely disparate image characteristics. We also evaluate the ability for online mapping on an embedded graphic processing unit (GPU) and compare it against different compute engines and it is found that the proposed method on GPU outperforms the other methods in terms of accuracy and processing time. [ABSTRACT FROM AUTHOR]

Copyright of Quintana: Revista do Departamento de Historia da Arte USC is the property of Universidad de Santiago de Compostela, Servicio de Publicaciones and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research faces the theme of the actuality of the Urban Morphology discipline. Starting with the reconstruction of the history of urban studies in the schools of architecture in Italy in the last century, a personal approach to the theme is proposed as a result of an intense activity of academic cooperation of the authors (from the University of Naples Federico II) with the research group of Uwe Schröder (RWTH Aachen University in Germany). The main innovation, if referred to the Italian tradition, is that the city is now observed not only from the point of view of the ‘form’ but also considering the ‘space’: thus, the different qualities of the spaces that build every city and its wealth. The ‘results’ of this research are in the projects that the authors develop on different academic occasions; in fact, in Architecture, theory and practice are strictly connected. This is the reason why two projects are described at the end of the study in order to demonstrate that only a disciplinary knowledge of the city can produce its responsible modification, while more general ‘Conclusions’ define the innovation that the study proposes to the debate on Urban Morphology.

The practice of implementing urban plans for the construction area of the City of Belgrade has shown that in some cases they do not contain enough elements to implement directly. This is especially true for special purpose sites and facilities, whose design and construction require the pre-definition of more detailed conditions and rules of landscaping and construction. In order to address such issues, the authors explore the practice of developing urban analyses in the case of the air traffic control facility at Belgrade's Nikola Tesla Airport. The paper presents the starting points for developing an urban analysis and excerpts from relevant planning documents, while analyzing the existing condition of the site. The basic results of the analysis show the available capabilities and capacity of the site. In particular, the paper analyzes how well the criteria for building a flight control tower, as a tall object and future location benchmark, were fulfilled. The findings underscore the importance of urban analysis as an instrument for implementing planning solutions for special purpose facilities, which by their nature are unique and of the utmost national importance. The authors also look at urban analysis as a scientific method, while laying out basic guidelines and emphasizing the need for further research.

The urban renewal of deteriorated areas is a challenge for many city decision-makers. In this study, we aimed to understand the role and impact of the Israeli national outline plan, TAMA38, on urban renewal areas by examining three urban renewal strategies. This plan was developed to strengthen individual buildings against earthquakes, but it also serves as a catalyst for the renewal of deteriorated individual residential buildings in old neighborhoods, particularly in high-demand districts. TAMA38 focuses on the renovation of individual buildings, primarily residential, but neglects the comprehensive vision of the public and private needs of the neighborhood/site complex, of which the individual building is only one component. To understand which planning strategy will achieve better spatial results under TAMA38, a broader examination is required. The objective of this study was to assess the performance of three urban sites developed under the TAMA38 program in the city of Haifa using three main strategies: (1) one comprehensive plan led by one developer with a change in building locations (2) one comprehensive plan but led by diverse developers, while building locations remain unchanged and (3) individual building renewals with no comprehensive plan. The methodology for this analysis was based on the evaluation of various quantitative and quality parameters that influence the performance of the built environment. The results of the research emphasize the need to choose an urban renewal strategy tailored to a specific location, as well as the need for the authority to take responsibility for planning open public spaces throughout the process. [ABSTRACT FROM AUTHOR]

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Recent improvements in remote sensing technologies have boosted building detection techniques from rough classifications using moderate resolution imagery to precise extraction from high-resolution imagery. Shadows frequently emerge in high-resolution urban images. To exploit shadow information, we developed a novel building detection and classification algorithm for images of urban areas with large-size shadows, employing only the visible spectral bands to determine the height levels of buildings. The proposed method, building general-classified by height (BGCH), calculates shadow orientation, detects buildings using seed-blocks, and classifies the buildings into different height groups. Our proposed approach was tested on complex urban scenes from Toronto and Beijing. The experimental results illustrate that our proposed method accurately and efficiently detects and classifies buildings by their height levels; the building detection rate exceeded 95%. The precision of classification by height levels was over 90%. This novel building-height-level detection method provides rich information at low cost and is suitable for further city scene analysis, flood disaster risk assessment, population estimation, and building change detection applications.

The rise of Information and Communication Technology, the spread of sustainability and urbanisation are among the three most unprecedented global trends in their size, impact and evolution, and will likely radically change our world. The world is expected to become largely urban and computerised in just a few decades. The global drive to integrate technology with the city to improve the functioning of the city’s various systems and urban areas is confirmed by numerous studies and research on the topic of information technology and urban communication ICT. Still, mostly in the context of smart cities (SC), as for SCC sustainable smart cities, there needs to be more research on this topic because these cities are emerging cities and pave the way for a new urban age. This paper will be one of the research on smart sustainable cities SSC explains the researcher’s motivations for the need to produce smart, sustainable urban forms that achieve greater sustainability by using more innovative and powerful engineering solutions that support models of such forms using the advanced ICT key of Context-Aware computing applications (CAA) and Big Data Analytics (BDA). The study concluded that it is necessary to take advantage of the innovative engineering solutions provided by CAA and BDA and integrate them with sustainable urban forms to achieve greater sustainability while demonstrating how these forms are produced and The importance of joint and close collaboration between researchers, architects, urban planners and urban ICT companies in the context of smart sustainable cities.

Copyright of Revista de Arquitectura (1657-0308) is the property of Universidad Catolica de Columbia, Facultad de Arquitectura and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Green transit-oriented development (TOD) is an evolution of the TOD theory, influenced by sustainable development and green urbanism. This advancement expands the environmental and ecological dimensions of conventional TOD. However, relevant research has only just started, particularly in combination with high-density cities in Asia. This study measures the built environment of Green TOD and identifies the key factors and characteristics of the Green TOD built environment. An evaluation method for the Green TOD built environment is developed on the basis of the 5D (density, diversity, design, destination, and distance) built environment framework and combined with green urbanism theories. Data from 23 rail station areas in Singapore were collected and analyzed using factor and cluster analysis. Five factors, namely, neighborhood spatial form factor, facility completeness factor, open space factor, high-density mixed land-use factor, and walking amenity factor, are extracted. Twenty-three station areas are classified into five clusters. The characteristics and optimization strategies of each cluster are analyzed on the basis of the statistical and graphical analyses. This research displays the characteristics and typology of the Green TOD built environment in an empirical way and offers a foundation for further research in diverse urban contexts.