Urban region profiling, the task of characterizing geographical areas, is crucial for urban planning and resource allocation. However, existing research in this domain faces two significant limitations. First, most methods are confined to single-task prediction, failing to capture the interconnected, multi-faceted nature of urban environments where numerous indicators are deeply correlated. Second, the field lacks a standardized experimental benchmark, which severely impedes fair comparison and reproducible progress. To address these challenges, we first establish a comprehensive benchmark for multi-task urban region profiling, featuring multi-modal features and a diverse set of strong baselines to ensure a fair and rigorous evaluation environment. Concurrently, we propose UrbanMoE, the first sparse multi-modal, multi-expert framework specifically architected to solve the multi-task challenge. Leveraging a sparse Mixture-of-Experts architecture, it dynamically routes multi-modal features to specialized sub-networks, enabling the simultaneous prediction of diverse urban indicators. We conduct extensive experiments on three real-world datasets within our benchmark, where UrbanMoE consistently demonstrates superior performance over all baselines. Further in-depth analysis validates the efficacy and efficiency of our approach, setting a new state-of-the-art and providing the community with a valuable tool for future research in urban analytics
Peng-Kai Hung, Janet Yi-Ching Huang, Rung-Huei Liang
et al.
Play is pivotal in fostering the emotional, social, and cultural dimensions of urban spaces. While generative AI (GAI) potentially supports playful urban interaction, a balanced and critical approach to the design opportunities and challenges is needed. This work develops iWonder, an image-to-image GAI tool engaging fourteen designers in urban explorations to identify GAI's playful features and create design ideas. Fourteen citizens then evaluated these ideas, providing expectations and critical concerns from a bottom-up perspective. Our findings reveal the dynamic interplay between users, GAI, and urban contexts, highlighting GAI's potential to facilitate playful urban experiences through generative agency, meaningful unpredictability, social performativity, and the associated offensive qualities. We propose design considerations to address citizen concerns and the `tourist metaphor' to deepen our understanding of GAI's impact, offering insights to enhance cities' socio-cultural fabric. Overall, this research contributes to the effort to harness GAI's capabilities for urban enrichment.
Accurate and fast urban noise prediction is pivotal for public health and for regulatory workflows in cities, where the Environmental Noise Directive mandates regular strategic noise maps and action plans, often needed in permission workflows, right-of-way allocation, and construction scheduling. Physics-based solvers are too slow for such time-critical, iterative "what-if" studies. We evaluate conditional Normalizing Flows (Full-Glow) for generating for generating standards-compliant urban sound-pressure maps from 2D urban layouts in real time per 256x256 map on a single RTX 4090), enabling interactive exploration directly on commodity hardware. On datasets covering Baseline, Diffraction, and Reflection regimes, our model accelerates map generation by >2000 times over a reference solver while improving NLoS accuracy by up to 24% versus prior deep models; in Baseline NLoS we reach 0.65 dB MAE with high structural fidelity. The model reproduces diffraction and interference patterns and supports instant recomputation under source or geometry changes, making it a practical engine for urban planning, compliance mapping, and operations (e.g., temporary road closures, night-work variance assessments).
Deep learning-based surrogate models offer a computationally efficient alternative to high-fidelity computational fluid dynamics (CFD) simulations for predicting urban wind flow. However, conventional approaches usually only yield low-frequency predictions (essentially averaging values from proximate pixels), missing critical high-frequency details such as sharp gradients and peak wind speeds. This study proposes a hierarchical approach for accurately predicting pedestrian-level urban winds, which adopts a two-stage predictor-refiner framework. In the first stage, a U-Net architecture generates a baseline prediction from urban geometry. In the second stage, a conditional Generative Adversarial Network (cGAN) refines this baseline by restoring the missing high-frequency content. The cGAN's generator incorporates a multi-scale architecture with stepwise kernel sizes, enabling simultaneous learning of global flow structures and fine-grained local features. Trained and validated on the UrbanTALES dataset with comprehensive urban configurations, the proposed hierarchical framework significantly outperforms the baseline predictor. With a marked qualitative improvement in resolving high-speed wind jets and complex turbulent wakes as well as wind statistics, the results yield quantitative enhancement in prediction accuracy (e.g., RMSE reduced by 76% for the training set and 60% for the validation set). This work presents an effective and robust methodology for enhancing the prediction fidelity of surrogate models in urban planning, pedestrian comfort assessment, and wind safety analysis. The proposed model will be integrated into an interactive web platform as Feilian Version 2.
The sustainable restoration of intermittent streams has become a critical priority in contemporary urban planning, particularly as cities confront the dual challenges of ecological degradation and climate change. In Tehran, decades of rapid urbanization and poor management practices have confined natural streams into rigid concrete channels, eroding their ecological value and disconnecting them from community life. This paper introduces an ecological and sustainability-oriented framework for the restoration of the Darband and Darabad river valleys, highlighting their potential to function as ecological corridors that support biodiversity, thermal regulation, cultural identity, and urban resilience. The study employs a systematic methodology that integrates ecological engineering, landscape design, hydrological modeling, and participatory planning. Findings suggest that restoring these river valleys through sustainable strategies, such as the creation of active green networks, multifunctional public spaces, and resilient hydrological systems, can transform them from degraded drainage corridors into life-giving urban landscapes. Moreover, the research emphasizes the necessity of linking restoration with sustainability goals to ensure long-term ecological balance, social well-being, and climate adaptation. This case study demonstrates that sustainable river restoration, when aligned with ecological design and community engagement, has the potential to reposition intermittent streams as essential infrastructures for sustainable urban development and resilience.
Urban analytics increasingly relies on AI-driven trajectory analysis, yet current approaches suffer from methodological fragmentation: trajectory learning captures movement patterns but ignores spatial context, while spatial embedding methods encode street networks but miss temporal dynamics. Three gaps persist: (1) lack of joint training that integrates spatial and temporal representations, (2) origin-agnostic treatment that ignores directional asymmetries in navigation ($A \to B \ne B \to A$), and (3) over-reliance on auxiliary data (POIs, imagery) rather than fundamental geometric properties of urban space. We introduce a conditional trajectory encoder that jointly learns spatial and movement representations while preserving origin-dependent asymmetries using geometric features. This framework decomposes urban navigation into shared cognitive patterns and origin-specific spatial narratives, enabling quantitative measurement of cognitive asymmetries across starting locations. Our bidirectional LSTM processes visibility ratio and curvature features conditioned on learnable origin embeddings, decomposing representations into shared urban patterns and origin-specific signatures through contrastive learning. Results from six synthetic cities and real-world validation on Beijing's Xicheng District demonstrate that urban morphology creates systematic cognitive inequalities. This provides urban planners quantitative tools for assessing experiential equity, offers architects insights into layout decisions' cognitive impacts, and enables origin-aware analytics for navigation systems.
As incubators of innovation, towns and cities have always played a key role in bringing about change and creating surprises. From the Renaissance and Industrial Revolution to the classical Greek and Roman civilizations, comparable occurrences took place in the ancient civilizations of Mesopotamia, the Nile, and the Indus Valley. Cities and towns serve as catalysts for the region's industrialization and modernization as well as for changes in people's lifestyles. In light of the most recent ideas in urban development, the paper supports the importance and necessity of using creative methods in the creation and execution of public policies pertaining to urban renewal. The provisions of each of the selected concepts are analysed in order to present the pertinent ideas for the innovation policy of revitalizing cities. It is concluded that there isn't a single city that has only adopted one of the most recent ideas for urban development. In actuality, we witness the multi-vectorality and amalgamation of urban strategies that eventually supplanted conventional sectoral perspectives. Transforming dilapidated neighborhoods into urban settings that support human life and activities in accordance with the requirements and preferences of their residents is the aim of innovative methods to urban rehabilitation strategy. Both the long-term synergistic impacts on the city as a whole and the impact on a particular degraded area should be taken into account when considering revitalization. It was discovered that human-oriented programs of sustainable area redevelopment lay the groundwork for creative urban revitalization initiatives. The programs are predicated on considering a range of ideas and integrating several activities, particularly: establishment of a small, multipurpose space.
By 2050, nearly 68% of the global population will reside in urban areas, while 1.6 billion people already inhabit informal settlements lacking tenure security, basic services, and public space. This study explores whether vertically integrated community spaces can enable medium- to high-rise slum upgrading in Bangkok, where land scarcity constrains conventional low-rise approaches. A research-by-design methodology, conducted through a postgraduate studio in collaboration with a local community, informs the investigation. Spatial analysis, mapping, and participatory processes guided the development of modular walk-up block proposals, featuring stacked semi-public ‘streets-in-the-sky’ and compact communal spaces. These configurations offer vertical social interaction zones, economic potential via shophouses above ground level, and environmental benefits through enhanced daylight penetration and cross ventilation. Findings indicate an improved sense of belonging, social cohesion, and place identity despite vertical displacement. Vertically shared spaces demonstrate capacity to align high-density urban forms with evolving informal practices, providing a replicable, climate-responsive model for inclusive regeneration in rapidly urbanising contexts across the Global South.
Heritage conservation in Alexandria demands integrative, data-driven approaches that reconcile preservation efforts with satisfactory visitor access. This study investigates how Geographic Information Systems (GIS) can document, evaluate, and spatially optimise the city’s cultural heritage. A four-stage framework was applied: (1) compiling a multi-source geodatabase of 294 heritage assets in addition to transport nodes; (2) digitising attributes for value scoring based on five National Organisation of Urban Harmony (NOUH) criteria; (3) conducting spatial analytics—hot-spot, nearest-neighbour, buffer, and network analysis—together with a six-parameter walkability index; and (4) translating findings into policy-relevant interventions and interactive web maps. Results reveal pronounced clustering in the historic downtown;however 83 high-value assets lie outside a 400 m walk from transit, notably in Foad Street, Kafr-Abdo, and Ancient Catacombs sub-areas. Proposed measures—two bus-stop extensions, and one new tram halt would reduce unserved sites to 8.5 per cent. Six optimised cultural routes cut average walking time within heritage clusters to maximise exposure to unique assets. A dashboard links routes, heritage metadata, and multimodal travel options as well as enabling user-defined preference customisation. The research demonstrates GIS’s capacity to integrate qualitative heritage evaluation with quantitative mobility analytics, offering a transferable model for sustainable, economically beneficial urban heritage management.
Abdullah Abu Zaid, Baha Eddine Youcef Belmekki, Mohamed-Slim Alouini
As urban air mobility (UAM) emerges as a transformative solution to urban transportation, the demand for robust communication frameworks capable of supporting high-density aerial traffic becomes increasingly critical. An essential area of communications improvement is reliably characterizing and minimizing interference on UAM aircraft from other aircraft and ground vehicles. To achieve this, accurate line-of-sight (LOS) models must be used. In this work, we highlight the limitations of a LOS probability model extensively used in the literature in accurately predicting interference caused by ground vehicles. Then, we introduce a modified probability of LOS model that improves interference prediction by incorporating the urban topography and the dynamic positioning of ground vehicles on streets. Our model's parameters are derived from extensive simulations and validated through real-world urban settings to ensure reliability and applicability.
Urbanization as a global trend has led to many environmental challenges, including the urban heat island (UHI) effect. The increase in temperature has a significant impact on the well-being of urban residents. Air temperature ($T_a$) at 2m above the surface is a key indicator of the UHI effect. How land use land cover (LULC) affects $T_a$ is a critical research question which requires high-resolution (HR) $T_a$ data at neighborhood scale. However, weather stations providing $T_a$ measurements are sparsely distributed e.g. more than 10km apart; and numerical models are impractically slow and computationally expensive. In this work, we propose a novel method to predict HR $T_a$ at 100m ground separation distance (gsd) using land surface temperature (LST) and other LULC related features which can be easily obtained from satellite imagery. Our method leverages diffusion models for the first time to generate accurate and visually realistic HR $T_a$ maps, which outperforms prior methods. We pave the way for meteorological research using computer vision techniques by providing a dataset of an extended spatial and temporal coverage, and a high spatial resolution as a benchmark for future research. Furthermore, we show that our model can be applied to urban planning by simulating the impact of different urban designs on $T_a$.
The development of smart cities requires innovative sensing solutions for efficient and low-cost urban environment monitoring. Bike-sharing systems, with their wide coverage, flexible mobility, and dense urban distribution, present a promising platform for pervasive sensing. At a relative early stage, research on bike-based sensing focuses on the application of data collected via passive sensing, without consideration of the optimization of data collection through sensor deployment or vehicle scheduling. To address this gap, this study integrates a binomial probability model with a mixed-integer linear programming model to optimize sensor allocation across bike stands. Additionally, an active scheduling strategy guides user bike selection to enhance the efficacy of data collection. A case study in Manhattan validates the proposed strategy, showing that equipping sensors on just 1\% of the bikes covers approximately 70\% of road segments in a day, highlighting the significant potential of bike-sharing systems for urban sensing.
Industrial parks are critical to urban economic growth. Yet, their development often encounters challenges stemming from imbalances between industrial requirements and urban services, underscoring the need for strategic planning and operations. This paper introduces IndustryScopeKG, a pioneering large-scale multi-modal, multi-level industrial park knowledge graph, which integrates diverse urban data including street views, corporate, socio-economic, and geospatial information, capturing the complex relationships and semantics within industrial parks. Alongside this, we present the IndustryScopeGPT framework, which leverages Large Language Models (LLMs) with Monte Carlo Tree Search to enhance tool-augmented reasoning and decision-making in Industrial Park Planning and Operation (IPPO). Our work significantly improves site recommendation and functional planning, demonstrating the potential of combining LLMs with structured datasets to advance industrial park management. This approach sets a new benchmark for intelligent IPPO research and lays a robust foundation for advancing urban industrial development. The dataset and related code are available at https://github.com/Tongji-KGLLM/IndustryScope.
This study examines users’ socio-spatial behaviour in promoting sustainable retail district projects in Bahrain. It evaluates how customer behaviour and movement impact the social sustainability of contemporary open-space shopping districts. By employing qualitative methods such as questionnaires, on-site observations, and expert interviews, this study investigates factors influencing consumer preferences and attractions to shopping districts. Findings reveal that the vitality of shopping districts is closely linked to meeting users’ needs, which fosters social sustainability. The study identifies key elements for a framework that can integrate social sustainability measures into shopping district designs. This framework aims to support stakeholders and designers in planning sustainable commercial projects in Bahrain, ensuring long-term success and vitality. Research highlights the importance of considering socio-spatial interactions in retail environments to enhance livability and user experience. By understanding these dynamics, designers can create retail districts that not only attract consumers but also contribute to the well-being and sustainability of the community. Insights gained from this study can guide the development of future retail projects, emphasizing the significance of socio-spatial behaviour in achieving sustainable urban design and planning.
Accessibility is critical in achieving sustainable and equitable transport systems. This study investigates the accessibility of older adults, a group that has received little attention in the accessibility literature yet faces significant mobility barriers. Following qualitative approaches, including interviews and open-ended questionnaires, and employing the Accessibility as a Capability framework (AaaC) and Transport-Related Social Exclusion (TRSE) dimensions, the study analyses the opportunities and barriers older adults face in accessing transport systems in six wards of South Manchester. Results reveal the crucial role of individual abilities and perceptions in converting resources into access. Analysis of travel experiences showed that even when accessibility appears high, older adults may still expend a significant amount of effort to gain accessibility, particularly through emotional ‘work’ caused by transport-related fear and stress. The findings suggest a focus on the subjective aspects of travel to remove barriers to accessibility and improve access to valued capabilities while creating more equitable and sustainable transport systems.
With the arrival of modernity in Iran, the concept of the "city" as we understand it today was born, undergoing significant transformations that have continued to evolve over time. These transformations in the physical structure of the city have created multifaceted challenges in three primary dimensions: housing, urban facilities, and transportation. This research, adopting a thematic approach to these challenges and aiming to provide valuable insights for social policymakers and urban planners, employs a descriptive-analytical method. It leverages existing data from documentary resources to explore opportunities for land use change in the process of redeveloping urban fabrics. The study delves deeply into the role of urban transformation in enhancing the quality of life and improving the efficiency of urban spaces. By investigating urban fabrics as crucial components in the redevelopment process, the research analyzes the various factors influencing land use change. Through detailed case studies, the article identifies existing opportunities for the improvement and modification of land uses within urban fabrics, offering practical solutions for optimizing these processes. The ultimate goal of this study is to propose comprehensive strategies for the optimal utilization of urban resources, thereby significantly enhancing the quality of life in urban environments. This study aims to provide a thorough understanding of the dynamics at play in urban redevelopment and to offer actionable recommendations for policymakers and urban planners to create more livable, efficient, and sustainable urban spaces.
An increasing number of related urban data sources have brought forth novel opportunities for learning urban region representations, i.e., embeddings. The embeddings describe latent features of urban regions and enable discovering similar regions for urban planning applications. Existing methods learn an embedding for a region using every different type of region feature data, and subsequently fuse all learned embeddings of a region to generate a unified region embedding. However, these studies often overlook the significance of the fusion process. The typical fusion methods rely on simple aggregation, such as summation and concatenation, thereby disregarding correlations within the fused region embeddings. To address this limitation, we propose a novel model named HAFusion. Our model is powered by a dual-feature attentive fusion module named DAFusion, which fuses embeddings from different region features to learn higher-order correlations between the regions as well as between the different types of region features. DAFusion is generic - it can be integrated into existing models to enhance their fusion process. Further, motivated by the effective fusion capability of an attentive module, we propose a hybrid attentive feature learning module named HALearning to enhance the embedding learning from each individual type of region features. Extensive experiments on three real-world datasets demonstrate that our model HAFusion outperforms state-of-the-art methods across three different prediction tasks. Using our learned region embedding leads to consistent and up to 31% improvements in the prediction accuracy.
We present the UrbanBIS benchmark for large-scale 3D urban understanding, supporting practical urban-level semantic and building-level instance segmentation. UrbanBIS comprises six real urban scenes, with 2.5 billion points, covering a vast area of 10.78 square kilometers and 3,370 buildings, captured by 113,346 views of aerial photogrammetry. Particularly, UrbanBIS provides not only semantic-level annotations on a rich set of urban objects, including buildings, vehicles, vegetation, roads, and bridges, but also instance-level annotations on the buildings. Further, UrbanBIS is the first 3D dataset that introduces fine-grained building sub-categories, considering a wide variety of shapes for different building types. Besides, we propose B-Seg, a building instance segmentation method to establish UrbanBIS. B-Seg adopts an end-to-end framework with a simple yet effective strategy for handling large-scale point clouds. Compared with mainstream methods, B-Seg achieves better accuracy with faster inference speed on UrbanBIS. In addition to the carefully-annotated point clouds, UrbanBIS provides high-resolution aerial-acquisition photos and high-quality large-scale 3D reconstruction models, which shall facilitate a wide range of studies such as multi-view stereo, urban LOD generation, aerial path planning, autonomous navigation, road network extraction, and so on, thus serving as an important platform for many intelligent city applications.