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Neighbourhood environment characteristics have been found to be associated with residents’ willingness to conduct physical activity (PA). Traditional methods to assess perceived neighbourhood environment characteristics are often subjective, costly, and time-consuming, and can be applied only on a small scale. Recent developments in deep learning algorithms and the recent availability of street view images enable researchers to assess multiple aspects of neighbourhood environment perceptions more efficiently on a large scale. This study aims to examine the relationship between each of six neighbourhood environment perceptual indicators—namely, wealthy, safe, lively, depressing, boring and beautiful—and residents’ time spent on PA in Guangzhou, China.

A human–machine adversarial scoring system was developed to …