Land-use classification based on spaceborne or aerial remote sensing images has been extensively studied over the past decades. Such classification is usually a patch-wise or pixel-wise labeling over the whole image. But for many applications, such as urban population density mapping or urban utility planning, a classification map based on individual buildings is much more informative. However, such semantic classification still poses some fundamental challenges, for example, how to retrieve fine boundaries of individual buildings. In this paper, we proposed a general framework for classifying the functionality of individual buildings. The proposed method is based on Convolutional Neural Networks (CNNs) which classify facade structures from street view images, such as Google StreetView, in addition to remote sensing images which usually only show roof structures. Geographic information was utilized to mask out individual buildings, and to associate the corresponding street view images. We created a benchmark dataset which was used for training and evaluating CNNs. In addition, the method was applied to generate building classification maps on both region and city scales of several cities in Canada and the US.
One of the detailed and useful ways to develop land use classification maps is use of geospatial techniques such as remote sensing and Geographic Information System (GIS). It vastly improves the selection of areas designated as agricultural, industrial and/or urban sector of a region. In Islamabad city and its surroundings, change in land use has been observed and new developments (agriculture, commercial, industrial and urban) are emerging every day. Thus, the rationale of this study was to evaluate land use/cover changes in Islamabad from 1992 to 2012. Quantification of spatial and temporal dynamics of land use/cover changes was accomplished by using two satellite images, and classifying them via supervised classification algorithm and finally applying post-classification change detection technique in GIS. The increase was observed in agricultural area, built-up area and water body from 1992 to 2012. On the other hand forest and barren area followed a declining trend. The driving force behind this change was economic development, climate change and population growth. Rapid urbanization and deforestation resulted in a wide range of environmental impacts, including degraded habitat quality.
Abstract Cities produce over 70% of the global CO2 emissions that result from energy use, and thus play a key role in climate mitigation and adaptation. While the factors influencing CO2 emissions have been subject to extensive study, via research that has explored the path of developing a low-carbon economy, little work has been undertaken at the city level as a result of a deficiency in data availability. Addressing this gap, this study firstly estimated CO2 emissions of cities in China using Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime light imagery. We then analyzed spatial variations in the estimated CO2 emissions at the city level, using a spatial analytical model, finding significant spatial autocorrelation in CO2 emissions. Subsequently, we compared the effects of different socioeconomic factors on CO2 emissions, using both global and local regression models. The results from the global regression model revealed that private car ownership, economic growth, and energy consumption were the major factors promoting CO2 emissions in China’s cities, while population density had an effect in reducing CO2 emissions. The use of a Geographically Weighted Regression (GWR) model provided more detailed results, revealing significant spatial heterogeneity in the impacts of different factors. Economic growth, private car ownership, and energy consumption all posed positive effects on CO2 emissions while the remainder of the factors studied were found to pose a bidirectional impact on CO2 emissions in different areas of China. Economic growth and private car ownership were to found to exert the strongest positive effects in the cities of western and central China, and energy consumption was shown to significantly and positively influence CO2 emissions in the southernmost part of China. Urban expansion and road density were identified as key promoting factors in CO2 emissions in the northeast of China; and the industrial structure demonstrated significantly positive effects in relation to CO2 levels in cities located in the Beijing-Tianjin-Hebei region. The role of foreign direct investment (FDI) was not found to be significant in most cities expect Guangdong, where a significant positive relationship appeared.
Abdeslam BAALLA, Nissrine KHOUNA, Tarik JELLOULI
et al.
Objectives: This study aims to analyze the drivers and barriers of digital transformation in radiology services within Moroccan public hospitals, identifying conditions necessary to guide these services toward a smart hospital model that enhances efficiency, quality, and governance. Prior Work: Previous research has highlighted the global importance of digitalization in healthcare, with radiology being central due to high data dependence and technological complexity. In Morocco, studies focus mostly on broad health system digitalization, with limited attention to public hospital radiology services. Approach: A narrative literature review was conducted, integrating academic publications, institutional reports (WHO, OECD, Moroccan Ministry of Health), and national strategic documents. Keywords related to digital health, hospital information systems, radiology services, and smart hospital governance were used to identify relevant sources between 2010 and 2025. The analysis built a conceptual framework linking technological infrastructure, interoperability, governance, workforce capacity, and cybersecurity with digital transformation outcomes. Results: The review identified key drivers, including robust infrastructures, interoperable PACS/RIS systems, strategic leadership, and staff training. Major barriers include heterogeneous equipment, system fragmentation, limited digital competencies, financial constraints, organizational resistance, and cybersecurity risks. Moroccan public hospitals show opportunities for modernization, particularly when digitalization is embedded within broader smart government strategies. Implications: Effective digital transformation in radiology requires integrated planning, investment in technology and workforce, phased adoption strategies, and robust governance structures. Aligning hospital digitalization with urban and national digital health initiatives can improve efficiency, quality, and patient safety.Value: This study provides a foundation for strategic planning and operational guidance to develop “intelligent radiology services” in Moroccan public hospitals, contributing to the broader modernization of healthcare delivery and digital governance.
Social sciences (General), Cities. Urban geography
Lorena Torres Lahoz, Carlos Lima Azevedo, Leonardo Ancora
et al.
Urban environments significantly influence mental health outcomes, yet the role of an effective framework for decision-making under deep uncertainty (DMDU) for optimizing urban policies for stress reduction remains underexplored. While existing research has demonstrated the effects of urban design on mental health, there is a lack of systematic scenario-based analysis to guide urban planning decisions. This study addresses this gap by applying Scenario Discovery (SD) in urban planning to evaluate the effectiveness of urban vegetation interventions in stress reduction across different urban environments using a predictive model based on emotional responses collected from a neuroscience-based outdoor experiment in Lisbon. Combining these insights with detailed urban data from Copenhagen, we identify key intervention thresholds where vegetation-based solutions succeed or fail in mitigating stress responses. Our findings reveal that while increased vegetation generally correlates with lower stress levels, high-density urban environments, crowding, and individual psychological traits (e.g., extraversion) can reduce its effectiveness. This work showcases our Scenario Discovery framework as a systematic approach for identifying robust policy pathways in urban planning, opening the door for its exploration in other urban decision-making contexts where uncertainty and design resiliency are critical.
This paper presents a novel computational approach for evaluating urban metrics through density gradient analysis using multi-modal satellite imagery, with applications including public transport and other urban systems. By combining optical and Synthetic Aperture Radar (SAR) data, we develop a method to segment urban areas, identify urban centers, and quantify density gradients. Our approach calculates two key metrics: the density gradient coefficient ($α$) and the minimum effective distance (LD) at which density reaches a target threshold. We further employ machine learning techniques, specifically K-means clustering, to objectively identify uniform and high-variability regions within density gradient plots. We demonstrate that these metrics provide an effective screening tool for public transport analyses by revealing the underlying urban structure. Through comparative analysis of two representative cities with contrasting urban morphologies (monocentric vs polycentric), we establish relationships between density gradient characteristics and public transport network topologies. Cities with clear density peaks in their gradient plots indicate distinct urban centers requiring different transport strategies than those with more uniform density distributions. This methodology offers urban planners a cost-effective, globally applicable approach to preliminary public transport assessment using freely available satellite data. The complete implementation, with additional examples and documentation, is available in an open-source repository under the MIT license at https://github.com/nexri/Satellite-Imagery-Urban-Analysis.
Alaa Al Hawarneh, M. Shahria Alam, Rajeev Ruparathna
et al.
Given the growing emphasis on life-cycle analysis in bridge design, the design community is transitioning from the concept of performance-based design in structural engineering to a performance-based design approach within a life-cycle context. This approach considers various indicators, including cost, environmental impact, and societal factors when designing bridges. This shift enables a comprehensive assessment of structural resilience by examining the bridge's ability to endure various hazards throughout its lifespan. This study provides a comprehensive review of two key research domains that have emerged in the field of bridge life-cycle analysis, namely life-cycle sustainability (LCS) and life-cycle performance (LCP). The discussion on the LCS of bridges encompasses both assessment-based and optimization-based studies, while the exploration of LCP focuses on research examining structures subjected to deterioration over their service life due to deprecating phenomena such as corrosion and relative humidity changes, as well as extreme hazards like earthquakes and floods. Moreover, this study discusses the integration between LCS and LCP, highlighting how combined consideration of these factors can minimize damage costs, improve resiliency, and extend the lifespan of the structure. A detailed evaluation encompasses various life-cycle metrics, structural performance indicators, time-dependent modelling techniques, and analysis methods proposed in the literature. Additionally, the research identifies critical gaps and trends in life-cycle analysis within the realm of bridge engineering, providing a concise yet thorough overview for advancing considerations in the life-cycle design of bridges.
Disasters and engineering, Cities. Urban geography
In the last two decades, seismic resilience (SR) has been developed as a main concept for the assessment of the structural vulnerabilities of buildings and city centres. In particular, historical centers consist of adjacent buildings organized in blocks with common characteristics and similar typologies. The paper proposes a methodology to quantify SR for urban regions, by overcoming the state of the art studies that focus on assessing the SR for singular buildings. In this regard, the presented methodology may calculate the SR of blocks of buildings for the assessment of recovery investments of historical city centers. The main idea is to assess the level of vulnerability by accurate 3D surveys and visual inspections in order to select empirical fragility curves. The proposed methodology was herein applied to the city center of San Marino, designated by UNESCO as a world heritage site.
Disasters and engineering, Cities. Urban geography
This study employs percolation theory to investigate the hierarchical organisation of Australian urban centres through the connectivity of their road networks. The analysis demonstrates how discrete urban clusters have developed into integrated regional entities, delineating the pivotal distance thresholds that regulate these urban transitions. The study reveals the interconnections between disparate urban clusters, shaped by their functional differentiation and historical development. Furthermore, the study identifies a dichotomy of urban agglomeration forces and a persistent spatial disconnection between Australia's wider urban landscape. This highlights the interplay between urban densification and peripheral growth. It suggests the need for new thinking on potential integrated governance structures that bridge urban development with broader social and economic policies across regional and national scales. Additionally, the study emphasises the growing importance of national coordination in Australian urban development planning to ensure regional consistency, equity, and productivity.
Social segregation in cities, spanning racial, residential, and income dimensions, is becoming more diverse and severe. As urban spaces and social relations grow more complex, residents in metropolitan areas experience varying levels of social segregation. If left unaddressed, this could lead to increased crime rates, heightened social tensions, and other serious issues. Effectively quantifying and analyzing the structures within urban spaces and resident interactions is crucial for addressing segregation. Previous studies have mainly focused on surface-level indicators of urban segregation, lacking comprehensive analyses of urban structure and mobility. This limitation fails to capture the full complexity of segregation. To address this gap, we propose a framework named Motif-Enhanced Graph Prototype Learning (MotifGPL),which consists of three key modules: prototype-based graph structure extraction, motif distribution discovery, and urban graph structure reconstruction. Specifically, we use graph structure prototype learning to extract key prototypes from both the urban spatial graph and the origin-destination graph, incorporating key urban attributes such as points of interest, street view images, and flow indices. To enhance interpretability, the motif distribution discovery module matches each prototype with similar motifs, representing simpler graph structures reflecting local patterns. Finally, we use the motif distribution results to guide the reconstruction of the two graphs. This model enables a detailed exploration of urban spatial structures and resident mobility patterns, helping identify and analyze motif patterns that influence urban segregation, guiding the reconstruction of urban graph structures. Experimental results demonstrate that MotifGPL effectively reveals the key motifs affecting urban social segregation and offer robust guidance for mitigating this issue.
Public urban spaces like streetscapes and plazas serve residents and accommodate social life in all its vibrant variations. Recent advances in Robotics and Embodied AI make public urban spaces no longer exclusive to humans. Food delivery bots and electric wheelchairs have started sharing sidewalks with pedestrians, while robot dogs and humanoids have recently emerged in the street. Micromobility enabled by AI for short-distance travel in public urban spaces plays a crucial component in the future transportation system. Ensuring the generalizability and safety of AI models maneuvering mobile machines is essential. In this work, we present MetaUrban, a compositional simulation platform for the AI-driven urban micromobility research. MetaUrban can construct an infinite number of interactive urban scenes from compositional elements, covering a vast array of ground plans, object placements, pedestrians, vulnerable road users, and other mobile agents' appearances and dynamics. We design point navigation and social navigation tasks as the pilot study using MetaUrban for urban micromobility research and establish various baselines of Reinforcement Learning and Imitation Learning. We conduct extensive evaluation across mobile machines, demonstrating that heterogeneous mechanical structures significantly influence the learning and execution of AI policies. We perform a thorough ablation study, showing that the compositional nature of the simulated environments can substantially improve the generalizability and safety of the trained mobile agents. MetaUrban will be made publicly available to provide research opportunities and foster safe and trustworthy embodied AI and micromobility in cities. The code and dataset will be publicly available.
Research on cycling conditions focuses on cities, because cycling is commonly considered an urban phenomenon. People outside of cities should, however, also have access to the benefits of active mobility. To bridge the gap between urban and rural cycling research, we analyze the bicycle network of Denmark, covering around 43,000 km2 and nearly 6 mio. inhabitants. We divide the network into four levels of traffic stress and quantify the spatial patterns of bikeability based on network density, fragmentation, and reach. We find that the country has a high share of low-stress infrastructure, but with a very uneven distribution. The widespread fragmentation of low-stress infrastructure results in low mobility for cyclists who do not tolerate high traffic stress. Finally, we partition the network into bikeability clusters and conclude that both high and low bikeability are strongly spatially clustered. Our research confirms that in Denmark, bikeability tends to be high in urban areas. The latent potential for cycling in rural areas is mostly unmet, although some rural areas benefit from previous infrastructure investments. To mitigate the lack of low-stress cycling infrastructure outside of urban centers, we suggest prioritizing investments in urban-rural cycling connections and encourage further research in improving rural cycling conditions.
Different social backgrounds and planning policies give rise to diverse urban morphologies. These morphologies influence urban microclimate factors and contribute to the formation of unique local microclimates, particularly in terms of outdoor temperature. In recent times, the heat island effect has gained increasing significance during the summer season. Therefore, this study aims to explore the correlation between urban microclimate simulation and urban morphology within the context of the heat island effect. Specifically, we investigate how the outside temperature varies across different types of residential buildings in Yeongdeungpo-gu, Seoul, South Korea, during the summer period. We compare temperature conditions using a multi-dimensional system of building clusters' morphological indices and employ ENVI-met software for simulation purposes. The results of the urban microclimate simulation are comprehensively analyzed, revealing a significant finding: high-rise residential buildings exhibit considerably higher outdoor temperatures compared to low-rise residential buildings. Furthermore, the presence of open spaces plays a crucial role in mitigating high neighborhood temperatures. By deriving insights from these findings, we aim to provide valuable conclusions to support city managers in making informed decisions.
With the recent expansion of urban greening interventions, the definition of spatial indicators to measure the provision of urban greenery has become of pivotal importance in informing the policy-design process. By analyzing the stability of the population and area rankings induced by several indicators of green accessibility for over 1,000 cities worldwide, we investigate the extent to which the use of a single metric provides a reliable assessment of green accessibility in a city. The results suggest that, due to the complex interaction between the spatial distribution of greenspaces in an urban center and its population distribution, the use of a single indicator might lead to insufficient discrimination across areas or subgroups of the population, even when focusing on one form of green accessibility. From a policy perspective, this indicates the need to switch toward a multi-dimensional framework that is able to organically evaluate a range of indicators at once.