Measuring human perceptions of a large-scale urban region using machine learning

By Fan Zhang, Bolei Zhou, Liu Liu, Yu Liu, H. Fung, Hui Lin, Carlo Ratti. 2018

686 citations recorded by the source corpus.

Abstract Measuring the human sense of place and quantifying the connections among the visual features of the built environment that impact the human sense of place have long been of interest to a wide variety of fields. Previous studies have relied on low-throughput surveys and limited data sources, which have difficulty in measuring the human perception of a large-scale urban region at flexible spatial resolutions. In this work, a data-driven machine learning approach is proposed to measure how people perceive a place in a large-scale urban region. Specifically, a deep learning model, which has been trained on millions of human ratings of street-level imagery, was used to predict human perceptions of a street view image. The model achieved a high accuracy rate in predicting six human perceptual indicators, namely, safe, lively, beautiful, wealthy, depressing, and boring. This model can help to map the distribution of the city-wide human perception for a new urban region. Furthermore, a series of statistical analyses was conducted to determine the visual elements that may cause a place to be perceived as different perceptions. From the 150 object categories segmented from the street view images, various objects were identified as being positively or negatively correlated with each of the six perceptual indicators. The results take researchers and urban planners one step toward understanding the interactions of the place sentiments and semantics.

Subfields: Computer Vision and Pattern Recognition, Global and Planetary Change, Media Technology

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In Computer Vision and Pattern Recognition

1. Yao Yao
2. Chau Yuen
3. Jinbao Zhang
4. Filip Biljecki
5. Qingfeng Guan
6. Koichi Ito

Citations in the corpus

• A survey of data fusion in smart city applications (2019, methodology): “In addition, a large variety of data sources can be used for urban planning such as physical sensors [160], photography [76], [161], or hybrid data sources [85].”
• Analyzing the correlation between visual space and residents' psychology in Wuhan, China using street-view images and deep-learning technique (2021, neutral): “Based on street view image data, many scholars have explored the impact of the scenes reflected in the images on residents ’ psychological perception [37,38,39,40].”
• A human-machine adversarial scoring framework for urban perception assessment using street-view images (2019, background): “Our result shows that boring was a more subjective perception with a large deviation, which was consistent with the fi ndings of (Zhang et al .”
• Street view imagery in urban analytics and GIS: A review (2021, background): “…understanding built environment characteristics associated with walkability, though there is also research on providing route recommendations (Zhang et al., 2018; Wakamiya et al., 2019), and some research projects include collecting walking data to verify the actual movement (Shatu &…”