Urban designers are interested in the environmental qualities of places that make them better, not only as settings for physical activity but also as sensorial and social settings [1]. Streets, as one of the most common urban open spaces, have been endowed with increasing importance because of their potential to sustain people’s outdoor lives and fulfil their physical, psychological and social needs [2]. With the continuous growth of human needs in public urban spaces, people’s perceived sense of comfort has become a vital quality in street evaluation studies. Referring to the existing literature, street comfort can be defined as the physical and psychological satisfaction people obtain when they sensorily interact with street settings [3]. Alfonzo (2005) states street comfort as a critical perceptive need of the public with the potential to create more opportunities for activities [4], increase street vitality and encourage social and consumer behavior [5]. Therefore, designing comfortable streets in urban areas is of great importance since it can promote social interaction, economic development and improve peoples’ health and well-being [6].

A better understanding of the relationship between people’s perceived comfort and physical street characteristics is necessary for planners and designers to deliver better street environments [7]. However, most studies concerning human perceptions are constrained by research scales due to the difficulties in obtaining users’ perceptive data and constructing their relations to environmental attributes. This also leads to a lack of a way to extrapolate large-scale spatial characteristics from small-scale human perceptions, as well as of cross-city and cross-culture perspectives in relevant research arenas. This study proposes that the big data of street view images and image analysis methods based on artificial intelligence (AI) may have the potential to carry out street comfort evaluations at the city scale. Through a multi-method approach involving perceptive comfort measurements, image analysis based on deep learning algorithms and spatial mapping using the geographic information system (GIS), street comfort is measured at a city scale, and the street-comfort-related attributes in different cities are identified and compared. The research outcomes provide cues on how …