This study investigates the role of digital technologies, specifically Building Information Modelling (BIM) and City Information Modelling (CIM), in sustainable urban renewal. The study reviews the literature on urban renewal and the application of BIM and CIM technologies. The findings show that digital technologies play a crucial role in facilitating the flow and sharing of information, improving the transparency of decision-making, and enhancing public participation. The study emphasises the need for interdisciplinary collaboration and technological innovation to achieve more sustainable and liveable urban environments.
Antonio Salis, Gabriele Troina, Gianluca Boanelli
et al.
The efficient design and management of public green spaces is a key factor in promoting the health and well-being of urban population, as emphasized by the WHO, UNEP, and EEA. These areas serve as the "green lungs" of the urban ecosystem, playing a vital role in enhancing quality of life thanks to the provision of ecosystem services. In this context, the Smart Green City use case in Campobasso municipality, funded by the Italian Ministry of Enterprises (MIMIT), emerges as an innovative model for the sustainable management of green urban areas through the adoption of an advanced system of emerging technologies integrated and interoperable. The project integrates IoT systems and data-driven governance platforms, enabling real-time monitoring of the health status of trees and green areas via a Decision Support System (DSS). It also facilitates the collection and analysis of data from diverse sources, including weather conditions, air quality, soil moisture, pollution levels. The resulting cloud-based platform supports a holistic real time decision making for green urban managers, technical experts and operational staff. It enables intelligent control and management of urban green spaces using Tree Talker sensors, integrated with soil moisture and water potential monitoring systems. Thanks to predictive models based on machine learning algorithms and real time data provided by IoT sensors, irrigation of public parks can be optimized by providing suggestions on when and how much water to apply. Customized alerts layers are also activated warning users when monitored parameters, such as soil temperature, humidity, or water potential, exceed predefined thresholds. This Use Case demonstrates how digitalization, IoT sensors fusion and technological innovation can support sustainable urban governance, fostering environmental resilience and improving citizens quality of life.
Urban and transportation research has long sought to uncover statistically meaningful relationships between key variables and societal outcomes such as road safety, to generate actionable insights that guide the planning, development, and renewal of urban and transportation systems. However, traditional workflows face several key challenges: (1) reliance on human experts to propose hypotheses, which is time-consuming and prone to confirmation bias; (2) limited interpretability, particularly in deep learning approaches; and (3) underutilization of unstructured data that can encode critical urban context. Given these limitations, we propose a Multimodal Large Language Model (MLLM)-based approach for interpretable hypothesis inference, enabling the automated generation, evaluation, and refinement of hypotheses concerning urban context and road safety outcomes. Our method leverages MLLMs to craft safety-relevant questions for street view images (SVIs), extract interpretable embeddings from their responses, and apply them in regression-based statistical models. UrbanX supports iterative hypothesis testing and refinement, guided by statistical evidence such as coefficient significance, thereby enabling rigorous scientific discovery of previously overlooked correlations between urban design and safety. Experimental evaluations on Manhattan street segments demonstrate that our approach outperforms pretrained deep learning models while offering full interpretability. Beyond road safety, UrbanX can serve as a general-purpose framework for urban scientific discovery, extracting structured insights from unstructured urban data across diverse socioeconomic and environmental outcomes. This approach enhances model trustworthiness for policy applications and establishes a scalable, statistically grounded pathway for interpretable knowledge discovery in urban and transportation studies.
Shahriar Iqbal Raj, Tajwar Hossain Wasif, Mujtaba Ahsan
et al.
Dhaka’s rapid urbanisation has intensified challenges of fragmented land use, congestion, and environmental decline, particularly in the Tejgaon Industrial Area (TIA), once designated for light industry but now marked by unregulated mixed-use growth. This study examines the superblock model as a sustainable redevelopment framework for TIA, aiming to integrate density management, mobility, and environmental resilience. A mixed-methods approach combined GIS-based spatial analysis, traffic observations, policy review of the Detailed Area Plan (2022–2035) and Building Construction Rules (2008), and participatory design workshops. Insights from 151 residents, workers, and students informed scenario testing across three models: plot-based, block-based, and superblock development. Findings show that superblocks reduce congestion by diverting traffic to peripheral roads, enhance permeability with 44% more permeable surfaces compared to block-based schemes, and allocate nearly 50% of land for open and public spaces. These outcomes highlight the potential of superblock urbanism to support Sustainable Development Goals (SDGs 3, 11, 13, and 15), improving walkability, public health, and climate resilience. The study concludes that superblocks provide a scalable planning strategy for Dhaka’s transformation and for other fast-growing urban contexts.
Dalma Erzsébet Varga, Ildikó Módosné Bugyi, Tibor Bíró
et al.
Napjaink egyik egyre inkább kiemelkedő feladata a víz-készletek megőrzése, a vízvisszatartás megvalósítása,amelynek az Európai Unió által elfogadott természet-helyreállítási rendelet is keretet szab. Magyarországterületének mintegy harmada a folyószabályozásokatmegelőzően időszakosan vagy rendszeresen vízjárta terü-let volt. A XVIII-XIX. századi ármentesítési munkálatokeredményeként megindult egyrészt a szárazodás folya-mata, másrészt a belvízképződés jelensége, amely nap-jainkban az ország síkvidéki, művelt területének mint-egy 60%-át érinti valamilyen mértékben. A belvíz – báridőszakosan jelentkezik egy adott tájrészletben – meg-határozó ökológiai szerepe mellett kiemelkedő tájképihatással is bír. Kutatásunk fő célja a belvízjárta területekértékalapú megközelítésével azok tájkarakterformáló,valamint tájképi szerepének azonosítása és elemzése. Tér-informatikai módszerek alkalmazásával összevetettük amagyarországi országos tájkarakter-kutatás eredménye-ként született tájkarakter-típusok adatbázisát a belvíz-járta területek adatállományával, illetve külön elemeztüka két kategória összefüggéseit belvízelöntési gyakori-ságok szerint is. Az eredmények alátámasztása érdeké-ben elemeztük a belvízjárta területek és a tájképvédelmiövezet területének átfedését is. Eredményeink alapjánmegállapítható, hogy a belvízjárta területek tájkaraktertbefolyásoló, illetve tájképformáló szerepe meghatározó ésszámadatokkal alátámasztható. A belvízi elöntés növeli atáj mozaikosságát, változatosságát – különösen homogénagrártájakban –, ami hozzájárul a biodiverzitás növelésé-hez is. Az elemzések során csoportosítottuk a tájkarak-ter-típusokat a vízbefolyásoltságuk mértéke szerint, ígyelkülönítettük, hogy a vízdomináns, vízbefolyásolt vagyvízhatástól független tájkarakterű területek miként viszo-nyulnak a belvízjárta területekhez. Az eredmények aztmutatják, hogy a belvízi elöntéssel érintett területek többmint ötöde (21,36%-a) vízdomináns, illetve vízbefolyásolttájkarakter-típusba került besorolásra. Következéskép-pen a belvízi érintettség úgy is szerepet játszott a tájka-rakter-típusba sorolás során, hogy nem volt indikátor atájkarakter-típusok kijelölésénél. A belvízjárta területekjellemző tájkarakterbeli besorolása tekintetében megál-lapítható, hogy elsősorban a homogén, szántó, illetve gye-pek által meghatározott agrárdomináns tájakon jelentke-zik magasabb arányban. Mivel a belvíz elsősorban lokálisszinten, kisebb foltokban jelentkező időszakos vízkészlet,a táji szintű hatásai kevésbé hangsúlyosan jelentkeznek,azonban nem elhanyagolható ezen területek tájkép- és táj-karakterformáló szerepe regionális léptékben sem.
This study examines whether the innovative city pilot policy launched by the Chinese government in 2008 has reduced or exacerbated urban carbon emissions. Using panel data from 269 cities in China (2007–2016) and applying a difference-in-differences approach, we find that the policy significantly increased carbon emissions. On average, annual per capita emissions in innovative cities reached 35,400 tons, compared to 19,100 tons in non-innovative cities, reflecting a 1.85% increase. These results suggest that while innovation-driven urban policies foster economic and technological progress, they may also intensify energy use and emissions, raising concerns about environmental effectiveness. Mechanism analysis indicates that impacts operate through innovation capacity, output levels, and development incentives. The findings highlight the importance of aligning innovation policies with sustainability goals. This study contributes by providing evidence on the unintended environmental effects of innovation pilots and offering policy insights for building innovative yet low-carbon cities in China.
The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management. Despite their effectiveness, these applications often rely on massive amounts of high-dimensional and multi-domain data for monitoring and characterizing different urban sub-systems, presenting challenges in application areas that are limited by data quality and availability, as well as costly efforts for generating urban scenarios and design alternatives. As an emerging research area in deep learning, Generative Artificial Intelligence (AI) models have demonstrated their unique values in data and code generation. This survey paper aims to explore the innovative integration of generative AI techniques and urban digital twins to address challenges in the realm of smart cities in various urban sectors, such as transportation and mobility management, energy system operations, building and infrastructure management, and urban design. The survey starts with the introduction of popular generative AI models with their application areas, followed by a structured review of the existing urban science applications that leverage the autonomous capability of the generative AI techniques to facilitate (a) data augmentation for promoting urban monitoring and predictive analytics, (b) synthetic data and scenario generation, (c) automated 3D city modeling, and (d) generative urban design and optimization. Based on the review, this survey discusses potential opportunities and technical strategies that integrate generative AI models into the next-generation urban digital twins for more reliable, scalable, and automated management of smart cities.
Cities emerged independently across different world regions and historical periods, raising fundamental questions: How did the first urban settlements develop? What social and spatial conditions enabled their emergence? Are these processes universal or context-dependent? Moreover, what distinguishes cities from other human settlements? This paper investigates the drivers behind the creation of cities through a hybrid approach that integrates urban theory, the biological concept of morphospace (the space of all possible configurations), and archaeological evidence. It explores the transition from sedentary hunter-gatherer communities to urban societies, highlighting fundamental forces converging to produce increasingly complex divisions of labour as a central driver of urbanization. Morphogenesis is conceptualized as a trajectory through morphospace, governed by structure-seeking selection processes that balance density, permeability, and information as critical dimensions. The study highlights the non-ergodic nature of urban morphogenesis, where configurations are progressively selected based on their fitness to support the diversifying interactions between mutually dependent agents. The morphospace framework effectively distinguishes between theoretical spatial configurations, non-urban and proto-urban settlements, and contemporary cities. This analysis supports the proposition that cities emerge and evolve as solutions balancing density, permeability, and informational organization, enabling them to support increasingly complex societal functions.
Fabio Miranda, Thomas Ortner, Gustavo Moreira
et al.
Urbanization has amplified the importance of three-dimensional structures in urban environments for a wide range of phenomena that are of significant interest to diverse stakeholders. With the growing availability of 3D urban data, numerous studies have focused on developing visual analysis techniques tailored to the unique characteristics of urban environments. However, incorporating the third dimension into visual analytics introduces additional challenges in designing effective visual tools to tackle urban data's diverse complexities. In this paper, we present a survey on visual analytics of 3D urban data. Our work characterizes published works along three main dimensions (why, what, and how), considering use cases, analysis tasks, data, visualizations, and interactions. We provide a fine-grained categorization of published works from visualization journals and conferences, as well as from a myriad of urban domains, including urban planning, architecture, and engineering. By incorporating perspectives from both urban and visualization experts, we identify literature gaps, motivate visualization researchers to understand challenges and opportunities, and indicate future research directions.
Urban morphology, examining city spatial configurations, links urban design to sustainability. Morphology metrics play a fundamental role in performance-driven computational urban design (CUD) which integrates urban form generation, performance evaluation and optimization. However, a critical gap remains between performance evaluation and complex urban form generation, caused by the disconnection between morphology metrics and urban form, particularly in metric-to-form workflows. It prevents the application of optimized metrics to generate improved urban form with enhanced urban performance. Formulating morphology metrics that not only effectively characterize complex urban forms but also enable the reconstruction of diverse forms is of significant importance. This paper highlights the importance of establishing a bi-directional mapping between morphology metrics and complex urban form to enable the integration of urban form generation with performance evaluation. We present an approach that can 1) formulate morphology metrics to both characterize urban forms and in reverse, retrieve diverse similar 3D urban forms, and 2) evaluate the effectiveness of morphology metrics in representing 3D urban form characteristics of blocks by comparison. We demonstrate the methodology with 3D urban models of New York City, covering 14,248 blocks. We use neural networks and information retrieval for morphology metric encoding, urban form clustering and morphology metric evaluation. We identified an effective set of morphology metrics for characterizing block-scale urban forms through comparison. The proposed methodology tightly couples complex urban forms with morphology metrics, hence it can enable a seamless and bidirectional relationship between urban form generation and optimization in performance-driven urban design towards sustainable urban design and planning.
Tram Thi Minh Tran, Callum Parker, Marius Hoggenmüller
et al.
Augmented reality (AR) has the potential to fundamentally change how people engage with increasingly interactive urban environments. However, many challenges exist in designing and evaluating these new urban AR experiences, such as technical constraints and safety concerns associated with outdoor AR. We contribute to this domain by assessing the use of virtual reality (VR) for simulating wearable urban AR experiences, allowing participants to interact with future AR interfaces in a realistic, safe and controlled setting. This paper describes two wearable urban AR applications (pedestrian navigation and autonomous mobility) simulated in VR. Based on a thematic analysis of interview data collected across the two studies, we found that the VR simulation successfully elicited feedback on the functional benefits of AR concepts and the potential impact of urban contextual factors, such as safety concerns, attentional capacity, and social considerations. At the same time, we highlighted the limitations of this approach in terms of assessing the AR interface's visual quality and providing exhaustive contextual information. The paper concludes with recommendations for simulating wearable urban AR experiences in VR.
Restructuring routes are significant urban elements that lead to physical changes in cities to meet modern needs, often resulting in traumatic transformations. This study investigates the morphological impact of the palimpsest phenomenon on Istanbul's Atatürk Boulevard, an exemplar of restructuring routes. The Boulevard's construction disrupted the organic urban fabric, creating a new structure and altering the area's identity. By employing a typomorphological analysis based on Conzen’s Town Plan Analysis, this research examines changes in the street, building, and subdivision systems. The study utilizes historical and contemporary maps to reveal the effects of Atatürk Boulevard on urban morphology, demonstrating both positive and negative consequences of restructuring routes. Findings indicate that the boulevard increased the economic value of the area but also led to the fragmentation and loss of historical urban elements, contributing to a complex urban palimpsest. The study highlights the need for sensitive urban planning to preserve the city's historical identity while accommodating modern developments. The insights gained are crucial for architects and urban planners in managing urban landscapes, ensuring sustainability, and respecting historical contexts in future developments
Urban traffic attributed to commercial and industrial transportation is observed to largely affect living standards in cities due to external effects pertaining to pollution and congestion. In order to counter this, smart cities deploy technological tools to achieve sustainability. Such tools include Digital Twins (DT)s which are virtual replicas of real-life physical systems. Research suggests that DTs can be very beneficial in how they control a physical system by constantly optimizing its performance. The concept has been extensively studied in other technology-driven industries like manufacturing. However, little work has been done with regards to their application in urban logistics. In this paper, we seek to provide a framework by which DTs could be easily adapted to urban logistics networks. To do this, we provide a characterization of key factors in urban logistics for dynamic decision-making. We also survey previous research on DT applications in urban logistics as we found that a holistic overview is lacking. Using this knowledge in combination with the characterization, we produce a conceptual model that describes the ontology, learning capabilities and optimization prowess of an urban logistics digital twin through its quantitative models. We finish off with a discussion on potential research benefits and limitations based on previous research and our practical experience.
Rapid urbanization is leading to a drastic hike in anthropogenic activities and urban surface alterations. As a result, there are many repercussions, one of them being higher temperatures in urban areas when compared to rural areas. This phenomenon is termed Urban Heat Island (UHI). The impacts of urban surface characteristics, climate, and population density on UHI have been extensively studied. However, the influence of UHI on the local climate remains elusive. Relatively few studies demonstrate interrelation between UHI, population density, and unanticipated precipitation events. Therefore, it is important to comprehend the connection as it can impact extreme temperature events like heat waves and unanticipated precipitation events like flash flooding. The objective of this study is to investigate the association between UHI, population density, and precipitation in the summer and winter seasons in Indian Western Coastal Cities. To comprehend this association, a hypothesis test employing the Spearman rank correlation coefficient is conducted for 1991, 2001, 2011, and 2021. From the study, it is found that in summer, the surface temperature is directly proportional to population density and inversely proportional to precipitation. In winter the contrary relation is observed. This study also provides the seasonal variation and temporal evolution of the correlation among the parameters. This research will aid in making informed decisions for urban planning and addressing climate change.
R Anantama Anantama Benanda Yasminingrat , Bambang Sulistyantara, Nizar Nasrullah
Promoting positive mental health is of utmost importance, especially in today's urban context, as recognized by the World Health Organization. However, the scarcity of green spaces in urban areas presents challenges for fostering a connection with nature. To address this gap, this study investigates the distinctive contribution of urban home gardens to the mental well-being of women in the Greater Jakarta Area. Through an online survey, participant backgrounds and mental well-being were assessed using the WEMWBS scale. Validity and reliability were established using Pearson's product-moment correlation and Cronbach's alpha, respectively. The study unravels several influential factors, including garden interaction (visit frequency and duration) and size. Crucially, it advances the literature by shedding light on the original contribution of urban home gardens to mental well-being. Notably, women who visit their urban home garden more than seven times a week and spend over two hours per day in their garden report the highest levels of mental well-being. These findings highlight the unique role of urban home gardens in promoting mental well-being among urban women. Consequently, they hold implications for policymakers and practitioners aiming to enhance mental well-being and foster access to green spaces in urban areas.
The relationship between urban density and social conditions in urban areas has received increasing attention in recent research. However, there is a lack of understanding of the dynamics between urban densification and these social conditions from a place-specific perspective, taking into account the institutional, socio-cultural, and contextual complexities. This paper seeks to enhance this understanding by unpacking the relationship between soft densification and place attachment in Tehran, Iran. The paper develops a framework for studying ‘soft densification’ as a process of incremental place change by prioritising local knowledge. The findings suggest that soft densification impacts place attachment by disrupting the everyday functionality of place, eroding its physical characteristics, erasing some of its collective and personal memories, and altering its socio-demographic structure. The paper highlights the importance of thinking ‘procedurally’ and ‘topologically’ about urban densification and calls for incorporating local knowledge and experiences into policy planning and urban decision-making.
Monitoring urban structure and development requires high-quality data at high spatiotemporal resolution. While traditional censuses have provided foundational insights into demographic and socioeconomic aspects of urban life, their pace may not always align with the pace of urban development. To complement these traditional methods, we explore the potential of analyzing alternative big-data sources, such as human mobility data. However, these often noisy and unstructured big data pose new challenges. Here we propose a method to extract meaningful explanatory variables and classifications from such data. Using movement data from Beijing, which are produced as a byproduct of mobile communication, we show that meaningful features can be extracted, revealing, for example, the emergence and absorption of subcentres. This method allows the analysis of urban dynamics at a high spatial resolution (here, 500m) and near real-time frequency, and high computational efficiency, which is especially suitable for tracing event-driven mobility changes and their impact on urban structures.
Harsh G. Kamath, Manmeet Singh, Neetiraj Malviya
et al.
We introduce University of Texas - Global Building heights for Urban Studies (UT-GLOBUS), a dataset providing building heights and urban canopy parameters (UCPs) for more than 1200 cities or locales worldwide. UT-GLOBUS combines open-source spaceborne altimetry (ICESat-2 and GEDI) and coarse-resolution urban canopy elevation data with a machine-learning model to estimate building-level information. Validation using LiDAR data from six US cities showed UT-GLOBUS-derived building heights had a root mean squared error (RMSE) of 9.1 meters. Validation of mean building heights within 1-km^2 grid cells, including data from Hamburg and Sydney, resulted in an RMSE of 7.8 meters. Testing the UCPs in the urban Weather Research and Forecasting (WRF-Urban) model resulted in a significant improvement (55% in RMSE) in intra-urban air temperature representation compared to the existing table-based local climate zone approach in Houston, TX. Additionally, we demonstrated the dataset's utility for simulating heat mitigation strategies and building energy consumption using WRF-Urban, with test cases in Chicago, IL, and Austin, TX. Street-scale mean radiant temperature simulations using the Solar and LongWave Environmental Irradiance Geometry (SOLWEIG) model, incorporating UT-GLOBUS and LiDAR-derived building heights, confirmed the dataset's effectiveness in modeling human thermal comfort in Baltimore, MD (daytime RMSE = 2.85 C). Thus, UT-GLOBUS can be used for modeling urban hazards with significant socioeconomic and biometeorological risks, enabling finer scale urban climate simulations and overcoming previous limitations due to the lack of building information.
The future of the human race is urban. The world's population is projected to grow an additional 2.5 billion by 2050, with all expected to live in urban areas. This will increase the percentage of urban population from 55% today to 70% within three decades and further strengthen the role of cities as the hub for information, transportation, and overall socio-economic development. Unlike any other time in human history, the increasing levels of autonomy and machine intelligence are transforming cities to be no longer just human agglomerations but a fusion of humans, machines, and algorithms making collective decisions, thus complex socio-technical systems. This manuscript summarizes and discusses my efforts from the urban autonomy and mobility perspective to develop the urban socio-technical system.