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Paper: Evaluating human perception of building exteriors using street view imagery
This study explores how building appearances shape urban perception, using machine learning and survey data to analyze human responses to over 250,000 building images from Singapore, San Francisco, and Amsterdam. Findings reveal how architectural styles influence streetscape perceptions, offering insights for architects and city planners.
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Paper: Revealing spatio-temporal evolution of urban visual environments with street view imagery
This study presents an embedding-driven clustering approach that combines physical and perceptual attributes to analyze the spatial structure and spatio-temporal evolution of urban visual environments. Using Singapore as a case study, it leverages street view imagery and graph neural networks to classify streetscapes into six clusters, revealing changes over the past decade. The findings provide insights into urban visual dynamics, supporting planning and landscape improvement.
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