Hasil untuk "Urban renewal. Urban redevelopment"

Wei Chen, Yuqian Wu, Junle Chen et al.

Urban systems, as dynamic complex systems, continuously generate spatio-temporal data streams that encode the fundamental laws of human mobility and city evolution. While AI for Science has witnessed the transformative power of foundation models in disciplines like genomics and meteorology, urban computing remains fragmented due to "scenario-specific" models, which are overfitted to specific regions or tasks, hindering their generalizability. To bridge this gap and advance spatio-temporal foundation models for urban systems, we adopt scaling as the central perspective and systematically investigate two key questions: what to scale and how to scale. Grounded in first-principles analysis, we identify three critical dimensions: heterogeneity, correlation, and dynamics, aligning these principles with the fundamental scientific properties of urban spatio-temporal data. Specifically, to address heterogeneity through data scaling, we construct WorldST. This billion-scale corpus standardizes diverse physical signals, such as traffic flow and speed, from over 100 global cities into a unified data format. To enable computation scaling for modeling correlations, we introduce the MiniST unit, a novel split mechanism that discretizes continuous spatio-temporal fields into learnable computational units to unify representations of grid-based and sensor-based observations. Finally, addressing dynamics via architecture scaling, we propose UrbanFM, a minimalist self-attention architecture designed with limited inductive biases to autonomously learn dynamic spatio-temporal dependencies from massive data. Furthermore, we establish EvalST, the largest-scale urban spatio-temporal benchmark to date. Extensive experiments demonstrate that UrbanFM achieves remarkable zero-shot generalization across unseen cities and tasks, marking a pivotal first step toward large-scale urban spatio-temporal foundation models.

Wenjing Gong, Udbhav Srivastava, Yuchen Wang et al.

Conventional urban indicators derived from censuses, surveys, and administrative records are often costly, spatially inconsistent, and slow to update. Recent geospatial foundation models enable Earth embeddings, compact satellite image representations transferable across downstream tasks, but their utility for neighborhood-scale urban monitoring remains unclear. Here, we benchmark three Earth embedding families, AlphaEarth, Prithvi, and Clay, for urban signal prediction across six U.S. metropolitan areas from 2020 to 2023. Using a unified supervised-learning framework, we predict 14 neighborhood-level indicators spanning crime, income, health, and travel behavior, and evaluate performance under four settings: global, city-wise, year-wise, and city-year. Results show that Earth embeddings capture substantial urban variation, with the highest predictive skill for outcomes more directly tied to built-environment structure, including chronic health burdens and dominant commuting modes. By contrast, indicators shaped more strongly by fine-scale behavior and local policy, such as cycling, remain difficult to infer. Predictive performance varies markedly across cities but remains comparatively stable across years, indicating strong spatial heterogeneity alongside temporal robustness. Exploratory analysis suggests that cross-city variation in predictive performance is associated with urban form in task-specific ways. Controlled dimensionality experiments show that representation efficiency is critical: compact 64-dimensional AlphaEarth embeddings remain more informative than 64-dimensional reductions of Prithvi and Clay. This study establishes a benchmark for evaluating Earth embeddings in urban remote sensing and demonstrates their potential as scalable, low-cost features for SDG-aligned neighborhood-scale urban monitoring.

فائزه محمدی ششکل, محمد شیخی

در بسیاری از سکونتگاه‌های غیررسمی، کیفیت فضاهای شهری به دلیل فقدان برنامه‌ریزی و توجه به نیازهای ساکنان، به شدت پایین است. این موضوع، چالش‌های متعددی را در زمینه‌های مختلف از جمله سلامت، امنیت، اقتصاد و محیط‌زیست به وجود می‌آورد. مکان‌سازی به‌عنوان رویکردی نوین در طراحی و مدیریت فضاهای شهری، می‌تواند به ارتقای کیفیت زندگی در سکونتگاه‌های غیررسمی کمک کند. این تحقیق با رویکرد تفسیری و روش کیفی از نوع مطالعه موردی، به‌صورت تحلیلی - توصیفی صورت‌گرفته است. جامعه آماری شامل متخصصان دانشگاهی و سرپرستان دفاتر تسهیلگری و مدیران شهری بود که به‌صورت هدفمند از نوع گلوله‌برفی انتخاب شدند. ابتدا از روش فراترکیب برای شناسایی مؤلفه‌های مکان‌سازی استفاده شد و سپس چالش‌ها از طریق مشاهده و مصاحبه‌های عمیق و نیمه‌ساختاریافته با متخصصان بررسی شدند. تحلیل داده‌ها با استفاده از نرم‌افزار MAXQDA و کدگذاری بر اساس ابعاد مدیریتی، اجتماعی، اقتصادی و کالبدی انجام شد. نتایج نشان داد که مکان‌سازی فرایندی زمان‌بر و مشارکتی است که نیازمند هماهنگی بین ذی‌نفعان، زیرساخت‌های مناسب و درک مزایای مکان‌سازی به سبب جدید بودن موضوع، توسط مقامات و جامعه محلی است. همچنین تأمین منابع مالی و اعتمادسازی از چالش‌های اصلی این فرایند محسوب می‌شوند. یافته‌های این تحقیق می‌تواند به‌عنوان سند راهنما برای مدیران و دفاتر تسهیلگری جهت ارتقای کیفیت فضاهای شهری در سکونتگاه‌های غیررسمی مورداستفاده قرار گیرد.

Anna Szövényi

Most már nehéz letagadni, hogy éghajlatunk rohamos ütemben alakul át. Magyarországon a nyári időszakban lassan elviselhetetlenné válik a lét városi tereinken. Mi lehet ebből a kiút? Tudunk-e időben, gyorsan reagálni mi, emberek, a saját magunk alkotta negatív környezeti kihívásokra? A hallgatók, amikor diplomatémát választanak, igyekeznek a számukra legaktuálisabb és a nagyközönség számára legérdekesebb témákat felvetni. Vajon mik lehetnek a jövő zöld megoldásai? Mi a jövője városainknak, tájainknak, városi tájainknak? A diplomaszámban megjelenő hallgatói tervek jórésze a klímaadaptációt és a zöldfelületek intenzitásának növelését, a biodiverzitás serkentését tartják a megoldásnak, de vannak, akik társadalmi kérdések megoldásában vagy technikai innovációban gondolkoznak. A tervek és álmok alkotta paletta diverz. Mégis, mik azok a sarokpontok, amelyekben hallgatóink szerint a megoldás rejlik? A diplomaszámban megjelenő hallgatói tervek jórésze a klímaadaptációt és a zöldfelületek intenzitásának növelését, a biodiverzitás serkentését tartják a megoldásnak, de vannak, akik társadalmi kérdések megoldásában vagy technikai innovációban gondolkoznak. A tervek és álmok alkotta paletta diverz. Mégis, mik azok a sarokpontok, amelyekben hallgatóink szerint a megoldás rejlik?

Qingyi Wang, Yuebing Liang, Yunhan Zheng et al.

Generative AI offers new opportunities for automating urban planning by creating site-specific urban layouts and enabling flexible design exploration. However, existing approaches often struggle to produce realistic and practical designs at scale. Therefore, we adapt a state-of-the-art Stable Diffusion model, extended with ControlNet, to generate high-fidelity satellite imagery conditioned on land use descriptions, infrastructure, and natural environments. To overcome data availability limitations, we spatially link satellite imagery with structured land use and constraint information from OpenStreetMap. Using data from three major U.S. cities, we demonstrate that the proposed diffusion model generates realistic and diverse urban landscapes by varying land-use configurations, road networks, and water bodies, facilitating cross-city learning and design diversity. We also systematically evaluate the impacts of varying language prompts and control imagery on the quality of satellite imagery generation. Our model achieves high FID and KID scores and demonstrates robustness across diverse urban contexts. Qualitative assessments from urban planners and the general public show that generated images align closely with design descriptions and constraints, and are often preferred over real images. This work establishes a benchmark for controlled urban imagery generation and highlights the potential of generative AI as a tool for enhancing planning workflows and public engagement.

Devashish Khulbe, Alexander Belyi, Stanislav Sobolevsky

Urban socioeconomic modeling has predominantly concentrated on extensive location and neighborhood-based features, relying on the localized population footprint. However, networks in urban systems are common, and many urban modeling methods don't account for network-based effects. In this study, we propose using commute information records from the census as a reliable and comprehensive source to construct mobility networks across cities. Leveraging deep learning architectures, we employ these commute networks across U.S. metro areas for socioeconomic modeling. We show that mobility network structures provide significant predictive performance without considering any node features. Consequently, we use mobility networks to present a supervised learning framework to model a city's socioeconomic indicator directly, combining Graph Neural Network and Vanilla Neural Network models to learn all parameters in a single learning pipeline. Our experiments in 12 major U.S. cities show the proposed model outperforms previous conventional machine learning models. This work provides urban researchers methods to incorporate network effects in urban modeling and informs stakeholders of wider network-based effects in urban policymaking and planning.

Ciro Beneduce, Bruno Lepri, Massimiliano Luca

Understanding how urban environments are perceived in terms of safety is crucial for urban planning and policymaking. Traditional methods like surveys are limited by high cost, required time, and scalability issues. To overcome these challenges, this study introduces Large Multimodal Models (LMMs), specifically Llava 1.6 7B, as a novel approach to assess safety perceptions of urban spaces using street-view images. In addition, the research investigated how this task is affected by different socio-demographic perspectives, simulated by the model through Persona-based prompts. Without additional fine-tuning, the model achieved an average F1-score of 59.21% in classifying urban scenarios as safe or unsafe, identifying three key drivers of perceived unsafety: isolation, physical decay, and urban infrastructural challenges. Moreover, incorporating Persona-based prompts revealed significant variations in safety perceptions across the socio-demographic groups of age, gender, and nationality. Elder and female Personas consistently perceive higher levels of unsafety than younger or male Personas. Similarly, nationality-specific differences were evident in the proportion of unsafe classifications ranging from 19.71% in Singapore to 40.15% in Botswana. Notably, the model's default configuration aligned most closely with a middle-aged, male Persona. These findings highlight the potential of LMMs as a scalable and cost-effective alternative to traditional methods for urban safety perceptions. While the sensitivity of these models to socio-demographic factors underscores the need for thoughtful deployment, their ability to provide nuanced perspectives makes them a promising tool for AI-driven urban planning.

Mingliang Zhong, Dennis Teutscher, Adrian Kummerländer et al.

Accurate prediction of urban wind flow is essential for urban planning, pedestrian safety, and environmental management. Yet, it remains challenging due to uncertain boundary conditions and the high cost of conventional CFD simulations. This paper presents the use of the modular and efficient uncertainty quantification (UQ) framework OpenLB-UQ for urban wind flow simulations. We specifically use the lattice Boltzmann method (LBM) coupled with a stochastic collocation (SC) approach based on generalized polynomial chaos (gPC). The framework introduces a relative-error noise model for inflow wind speeds based on real measurements. The model is propagated through a non-intrusive SC LBM pipeline using sparse-grid quadrature. Key quantities of interest, including mean flow fields, standard deviations, and vertical profiles with confidence intervals, are efficiently computed without altering the underlying deterministic solver. We demonstrate this on a real urban scenario, highlighting how uncertainty localizes in complex flow regions such as wakes and shear layers. The results show that the SC LBM approach provides accurate, uncertainty-aware predictions with significant computational efficiency, making OpenLB-UQ a practical tool for real-time urban wind analysis.

Dennis Teutscher, Fedor Bukreev, Adrian Kummerlaender et al.

Digital twin (DT) technology is increasingly used in urban planning, leveraging real-time data integration for environmental monitoring. This paper presents an urban-focused DT that combines computational fluid dynamics simulations with live meteorological data to analyze pollution dispersion. Addressing the health impacts of pollutants like particulate matter and nitrogen dioxide, the DT provides real-time updates on air quality, wind speed, and direction. Using OpenStreetMaps XML-based data, the model distinguishes between porous elements like trees and solid structures, enhancing urban flow analysis. The framework employs the lattice Boltzmann method (LBM) within the open-source software OpenLB to simulate pollution transport. Nitrogen dioxide and particulate matter concentrations are estimated based on traffic and building emissions, enabling hot-spot identification. The DT was used from November 7 to 23, 2024, with hourly updates, capturing pollution trends influenced by wind patterns. Results show that alternating east-west winds during this period create a dynamic pollution distribution, identifying critical residential exposure areas. This work contributes a novel DT framework that integrates real-time meteorological data, OpenStreetMap-based geometry, and high-fidelity LBM simulations for urban wind and pollution modeling. Unlike existing DTs, which focus on structural monitoring or large-scale environmental factors, this approach enables fine-grained, dynamic analyses of urban airflow and pollution dispersion. By allowing interactive modifications to urban geometry and continuous data updates, the DT serves as a powerful tool for adaptive urban planning, supporting evidence-based policy making to improve air quality and public health.

Sri Rum Giyarsih, Armansyah, Andy Ahmad Zaelany et al.

This study focuses on urban farming practices in Bandung, West Java, known as Buruan SAE, a programme initiated by the Department of Food Security and Agriculture, Bandung. The objectives of this study are to analyse the effects of urbanisation on urban farming and to explore the potential of urban farming in supporting food security. This study uses a qualitative approach. The results of the study show 3 important issues: the diverse production of urban farming is able to meet the food needs of community members and some have already become independent farmers; there has been a change in the attitude of the people of Bandung city, through urban farming they have become ‘farmers’ a completely new activity for them; urban farming provides a source of income for the urban community. These findings suggest that urban farming can enhance the sustainability of the Buruan SAE community.

Maher Niger, Sanjida Ahmed Sinthia

Transit-Oriented Development (TOD) principles offer a promising framework for integrating metro stations with their surrounding urban fabric, promoting sustainable urbanization and efficient transportation systems. Dhaka, one of the fastest-growing cities globally, faces significant challenges in traffic congestion, air pollution, and urban sprawl. Introducing metro systems offers a promising solution to alleviate these issues and enhance urban mobility. This study presents a case study of Agargaon Metro Station in Dhaka City, examining its integration with the adjacent urban fabric using TOD principles. Through a combination of field observations, spatial analysis, and stakeholder interviews, the study evaluates the current state of Agargaon Metro Station. On-site assessments examined the station's physical infrastructure, accessibility, and connectivity with nearby areas, while Geographic Information Systems (GIS) analyzed spatial data, including land use patterns and transportation networks. Semi-structured interviews with urban planners, government officials, and community members provided insights into the challenges and opportunities for implementing TOD at Agargaon. The findings reveal that the station is underutilized as a TOD hub, with inadequate pedestrian infrastructure and mixed-use developments, leading to poor connectivity and accessibility. The study highlights the necessity of improved land use planning, policy support, and community engagement to enhance the station's role in fostering TOD. These recommendations, if implemented, could alleviate traffic congestion, improve air quality, and create more livable urban spaces, thereby enhancing Dhaka's overall quality of life. Additionally, the research contributes to the social and economic dimensions of urbanization by offering a framework that can be adapted to similar metro stations in Dhaka and other rapidly urbanizing cities.

Pratham Singla, Ayush Singh, Adesh Gupta et al.

Urban planning faces a critical challenge in balancing city-wide infrastructure needs with localized demographic preferences, particularly in rapidly developing regions. Although existing approaches typically focus on top-down optimization or bottom-up community planning, only some frameworks successfully integrate both perspectives. Our methodology employs a two-tier approach: First, a deterministic solver optimizes basic infrastructure requirements in the city region. Second, four specialized planning agents, each representing distinct sub-regions, propose demographic-specific modifications to a master planner. The master planner then evaluates and integrates these suggestions to ensure cohesive urban development. We validate our framework using a newly created dataset comprising detailed region and sub-region maps from three developing cities in India, focusing on areas undergoing rapid urbanization. The results demonstrate that this hybrid approach enables more nuanced urban development while maintaining overall city functionality.

Pegah Eshraghi, Riccardo Talami, Arman Nikkhah Dehnavi et al.

In rapidly urbanizing regions, designing climate-responsive urban forms is crucial for sustainable development, especially in dry arid-climates where urban morphology has a significant impact on energy consumption and environmental performance. This study advances urban morphology evaluation by combining Urban Building Energy Modeling (UBEM) with machine learning methods (ML) and Explainable AI techniques, specifically Shapley Additive Explanations (SHAP). Using Tehran's dense urban landscape as a case study, this research assesses and ranks the impact of 30 morphology parameters at the urban block level on key energy metrics (cooling, heating, and lighting demand) and environmental performance (sunlight exposure, photovoltaic generation, and Sky View Factor). Among seven ML algorithms evaluated, the XGBoost model was the most effective predictor, achieving high accuracy (R2: 0.92) and a training time of 3.64 seconds. Findings reveal that building shape, window-to-wall ratio, and commercial ratio are the most critical parameters affecting energy efficiency, while the heights and distances of neighboring buildings strongly influence cooling demand and solar access. By evaluating urban blocks with varied densities and configurations, this study offers generalizable insights applicable to other dry-arid regions. Moreover, the integration of UBEM and Explainable AI offers a scalable, data-driven framework for developing climate-responsive urban designs adaptable to high-density environments worldwide.

Ahmet Gün

In recent years, the changing pattern of human activities, increasing data regarding the spatial environment, and the possibility of collecting and processing this data allowed us to reconsider how we approach urban design, with a focus on a digital-oriented and data-driven perspective. In this study, we examine the evolution of urban design by analyzing the roles of designers and citizen empowerment. Our analysis includes a literature review and semi-structured interviews with computational design experts. In this sense, the literature is reviewed to investigate previous discussions and findings about the topic, and semi-structured interviews were carried out with seven computational design experts. The experts were selected by considering two criteria: (1) their experience with computational urban design subjects in practice and (2) their academic research background. This study concludes that technology-driven urban design solutions change designers' relationship with data, opening new avenues for objective, data-driven & data-informed decision-making. There are few differences between traditional and computational design practices regarding user empowerment and participatory design. Moreover, technology-driven urban design tools and methods are still in their early stages and are rarely used in actual projects.

Husni Thamrin, Ferry Anderson S.

Steel column structures can be used for various types of buildings, various advantages can be obtained when using steel as a building material, but its use cannot be estimated. Each of these elements will bear forces such as moments, normal or latitude, although the percentages differ from one another. Structures that carry normal forces are generally found in columns, both compressive and tensile so that a normal stress occurs. At this time the framework for the column is conventional steel but at this time there are other alternative materials, namely frames made of mild steel. As we know so far that most of the use of mild steel for roof truss construction, technological advances in the field of steel materials, especially mild steel, have been able to produce high quality mild steel, namely with a minimum tensile strength of 550 MPa. (Zincalume). Based on the results of the analysis, there is a critical stress difference in two cross sections with the same size from different types of materials, namely mild steel and conventional steel.

Rizky Khairi, Faurantia Forlana Sigit, Novalinda et al.

The condition of Taman Sri Deli currently functions as a public space where one of its functions is used as a culinary tourism spot. The problem that occurs is when the Ramadan Fair event is held and there are road closures that disrupt the activities of road users. Therefore, it is necessary to do a solution by referring to the design of the culinary tourism area allocated in Taman Sri Deli in order to maximize the design that can support the activities of existing events. The theme that will be applied in the design of Taman Sri Deli Culinary Tour is the Green Space theme.

Paul Baba Haruna, Stella Zubairu, Remi Ebenezer Olagunju et al.

This study investigates liveability in the context of sustainable public housing in Niger State, Nigeria, where existing housing efforts have fallen short of residents' satisfaction. Recognizing the critical link between liveability indicators and environmental sustainability, this research aims to identify key liveability variables that could be integrated into the design and construction of sustainable public housing. Employing a mixed-method approach, the study involved cluster sampling for selecting housing estates and units, followed by the administration of 910 questionnaires containing 102 questions on liveability variables. Analytical techniques, including Hierarchical Cluster Analysis, Factor Analysis, and Multiple Regression Analysis, were used to group, refine, and validate the liveability variables. The results revealed 21 significant variables that collectively could achieve a 92.9% satisfaction rate among residents if incorporated into public housing design. These findings underline the potential of addressing liveability in the pursuit of sustainable housing solutions, offering insights for urban planners, architects, and policymakers. By focusing on the residents' perspectives, the study contributes to a more user-centred approach in public housing development, promoting long-term satisfaction and reducing the need for post-occupancy alterations.

Dongjie Wang, Chang-Tien Lu, Xinyue Ye et al.

The two fields of urban planning and artificial intelligence (AI) arose and developed separately. However, there is now cross-pollination and increasing interest in both fields to benefit from the advances of the other. In the present paper, we introduce the importance of urban planning from the sustainability, living, economic, disaster, and environmental perspectives. We review the fundamental concepts of urban planning and relate these concepts to crucial open problems of machine learning, including adversarial learning, generative neural networks, deep encoder-decoder networks, conversational AI, and geospatial and temporal machine learning, thereby assaying how AI can contribute to modern urban planning. Thus, a central problem is automated land-use configuration, which is formulated as the generation of land uses and building configuration for a target area from surrounding geospatial, human mobility, social media, environment, and economic activities. Finally, we delineate some implications of AI for urban planning and propose key research areas at the intersection of both topics.

Mr. Shardul P. Gavande

Abstract: Mumbai being the largest city of India, capital of Maharashtra and one of the most populous cities in the world with current estimated population of about 12.4 million it is the world’s 37th largest city by Gross Domestic Product (GDP). As of 2019, recent estimates of the economy of the Mumbai Metropolitan Region have ranged from 3.16 lakh crore (US$44 billion) (2019–20 est.) 4.04 lakh crore (US$57 billion) (2019–20 est.) ranking it either the most or second-most productive metro area of India. In the year 2015, a government led by Bharatiya Janta Party (BJP) launched a program called "Smart City Mission" in the country to develop 100 smart cities for urban renewal and redevelopment of older system by adding the flavour of new technologies. In the recent budget speech of the Finance Minister in February 2020, the move to set up 5 new smart cities has been proposed which will be developed under the public private partnership (PPP) mode. The main focus to develop smart city is to provide with high end infrastructure, excellent services and access to these services is governed based on connectivity at different levels between the administration and the end user. In context to the Smart Cities Mission, the objective is to bring up the best cities from the country by competing them with each other based on the core infrastructure, standard quality of life to its citizens, a clean sustainable environment and application of 'Smart Solutions'. The research article is the study on Mumbai as an aspiration to become a smart city by focusing on number of aspects of smart cities: smart mobility, smart living, smart healthcare, smart environment, smart citizens, smart government, and smart architecture as well as related technologies and concepts. The Internet of Things plays a major role as building block for transforming cities into smart cities by improving quality, performance and interactivity of urban services, optimize resources and reduce costs. Keywords: Smart City, Sustainable Development, Citizen friendly, Information and Communication Technology (ICT), Internet of Things (IoT)

Deniz Ay, Kaner Atakan Turker

This paper explores the governance of a state-led urban renewal project in a politically contested area in the aftermath of a major armed conflict. Building on the ethnocratic regime theory, we explore the governance of the urban renewal process in the historic district of Suriçi by focusing on the political, spatial, and governmental underpinnings of displacement and dispossession in the context of the unresolved “Kurdish Question” of Turkey. We argue that this exclusionary and state-led urban renewal project is shaped around the ethnocratic state interests with limited real estate returns that aims to sanitize and dehistoricize the historic core of Diyarbakir given its political and socioeconomic significance for the Kurdish Movement. The rhetorical formation of a “renewed” historic core epitomizes the racialized governance that intensifies the race-class realities sitting at the center of the decades-old ethnic conflict in Turkey. The central government authority's use of gentrification in practice illustrates the ethnocratic regime's spatial, political, and economic repercussions for the Kurdish population as the country's largest ethnic minority. Suriçi‘s redevelopment illustrates that ethnocratic regime practices coexist with a democratic façade and militarization activates an ethnocratic urban regime. Our findings contribute to the literature on space and power by illustrating the incompleteness and paradoxical elements of settler-colonial urbanism.