High-density urban environments present competing environmental exposures that jointly influence human health. Increased sky openness enhances visual quality but simultaneously intensifies thermal stress, while shading strategies that reduce heat exposure may constrain restorative benefits. Such trade-offs pose challenges for environmental assessment and urban design in dense urban settings. This study proposes a spatially explicit framework to jointly assess and optimize thermal comfort and visual restorative potential at the panorama level, integrating street-view image-based spatial metrics, XGBoost with explainable SHAP analysis, and Genetic Algorithm-based optimization, enabling interpretation of non-linear trade-offs that prior separate-domain analyses could not resolve. Using nine residential neighborhoods in Singapore, the Universal Thermal Climate Index (UTCI) and Perceived Restorativeness Scale Score (PRSS) were predicted from image-based metrics and synthesized into an Integrated Thermal-Visual Score (ITVS). SHAP analysis revealed that Sky (31.9%), Tree (20.8%), and Depth (13.0%) were the dominant predictors with non-linear patterns: Tree > 0.15 and Depth > 0.35 improved integrated performance, while Sky > 0.06 degraded it as thermal penalties outweighed visual gains. Optimization achieved a mean UTCI reduction of 0.327 °C and PRSS increase of 0.535. Cluster analysis further identified three morphological typologies, i.e., open-exposed, dense-shaded, and green-balanced, with optimization gains ranging from 22.1% to 32.5%, underscoring the need for morphology-specific strategies. These empirically derived thresholds and the integrated optimization framework offer transferable, quantitative guidance for performance-oriented urban design in high-density tropical cities. ... Read more

Smart city initiatives have been a driving force for city-level dataset collection and the development of data-driven applications that benefit effective city management. There is a need to demonstrate use cases for effective city management using the available dataset. Urban Digital Twin (UDT) is a 3D city model that can integrate multi-disciplines and improve systems operability on a digital platform. However, UDTs are developed within organisations, and there is only limited availability of authoritative open 3D datasets to explore the potential of UDT concepts. This paper reports a methodology for creating a UDT platform for visualising and querying city energy data. We demonstrate a bottom-up approach to constructing an integrated 3D city dataset and create a query system for rapid access and navigation of the 3D city dataset through a visualisation platform using Cesium Ion. Various use cases are explored based on the dataset, such as building material stock management, energy demand simulation, electric vehicles (EV) demand and flexibility, and estimation of greenhouse gas (GHG) emissions. These use cases can help decision-makers and stakeholders involved in city planning and management. Furthermore, it provides a guideline for developers willing to create UDT applications for smart city initiatives. ... Read more