Hasil untuk "Urban renewal. Urban redevelopment"

Jie Zhang, Xingtong Yu, Yuan Fang et al.

Learning transferable multimodal embeddings for urban environments is challenging because urban understanding is inherently spatial, yet existing datasets and benchmarks lack explicit alignment between street-view images and urban structure. We introduce UGData, a spatially grounded dataset that anchors street-view images to structured spatial graphs and provides graph-aligned supervision via spatial reasoning paths and spatial context captions, exposing distance, directionality, connectivity, and neighborhood context beyond image content. Building on UGData, we propose UGE, a two-stage training strategy that progressively and stably aligns images, text, and spatial structures by combining instruction-guided contrastive learning with graph-based spatial encoding. We finally introduce UGBench, a comprehensive benchmark to evaluate how spatially grounded embeddings support diverse urban understanding tasks -- including geolocation ranking, image retrieval, urban perception, and spatial grounding. We develop UGE on multiple state-of-the-art VLM backbones, including Qwen2-VL, Qwen2.5-VL, Phi-3-Vision, and LLaVA1.6-Mistral, and train fixed-dimensional spatial embeddings with LoRA tuning. UGE built upon Qwen2.5-VL-7B backbone achieves up to 44% improvement in image retrieval and 30% in geolocation ranking on training cities, and over 30% and 22% gains respectively on held-out cities, demonstrating the effectiveness of explicit spatial grounding for spatially intensive urban tasks.

Ulysse Marquis, Oriol Artime, Riccardo Gallotti et al.

Urban sprawl reshapes cities, yet its quantitative laws remain elusive. Analyzing built-up expansion in 19 cities (1985-2015) with tools from surface growth physics in radial geometry, we reveal anisotropic, branch-like growth and a piecewise linear scaling between area and population. We uncover a robust local roughness exponent $α_{\text{loc}}\approx 0.54$, coexisting with variable $β$ and $z$. This unusual coexistence of universal and variable exponents offers a rare empirical testbed for nonequilibrium growth and an empirical basis for modeling urban sprawl.

Alex Richardson, Jonathan Sprinkle

Digital twins of urban environments play a critical role in advancing autonomous vehicle (AV) research by enabling simulation, validation, and integration with emerging generative world models. While existing tools have demonstrated value, many publicly available solutions are tightly coupled to specific simulators, difficult to extend, or introduce significant technical overhead. For example, CARLA-the most widely used open-source AV simulator-provides a digital twin framework implemented entirely as an Unreal Engine C++ plugin, limiting flexibility and rapid prototyping. In this work, we propose OpenTwinMap, an open-source, Python-based framework for generating high-fidelity 3D urban digital twins. The completed framework will ingest LiDAR scans and OpenStreetMap (OSM) data to produce semantically segmented static environment assets, including road networks, terrain, and urban structures, which can be exported into Unreal Engine for AV simulation. OpenTwinMap emphasizes extensibility and parallelization, lowering the barrier for researchers to adapt and scale the pipeline to diverse urban contexts. We describe the current capabilities of the OpenTwinMap, which includes preprocessing of OSM and LiDAR data, basic road mesh and terrain generation, and preliminary support for CARLA integration.

Hui Zhong, Qing-Long Lu, Qiming Zhang et al.

Urban sensing is essential for the development of smart cities, enabling monitoring, computing, and decision-making for urban management.Thanks to the advent of vehicle technologies, modern vehicles are transforming from solely mobility tools to valuable sensors for urban data collection, and hold the potential of improving traffic congestion, transport sustainability, and infrastructure inspection.Vehicle-based sensing is increasingly recognized as a promising technology due to its flexibility, cost-effectiveness, and extensive spatiotemporal coverage. However, optimizing sensing strategies to balance spatial and temporal coverage, minimize redundancy, and address budget constraints remains a key challenge.This study proposes an adaptive framework for enhancing the sensing utility of sensor-equipped vehicles.By integrating heterogeneous open-source data, the framework leverages spatiotemporal weighting to optimize vehicle selection and sensing coverage across various urban contexts.An entropy-based vehicle selection strategy, \texttt{Improved OptiFleet}, is developed to maximize sensing utility while minimizing redundancy.The framework is validated using real-world air quality data from 320 sensor-equipped vehicles operating in Guangzhou, China, over two months.Key findings show that the proposed method outperforms baseline strategies, providing up to 5\% higher sensing utility with reduced fleet sizes, and also highlights the critical role of dynamic urban data in optimizing mobile sensing strategies.

Tatsuro Banno, Mizuki Takenawa, Leslie Wöhler et al.

We introduce a novel urban visualization system that integrates 3D urban model (CityGML) and 360° walkthrough videos. By aligning the videos with the model and dynamically projecting relevant video frames onto the geometries, our system creates photorealistic urban visualizations, allowing users to intuitively interpret geospatial data from a pedestrian view.

Tianhui Liu, Hetian Pang, Xin Zhang et al.

Understanding urban socioeconomic conditions through visual data is a challenging yet essential task for sustainable urban development and policy planning. In this work, we introduce \textit{CityLens}, a comprehensive benchmark designed to evaluate the capabilities of Large Vision-Language Models (LVLMs) in predicting socioeconomic indicators from satellite and street view imagery. We construct a multi-modal dataset covering a total of 17 globally distributed cities, spanning 6 key domains: economy, education, crime, transport, health, and environment, reflecting the multifaceted nature of urban life. Based on this dataset, we define 11 prediction tasks and utilize 3 evaluation paradigms: Direct Metric Prediction, Normalized Metric Estimation, and Feature-Based Regression. We benchmark 17 state-of-the-art LVLMs across these tasks. These make CityLens the most extensive socioeconomic benchmark to date in terms of geographic coverage, indicator diversity, and model scale. Our results reveal that while LVLMs demonstrate promising perceptual and reasoning capabilities, they still exhibit limitations in predicting urban socioeconomic indicators. CityLens provides a unified framework for diagnosing these limitations and guiding future efforts in using LVLMs to understand and predict urban socioeconomic patterns. The code and data are available at https://github.com/tsinghua-fib-lab/CityLens.

Syeda Rizwana, Tahmina Rahman, S M Ehsan Ul Haque Shawpnil

Dhaka’s urban landscape is undergoing rapid transformation with the introduction of the Metro Rail, reshaping mobility patterns and influencing informal economies. This study investigates how the establishment of Farmgate and Mirpur-10 metro stations has affected the livelihoods of informal vendors, applying the Sustainable Livelihood Framework (SLF) as an analytical lens. A mixed-method research design was adopted, combining structured questionnaires, semi-structured interviews, field observations, and photographic documentation with statistical analyses. Fifty vendors were surveyed across the two sites, and independent sample t-tests were performed to compare livelihood outcomes. Results indicate that vendors who relocated their vending spaces after the opening of metro stations achieved significantly higher SLF scores (p < .01), reflecting improved access to financial and social capital. In contrast, vendors who started business after the metro inauguration reported comparatively lower livelihood scores, suggesting vulnerabilities linked to competition, limited infrastructure, and regulatory constraints. Findings underscore the dual role of transport infrastructure as both an enabler of opportunity and a source of precarity for informal workers. The study highlights the necessity of inclusive urban policies, particularly through designated vending zones and supportive planning strategies, to enhance resilience and ensure equitable benefits from infrastructure-led urban transformation.

حسن بخشی زاده

مسأله و هدف پژوهش حاضر، تبیین عوامل اجتماعی تطور و توسعه شهر تهران با تأکید بر مؤلفه‌های شهر اسلامی در دوره‌های قاجاریه و پهلوی (1304-1321) است که طبق روش تحقیق کیفی از تحلیل تاریخی و تحلیل هرمنوتیک تفسیری به موازات یکدیگر بهره گرفته شده است. طبق یافته‌های تحقیق، مهم‌ترین و بنیادی‌ترین تغییرات اجتماعی تهران در دوره قاجاریه تجربه شده، دورانی که جمعیت شهر به سه برابر می‌رسد و تغییرات اجتماعی (نظیر تغییر الگوی زندگی ساکنان و هجوم عناصر و نمادهای غربی) منجر به تغییرات معماری و شکل‌گیری سبکی به نام سبک تهران در عین و ذهن افراد جامعه می‌گردد. از سوی دیگر، درپی سیاست‌های تجددگرایی سال‌های 1304-1321، شاهد شدت گرفتن ورود فرهنگ غربی به شکل بی‌سابقه‌ای در تهران هستیم. درواقع، مهمترین تغییرات اجتماعی و مواجه و رویارویی با ناشناخته‌ترین مسائل شهری در بین مردم، در این دوره در شهر تهران به عنوان یک شهر اسلامی (و یک واقعیت اجتماعی) بروز و نمود پیدا می‌کند. طبق نتایج تحقیق، ایجاد و تحول عوامل اجتماعی شهر تهران، با تأثیر گذاری عاملیت‌ها و عوامل سیاسی (داخلی و خارجی) و تعامل یکطرفه افراد در شهر با این ساختارها، بر مهمترین معیار‌ها و مؤلفه‌های شهر اسلامی نظیر نظم (برهم ریختن نظم اجتماعی سنتی و ارائه نظم جدید و ناآگاه)، کالبد اسلامی (اقتباس از معیار‌های غربی و نادیده گرفتن معیار‌های اسلامی و بومی) و وجود هویت توحیدی (چالش هویت سنتی-مذهبی با هویت مدرن و متکثر) به پیش رفته است و اجتماع شهری را با چالش‌های متعدد خرد و کلان مواجه کرده است.

Xu Xiang, Wei Shang, Wendi Huang

Against the backdrop of a global shift in urban renewal from large-scale redevelopment towards people-centered quality enhancement and evidence-based governance, accelerated population aging presents unprecedented challenges for global urban renewal, particularly in China’s unit-based communities where over 40% of urban elderly reside. This study integrates electroencephalography (EEG) and eye-tracking technologies to investigate the neural and behavioral responses of older adults to public spaces in Wuhan’s unit-based communities. We classified spaces into five functional types based on Maslow’s hierarchy of needs (physiological, safety, social, esteem, self-actualization) and established a tri-level cleanliness protocol (high/medium/low). Linear mixed models revealed that environmental cleanliness significantly predicted neural stress, indexed by the EEG β/α ratio, with low-cleanliness spaces eliciting at least 2% higher β/α values than high-cleanliness environments. Conversely, spatial function type had significant effect. Eye-tracking heatmaps and fixation counts identified ground pavement, vehicles, and disorderly objects as primary visual attractors, indicating heightened attention to utilitarian and chaotic elements. These findings underscore that optimizing environmental cleanliness—rather than spatial functionality alone—is critical for reducing cognitive load in age-friendly community renewal. This study advances a “safety-function-psychological comfort” framework for age-friendly design, offering neuro-urbanistic evidence for global aging cities.

Nor Suzylah Sohaimi, Muhammad Hafiz Abd Razak, Mohd Syahril Said et al.

Rapid urbanisation across Southeast Asia intensifies the demand for housing that is simultaneously affordable, sustainable, and socially inclusive. This study investigates how smart technologies—Artificial Intelligence, Internet of Things devices (IoT), Building Information Modelling, and passive cooling innovations—can advance socioeconomic sustainability in urban housing. A three-phase methodology combined a scientometric analysis of 454 Scopus-indexed papers, a systematic literature review of eight rigorously screened studies, and a qualitative content analysis of practice-based sources. The scientometric mapping reveals growing scholarly attention to energy efficiency and climate resilience, yet affordability and social equity remain peripheral themes. Evidence from Malaysia, Indonesia, the Philippines, Singapore, and Thailand shows that smart sensors, digital simulations, and value-management frameworks can reduce cooling energy by up to 18,000 kWh annually, cut construction costs, and enhance thermal comfort in low-income settings. However, adoption is uneven owing to high capital costs, limited policy incentives, and skills gaps. The study proposes an integrated framework linking environmental performance, housing affordability, and social inclusion through appropriate digital tools. Policymakers and urban planners are urged to embed financing mechanisms, capacity-building, and participatory design into housing programmes to mainstream technology-enabled, equitable sustainability across the region within the next decade.

Deepak Kumar Khadka

The Nepal Public Policy Review was launched as a multidisciplinary journal to provide an academic platform for research relevant to public policy. It has published three volumes, including a special issue arising from a symposium. Following a critical review of the journal’s policies, we identified the need for better alignment between academic research and public policy. In response, we redefined the journal’s purpose to bridge research with policy, updating the Aims and Scope to encourage collaboration between researchers and the policy community. This shift emphasizes the importance of clear and accessible communication for policymakers. To facilitate this, we introduced new manuscript requirements, including sections for Policy Recommendations and Suggested Course of Action to translate research findings into actionable policy steps. We also introduced an initial formative editorial assessment for manuscripts before peer review. This aimed to enrich the research and ensure its alignment with relevant policies. Piloted in Volume 4, the manuscript improvement initiative successfully enhanced five articles by translating their conclusions into detailed policy recommendations. Positive feedback from authors and reviewers affirmed the success of this approach. We consider the manuscript improvement a success and will expand it in future volumes, along with exploring broader mentoring for both academics and policy professionals.

Houssam Razouk, Michael Leitner, Roman Kern

Urban blight is a problem of high interest for planning and policy making. Researchers frequently propose theories about the relationships between urban blight indicators, focusing on relationships reflecting causality. In this paper, we improve on the integration of domain knowledge in the analysis of urban blight by introducing four rules for effective modeling of causal domain knowledge. The findings of this study reveal significant deviation from causal modeling guidelines by investigating cognitive maps developed for urban blight analysis. These findings provide valuable insights that will inform future work on urban blight, ultimately enhancing our understanding of urban blight complex interactions.

Di Mo, Keyi Liu, Qi Tian et al.

This study explores the application of Artificial Intelligence Generated Content (AIGC) technology in urban planning and design, with a particular focus on its impact on placemaking and public participation. By utilizing natural language pro-cessing and image generation models such as Stable Diffusion, AIGC enables efficient transformation from textual descriptions to visual representations, advancing the visualization of urban spatial experiences. The research examines the evolving role of designers in participatory planning processes, specifically how AIGC facilitates their transition from traditional creators to collaborators and facilitators, and the implications of this shift on the effectiveness of public engagement. Through experimental evaluation, the study assesses the de-sign quality of urban pocket gardens generated under varying levels of designer involvement, analyzing the influence of de-signers on the aesthetic quality and contextual relevance of AIGC outputs. The findings reveal that designers significantly improve the quality of AIGC-generated designs by providing guidance and structural frameworks, highlighting the substantial potential of human-AI collaboration in urban design. This research offers valuable insights into future collaborative approaches between planners and AIGC technologies, aiming to integrate technological advancements with professional practice to foster sustainable urban development.

Uttam Kumar Roy, Deeksha Sharma

Urban green spaces (UGSs) improve the environmental value of the city as well as the quality of life for citizens. Unfortunately, many cities in India are lagging behind the minimum standard of UGS required in the city. For example, Agra city in India, one of the most famous tourist destinations in the world, represents two distinct realities of UGS in the same city. One is the focus of tourism, and the rest is the place of common people with very limited public green spaces, leading to multiple social issues (like spatial polarization, etc.). The secondary data concludes that the PPGC of Agra is lower than the standards, and the ongoing schemes are approached through quantitative methods. Despite having many UGS development schemes, Agra's UGS shows stalled growth. This research examines the policies and programs of UGS development (AMRUT, SCM, etc.) and reviews them to understand the unique gaps and possible regulatory interventions. The study includes an assessment of stakeholders' readiness to accept plausible UGS strategies using an analytical analysis approach. The primary data shows that PPP is the requirement for the integrated development of UGSs. The planners can make policies highlighting citizen's rights and responsibilities to enhance UGSs in Agra.

Raghad Almashhour, Jerry Kolo, Salwa Beheiry

Cities are described as urban heat islands (UHI) due to the intensity of the heat generated by urban activities. Buildings, for example, absorb and emit heat, which contributes to urban heat. Cities contribute to global warming, which, over time, influences climate change. Cities contend with these challenges concurrently through mitigation and adaptation strategies. Through their unintended conflicts and trade-offs, the strategies may impact each other adversely. What are typologies of these trade-offs and conflicts, and how do they influence the effectiveness of UHI management by governments? To answer this research question, this paper used the desktop, case-study and evidence-based research techniques. The paper found and discussed specific conflicts and trade-offs between UHI mitigation and adaptation strategies, as well as effective integration, innovation and evaluation management mechanisms. The findings should provide actionable insights for urban policymakers and planners, on UHI management and long-term climate resilience in cities.

Rahisha Thottolil, Uttam Kumar, Tanujit Chakraborty

The rapid urbanization trend in most developing countries including India is creating a plethora of civic concerns such as loss of green space, degradation of environmental health, clean water availability, air pollution, traffic congestion leading to delays in vehicular transportation, etc. Transportation and network modeling through transportation indices have been widely used to understand transportation problems in the recent past. This necessitates predicting transportation indices to facilitate sustainable urban planning and traffic management. Recent advancements in deep learning research, in particular, Generative Adversarial Networks (GANs), and their modifications in spatial data analysis such as CityGAN, Conditional GAN, and MetroGAN have enabled urban planners to simulate hyper-realistic urban patterns. These synthetic urban universes mimic global urban patterns and evaluating their landscape structures through spatial pattern analysis can aid in comprehending landscape dynamics, thereby enhancing sustainable urban planning. This research addresses several challenges in predicting the urban transportation index for small and medium-sized Indian cities. A hybrid framework based on Kernel Ridge Regression (KRR) and CityGAN is introduced to predict transportation index using spatial indicators of human settlement patterns. This paper establishes a relationship between the transportation index and human settlement indicators and models it using KRR for the selected 503 Indian cities. The proposed hybrid pipeline, we call it RidgeGAN model, can evaluate the sustainability of urban sprawl associated with infrastructure development and transportation systems in sprawling cities. Experimental results show that the two-step pipeline approach outperforms existing benchmarks based on spatial and statistical measures.

Arnau Bayón, Daniel Valero, Mário J. Franca

Extreme floods threaten lives, assets and ecosystems, with the largest impacts occurring in urbanised areas. However, flood mitigation schemes generally neglect the fact that urban floods carry a considerable amount of solid load. In this study, we define Urban Flood Drifters (UFDs) as loose objects present in the urban landscape that can become mobile under certain flow conditions, thereafter blocking drainage infrastructure and endangering both downstream and upstream communities. Based on 270 post-flood photographic records from 63 major inundations of the past quarter-century across 46 countries, we provide a comprehensive analysis of UFDs and their flood-hazard implications. We show that a variety of vehicles, furniture and a heterogeneous mixture of drifters are present in post-flooding scenarios. Plastic, construction debris and wood (natural or anthropogenic) dominate the statistics of transported drifters in urban floods (with frequencies of roughly 50-60% each), followed by cars (present in 31.5% of post-flood images). Other heavy vehicles are readily observed in post-flood imagery and furniture such as bins, garden sheds or water tanks also appear occasionally, therefore suggesting that they can play a relevant role in extreme floods.

Yi Yang, Tetsuo Kidokoro, Fumihiko Seta et al.

The Characteristic Town (CT) program is one of the most notable strategies in China’s urbanization process in recent years, responding to the drawbacks of the past decades of crude urbanization development model and maintaining and promoting capital accumulation and economic growth with innovative approach to space production. However, no studies have been conducted to examine whether residents actually benefit from it. Therefore, we combined desk research, participatory observation, in-depth interviews, and questionnaires to evaluate its influence on residents in two representative cases in Hangzhou, Zhejiang Province, where the program originated. The results show limited improvement in public benefits: a general but insignificant improvement in the living standard of the residents; residents’ public participation is generally lacking; residents’ cognition of self-identity has begun to appear deviation, and barriers between them and foreign workers have begun to emerge; residents’ assessment of the new development strategy varies from case to case. Compared to other urban and rural redevelopment, renewal, and construction practices around the world, the CT program does not seem to appear to be overly special or advanced in terms of securing and enhancing public benefits. To this end, this study concluded that it is necessary to consider the need to adopt an official evaluation system that attaches equal importance to economic, environmental, and social factors, further strengthen the supervision of local financial expenditure, effectively strengthen the role of the public, improve infrastructure and public service facilities, and enhance the comprehensive training of indigenous people.

Mathieu Paris, Frédéric Dubois, Stéphane Bosc et al.

The research focused on simulating wind patterns in urban planning design offers substantial contributions to both the social and economic aspects of the urban planning and design field. To begin with, it addresses a critical factor in urban development, especially in Mediterranean climates, where natural ventilation significantly influences summer comfort. By incorporating predictive numerical simulations of urban wind patterns, this study provides valuable insights into improving outdoor thermal comfort within urban areas. This holds particular importance in the context of adapting to climate change, as it equips urban planners and architects with informed decision-making tools to create more sustainable and comfortable urban environments. Additionally, this research makes an economic contribution by presenting guidelines for iterative wind simulations in the early stages of designing medium-scale urban projects. Through the validation of a simulation workflow, it streamlines the design process, potentially reducing the time and resources required for urban planning and architectural design. This enhanced efficiency can result in cost savings during project development. Moreover, the study's recommendations concerning simulation parameters, such as wind tunnel cell size and refinement levels, offer practical insights for optimizing simulation processes, potentially lowering computational expenses and improving the overall economic viability of urban design projects. To summarize, this research effectively addresses climate-related challenges, benefiting both social well-being and economic efficiency in the field of urban planning and design, while also providing guidance for more efficient simulation-driven design procedures.