The 15-minute city concept emphasizes accessible urban living by ensuring essential services are reachable within walking or biking distance. However, most evaluations rely on two-dimensional (2D) analyses, neglecting the vertical complexity of high-density cities. This study introduces a 3D framework for assessing 15-minute accessibility in Nanjing, China. Using natural language processing and rule-based methods, we construct a 3D functional composition dataset from multi-source data. We then develop floor-level proximity indices that account for both horizontal travel time and vertical circulation (e.g., stairs, elevators). Analyzing over 90 million simulated trips, we find that accessibility generally declines with building height, though access to offices and commercial facilities improves at 20th or higher floors. Spatial inequalities emerge not only between central and peripheral zones but also across building levels and regional GDP levels, with a U-shaped disparity tied to distance from downtown. Notably, 11%–17% of trips considered accessible in 2D analyses exceed the 15-minute threshold when vertical travel is included. Our findings highlight the need to incorporate vertical space in 15-minute city evaluations and offer a scalable method to support inclusive, fair, and livable 3D urban planning with the background of 15-minute city.

Urban environments are increasingly recognised for their potential to support psychological restoration, yet most studies assess green and grey spaces in isolation and rely on static, lab-based measures. This study introduces a multi-layered analytical framework that integrates experimental walking, momentary perception tracking, and machine learning to investigate how multisensory urban features shape restoration. Conducted on a university campus, the experiment exposed 20 participants to sequential grey–green–grey walking routes. Restoration was measured through pre/post psychometric surveys, heart rate variability (HRV), and minute-level micro-surveys during walking. Results reveal three key insights: (1) green exposure induces a short-term “inoculation effect”, with restorative benefits persisting even after re-entering grey environments; (2) visual features emerged as the most influential predictors of restoration, followed by noise and microclimate; and (3) solar irradiance — when balanced with moderate temperature and humidity — positively contributing to relaxation and stress reduction. Beyond experiments, we simulated design interventions on low-restoration scenarios using a large language model to enhance visual attributes, followed by predictive evaluation via machine learning. These simulations showed measurable improvements in predicted restoration, validating a data-driven approach for environmental optimisation. This research contributes to neurourbanism by bridging spatial sensing, physiological feedback, and AI-driven interpretation. It offers practical guidance for creating psychologically supportive urban environments — such as prioritising early green exposure and mitigating noise pollution — and introduces a replicable pipeline for evaluating restorative potential in future urban design.

Urban street environments are vital to supporting human activity in public spaces. The emergence of big data, such as street view images (SVI) combined with multi-modal large language models (MLLM), is transforming how researchers and practitioners investigate, measure, and evaluate semantic and visual elements of urban environments. Considering the low threshold for creating automated evaluative workflows using MLLM, it is crucial to explore both the risks and opportunities associated with these probabilistic models. In particular, the extent to which the integration of expert knowledge can influence the performance of MLLM in the evaluation of the quality of urban design has not been fully explored. This study set out an initial exploration of how integrating more formal and structured representations of expert urban design knowledge (e.g., formal quantifiers and descriptions from existing methods) into the input prompts of an MLLM (ChatGPT-4) can enhance the model’s capability and reliability to evaluate the walkability of built environments using SVIs. We collect walkability metrics through the existing literature and categorise them using relevant ontologies. Then we select a subset of these metrics, used for assessing the subthemes of pedestrian safety and attractiveness, and develop prompts for MLLMs accordingly. We analyse MLLM’s abilities to evaluate SVI walkability subthemes through prompts with multiple levels of clarity and specificity about evaluation criteria. Our experiments demonstrate that MLLMs are capable of providing assessments and interpretations based on general knowledge and can support the automation of imagetext multimodal evaluations. However, they generally provide more optimistic scores and can make mistakes when interpreting the provided metrics, resulting in incorrect evaluations. By integrating expert knowledge, MLLM’s evaluative performance exhibits higher consistency and concentration. Therefore, this paper highlights the importance of formally and effectively integrating domain knowledge into MLLMs for evaluating urban design quality.

The rapid urbanization of cities presents significant sustainability challenges, necessitating big data and digital tools as solutions for efficient resource management. A key advancement in this area is the Urban Digital Twin (UDT). UDTs aim to create dynamic virtual replicas of urban environments, enabling informed decision-making for city planners and policymakers. UDTs enable predictive modeling, resource optimization, and impact assessment of urban interventions. On the other hand, one of the globally accepted sustainable development goals (SDGs) to achieve by 2030 is SDG 11, which focuses specifically on “Sustainable Cities and Communities”. SDGs and SDG 11 consider the cities as a system that consists of the physical urban environment and social dynamics coming from governance, citizens and communities. However, current research on UDTs has primarily focused on technical aspects, leaving the potential of UDTs to support SDG 11 and its social dynamics underexplored. This study aims to understand whether UDTs can support the realization of SDG 11. Therefore, we explore how the capabilities of UDTs, such as monitoring, modelling and simulation, visualization, information provision and collection can support the SDG 11 principles of managing interconnected targets, inclusivity, multi-stakeholder collaboration, and monitoring of SDG 11 targets. We propose a socio-technical framework illustrating how UDTs can support SDG 11 and outline the key social and technical challenges to be addressed to fully realize UDTs’ potential. Finally, we discuss the conclusions and outlook for overcoming such challenges.

Adopting 3D City Index, a comprehensive 3D data scoring framework encompassing four categories—data portals, model descriptions, thematic content, and semantic information, we assess and benchmark currently available 3D city models made accessible openly by governments worldwide. The 2025 update, including 47 datasets, reveals both the current situation and advancements in the open 3D data landscape since the previous benchmark 3 years ago. The heterogeneous landscape continues, with European cities demonstrating sustained progress, such as the datasets of Helsinki and Espoo. Japan as a country, performs well in the large-scale availability of 3D geoinformation. The trend analysis between 2022 and 2025 highlights measurable progress in the development of open 3D city models. Among the 28 datasets assessed in both years, 11 models show improvement, with an average increase of 2.5 points. While in general there is an improvement, many aspects declined, such as data portals and semantic richness. Further, the analysis implies an emerging trend toward large-scale harmonised initiatives at the state or national level, such as the PLATEAU project in Japan and Digital Twin Victoria in Australia. Such efforts indicate promise for standardised modelling, interoperability, and collaboration between governments, companies and research institutions. Understanding the current status and development of 3D city models, this work aims to inform improvements of 3D geoinformation and support broader adoption of 3D city models in research and practice.