Building roofs are essential for various geographical analyses such as solar potential analysis and urban microclimate simulation. Despite growing demand, reconstructing detailed 3D roofs remains challenging due to the complexity of roof geometries and variations in architectural styles. This paper introduces RooFormer, an end-to-end learning framework for reconstructing detailed and textured 3D roof models in mesh format from high-resolution imagery. RooFormer consists of a MaskFormer branch, which identifies and focuses on roof features, and a MeshFormer branch, which predicts detailed roof meshes. In the MeshFormer branch, a local self-attention mechanism is employed to understand mesh features, and a positional embedding layer is designed to integrate geometric and texture features. In addition, to measure the geometric similarity between predicted meshes and ground truth, we develop a loss function that integrates terms from both image and mesh spaces. Compared to existing 3D metrics, the proposed geometric loss term more accurately reflects the geometric differences in meshes. Experiments show that its normalized height error of 0.014 is lower than the 0.034 error of state-of-the-art methods. Visually, the reconstruction accurately reflects the geometric contours and structures of roofs, even with slight occlusions. We also demonstrate its generalization by testing it across various areas. The framework promises to enable richer building modeling and analysis for a wide range of digital city applications.

Humans can play a more active role in improving their comfort in the built environment if given the right information at the right place and time. This paper outlines the use of Just-in-Time Adaptive Interventions (JITAI) implemented in the context of the built environment to provide information that helps humans minimize the impact of heat and noise on their daily lives. This framework is based on the open-source Cozie iOS smartwatch platform. It includes data collection through micro-surveys and intervention messages triggered by environmental, contextual, and personal history conditions. An eight-month deployment of the method was completed in Singapore with 103 participants who submitted more than 12,000 micro-surveys and had more than 3,600 JITAI intervention messages delivered to them. A weekly survey conducted during two deployment phases revealed an overall increase in perceived usefulness ranging from 8%–19% over the first three weeks of data collection. For noise-related interventions, participants showed an overall increase in location changes ranging from 4%–11% and a 2%–17% increase in earphone use to mitigate noise distractions. For thermal comfort-related interventions, participants demonstrated a 3%–13% increase in adjustments to their location or thermostat to feel more comfortable. The analysis found evidence that personality traits (such as conscientiousness), gender, and environmental preferences could be factors in determining the perceived helpfulness of JITAIs and influencing behavior change. These findings underscore the importance of tailoring intervention strategies to individual traits and environmental conditions, setting the stage for future research to refine the delivery, timing, and content of intervention messages.

Recognising GeoAI as an emerging and rapidly evolving field that has been increasingly adopted in urban geography, this chapter provides an overarching overview of the GeoAI methods for urban analytics. It begins by revisiting the theoretical underpinnings of urban theory and mapping the evolution of urban spatial analytics, tracing the journey from traditional statistical methods to the cutting-edge AI-driven approaches reshaping the discipline today. Beyond examining the current state of GeoAI, the chapter also identifies current trending topics and investigates future directions for developing human-centric methodologies that prioritise the needs and experiences of urban residents. By emphasising the human dimension of urban analytics, the chapter seeks to contribute to the ongoing discourse on how GeoAI can be harnessed to enhance city governance, urban planning, and the overall quality of urban life.

Cities are supported by multiple, interacting networks, most prominently streets, which channel movement and economic exchange, and, in many contexts, waterways, which regulate flows of goods, people, and environmental amenities. Conventional quantitative studies of urban form have tended to privilege streets alone, limiting their ability to capture the full spatial logic of the urban fabric. This paper introduces a Heterogeneous Graph Autoen-coder (HeterGAE) that jointly embeds street and waterway systems, providing a unified, graph-based representation of urban form. Using Singapore as a case study, we train HeterGAE embeddings and employ them in two downstream tasks: predicting daytime and night-time land-surface temperature (LST) and estimating resale prices of public housing. Relative to a baseline model that encodes streets only, the dual-network embeddings improve predictive accuracy by about 20% for both tasks, confirming that natural and built infrastructures make complementary contributions to urban socio-environmental processes. By capturing the interaction between street junctions and waterway nodes within a single latent space, the proposed approach provides a flexible template for GeoAI-assisted urban analytics in diverse settings. The results underscore the value of integrating heterogeneous urban networks in evidence-based planning and highlight the potential of graph-neural techniques for developing more nuanced and sustainable urban strategies.

Climate change and urbanization present critical challenges to cities, requiring innovative energy and food security strategies. This study introduces a novel agrivoltaic system for building façades in Singapore’s dense urban context, addressing the trade-off between photovoltaic (PV) electricity generation and plant growth under shared solar exposure. By combining field experiments and advanced simulations, a genetic algorithm was employed to optimize PV arrangements, balancing solar exposure conflict for energy production and crop cultivation while also reducing building cooling load. Lettuce (Lactuca sativa) grown under PV shading yielded up to 120 g per plant, meeting commercial standards. Simulations revealed significant building energy benefits, with approximate annual savings of 50 kWh/m2 and CO2 reductions of 35 kg/m2 for every 100 m2 building block. This innovative system integrates renewable energy generation, urban agriculture, and passive cooling, maximizing the utility of vertical surfaces. By efficiently utilizing building surfaces, this approach offers a land-efficient strategy for integrating sustainable food and energy solutions in dense urban areas, contributing to urban resilience and further supporting sustainable development goals.