City landscape in sight: A crowdsourced framework for unlocking urban-scale window view perceptions from real estate imagery
Researchers have developed a crowdsourced framework to analyze urban-scale window view perceptions using real estate imagery from Wuhan, China. The study collected over 27,000 pairwise comparisons from 300 participants on six perceptual dimensions, which were then used to train a hybrid neural network model. This model predicts human perceptions and maps their spatial distribution across the city, revealing that floor level and the composition of the view (sky, trees, buildings) significantly influence preferences, with non-linear effects observed. AI
IMPACT Provides a novel method for urban planning and real estate by quantifying visual preferences from residential window views.