Urban Function Recognition at Street Level by Integrating Taxi Trajectory and Street-Level Imagery

Urbanization in China has entered a new phase that emphasizes both scale expansion and quality improvement. This has led to demands on urban functional structures and rational urban planning. Street space serves as a vital spatial carrier for meeting urban residents' needs, such as travel, shopping, and leisure. It is comprised of urban roads and their ancillary facilities, buildings along the route, and many other elements. However, existing studies typically focus on the traffic function of urban roads, overlooking other functional aspects of street space as complex public activity areas, thereby hindering the optimization of street space quality. Therefore, there is a need to propose a classification method for the urban street space functions. Given the proliferation of taxi trajectory data and street view imagery, street space can be described in detail from the citizens' perspectives. Therefore, this study proposes a street space classification method that integrates taxi trajectory and street view imag