Some urban form characteristics, e.g., dense street network, diverse pedestrian destinations, can promote vitality, because they support walking, social interaction, and public life. Yet these characteristics mainly focus on pedestrians rather than indoor occupants, although people spend around 90% of their time indoors. Using 1,352,550 simulated window views across Hong Kong, this study examines whether vitality-oriented urban form indicators, including floor area ratio, road network density, point-of-interest entropy, and building-age entropy, are associated with model-predicted window-view quality. Multilevel mixed-effects models were used, complemented by height-interaction models (height refers to the vertical elevation), contextual heterogeneity tests, and multi-scale comparisons. The results show that road network density, point-of-interest entropy, and floor area ratio are negatively associated with window-view quality (β = − 0.111, − 0.082, and − 0.054, respectively; all p < 0.001), whereas the building-age association is very small (β = − 0.004). Moreover, window height is positively associated with window-view quality (β = 0.396), and all four height-interaction terms are positive (β = 0.026–0.055). Window-view quality was lowest in high-development-intensity Tertiary Planning Units, and associations with road network density and point-of-interest entropy were more negative in old urban areas. Across buffers, the road-density and floor area ratio associations remain negative in the predicted-score models. In the human-rated subsample, the height and two entropy associations remain directionally consistent, whereas the other two are sensitive to outcome measurement. The findings call for more vertically and contextually differentiated urban design strategies that consider indoor visual welfare alongside street-level vibrancy.

Understanding geodemographic distribution across neighbourhoods is essential for identifying socio-spatial differentiation in cities. Traditional geodemographic classification studies rely primarily on various census and socioeconomic data, offering limited insight into the street-level environments through which such differences are experienced and reproduced. In particular, the visual and perceptual dimensions of neighbourhoods remain largely absent from existing classification frameworks. This study advances geodemographic classification research by introducing street view imagery (SVI) as a novel source for capturing latent visual features linked to socio-spatial structure. We propose an automated machine learning framework that leverages 556 image-derived variables to predict socio-demographic groups. The framework is applied in ten UK cities, where a national model and local instances are developed to assess the extent to which visual– demographic relationships are place-dependent or generalizable. Results show that SVI has potential for capturing geodemographic patterns and enabling national-scale generalization. The local model achieves an accuracy ranging from 0.49 to 0.70, while the national model brings an accuracy of 0.62. Finally, cross-city transfer analysis reveals both shared and heterogeneous visual–demographic relationships across urban contexts. This study contributes a new street-level observational layer that complements traditional data sources and extends the methodological scope of geodemographics, while informing improvements to public spaces and supports policy objectives aimed at fostering inclusive neighbourhood environments.

Over the past three decades, nighttime light (NTL) has become one of the most widely used and powerful proxies for characterizing human activities. Despite the recent proliferation of available NTL data from both satellite- and ground-level sensors, intrinsic differences between these two observation approaches can result in distinct NTL brightness patterns (satellite-ground disagreement). Such disagreement, particularly its spatial heterogeneity, represents a critical yet largely overlooked issue in NTL studies, introducing uncertainty into the interpretation and application of NTL observations. Here, by incorporating SDGSAT-1 glimmer imagery and panoramic nighttime street view imagery with geospatial AI approaches, we present a spatially explicit, wall-to-wall investigation into satellite-ground disagreement across Singapore. Specifically, we characterized citywide disagreement and its variation across land-use types, analyzed contributing factors, and assessed its implications for NTL-based applications, reinforced by Monte Carlo-based uncertainty quantification and sensitivity analyses using independent observations from Singapore and Shenzhen, China. Our study revealed substantial and spatially heterogeneous satellite-ground disagreement across Singapore. Relative disagreement averaged 15% ± 13% citywide but reached 42% ± 12% in 13% of the city. Such pronounced heterogeneity was likewise evident across land-use types, with commercial areas standing out for consistently significant disagreement, where ground-level NTL brightness was 29% ± 26% higher than satellite-level NTL brightness. Building morphology, vegetation canopy cover, and sensor spectral response contributed to satellite-ground disagreement, suggestive of distinct mechanisms underlying the identified patterns. Applying satellite- and ground-level NTL data yielded relative differences of at least 115% and 83% in GDP estimation and NTL exposure inequality assessment, respectively. This study sheds new light on satellite-ground disagreement by moving beyond discrete comparisons toward a comprehensive analysis of its heterogeneity and implications, thereby establishing an empirical basis for integrating NTL observations and contributing to more reliable NTL-based applications and evidence-informed policies on sustainable urbanization.

Flooding is one of the most frequent and damaging natural hazards, increasingly intensified by rapid urbanization and extreme precipitation events. Accurate modelling of water accumulation and runoff in dense urban environments remains challenging due to complex geometries and the limitations of conventional surface-based representations. Most approaches require extensive LiDAR preprocessing before hydrological analysis. This study introduces a methodology for direct flood risk analysis on Aerial Laser Scanning (ALS) point clouds, avoiding intermediate surface reconstruction steps. The proposed approach enriches points with indicators of water accumulation and surface runoff potential. Water accumulation is estimated through elevation differences within a 100-m local neighbourhood to detect topographic depressions, while runoff potential is derived from three geometric descriptors (verticality, planarity, and scattering) that describe the capacity of urban structures to channel or obstruct water flow. The method was evaluated using open ALS datasets from three cities with contrasting urban morphologies: Vigo (Spain), Rotterdam (The Netherlands), and Hong Kong (China). Results demonstrate the capacity of the enriched point clouds to identify flood-prone urban features such as low-lying harbour areas, semi-tunnels, and urban canyons, as well as runoff pathways including sloped roofs, stairways, ramps, and road surfaces. The resulting point-level risk representation shows strong agreement with historical flood events and traditional multi-criteria assessments. By using LiDAR point clouds as an analytical medium, the framework enables intuitive 3D flood risk mapping while preserving their geometric richness.