City landscape in sight: A crowdsourced framework for unlocking urban-scale window view perceptions from real estate imagery

2026-06-13

AI Summary

This study addresses the significant yet underexplored influence of urban residential window views on residents’ quality of life at the city scale. Leveraging real-world window view images sourced from a real estate platform, the research conducts large-scale crowdsourced paired comparison experiments via a non-immersive virtual reality interface to collect multidimensional perceptual data. By integrating hybrid neural networks, image semantic segmentation, and spatial autocorrelation analysis, the authors develop a predictive model of visual perception and generate a citywide map of window view desirability. The findings reveal nonlinear effects of view composition—such as proportions of sky, trees, and buildings—and floor height on perceptual dimensions like preference and vividness, overcoming limitations of prior studies reliant on rendered or simulated scenes, and uncover distinct spatial hotspots and coldspots in perceptual quality across the urban landscape.

Abstract

City landscapes viewed through home windows influence quality of life, yet perceptions of actual window views at the urban scale remain understudied. This study presents an approach for large-scale mapping of perceptions using 12,334 window view images (WVIs) collected from actual residential properties listed on real estate platforms in Wuhan, China, representing a rarely explored form of urban view imagery that offers advantages over the rendered or simulated window views commonly examined in previous studies. Through a non-immersive virtual reality platform, we collected 27,477 pairwise comparisons across six perceptual dimensions (e.g.\ Vivid) from 304 participants based on 499 WVIs. A hybrid neural network model was trained to predict human perceptions of all crowdsourced WVIs and map their spatial distribution. Results reveal significant spatial autocorrelation with distinct hot and cold spots across the whole city. Floor level strongly influences human perceptions: while higher floors offer more preferred and extensive window views, lower-floor windows provide residents with quiet and vivid views. An inference model further shows that window view composition matters considerably: high ratios of sky, trees, and low-rise buildings enhance people's preferences and perceptions of vividness, whereas high ratios of high-rise buildings increase perceptions of monotony and oppression. Importantly, these effects are non-linear: the excessive presence of certain elements can alter their impact on human perception. This work advances urban-scale understanding of residents' visual experiences and provides evidence-based guidance for human-centric urban planning and real estate to optimise visual landscapes from windows.

Problem

• window view perception
• urban landscape
• crowdsourced imagery
• visual experience
• urban planning

Innovation

• window view perception
• crowdsourced imagery
• urban-scale mapping
• hybrid neural network
• non-linear visual impact