Abstract
Although investigators are using data sources to describe the visual characteristics of streets, few researchers have linked human perceptions of the street environment with human activity density. This study proposes a conceptualized analytical framework that explains the relationship between human activity density and the visual characteristics of the streetscape. The image-segmentation model DeepLabv3+ automatically extracts each pixel’s semantic information and classifies visual elements from 120,012 collected panoramic street view images of Zhengzhou, China, using the entropy weighting method and weighted superposition to calculate the street perception summary score. This deep learning approach can successfully describe the semantics of streets and the connection between population density and street perception. The study provides a new quantitative method for urban planning and the development of high-density cities.
Highlights
With the rapid development of motorized transportation, the traditional street space scale and living environment have been destroyed, and more and more attention and research have been paid to urban streets
The 120,012 Baidu map street view (BSV) images were processed by semantic segmentation to extract elements
The study used BSV images and deep learning methods to investigate the relationship between human perception of streets and human activity density
Summary
Streets are an important part of urban public spaces [1,2]. They play a key role in the spatial organization of cities and are necessary for daily life as well [3]. Streets represent the continuation of a rich social culture. In 1970, the United States proposed “complete streets”, and the Netherlands launched the concept of “life-oriented roads” [6,7]. What these concepts have in common is the transformation of streets from mere traffic into complex spaces of diversity. Urban cultural symbols include urban signs, urban visual guides, urban color, space environment, etc. This is a systematic process which involves refined work [10]
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