Decoding urban green spaces: Deep learning and google street view measure greening structures

Abstract

Street greenery is an essential component in assessing the ecological quality of urban areas. Panoramic images of streets provide a more comprehensive representation of the streetscape, enabling researchers to explore the diverse composition of street greenery and its correlation with the physical and mental health of urban residents. In this study, we propose a method based on semantic segmentation of panoramic street images to evaluate the structure of urban street greenery and calculate the proportion of its different broad structural categories of plants. To accomplish this, we constructed a new street view dataset called Street Greening Space Structure Dataset (S-G-S-S), which is specifically designed for street greening structure evaluation. Using the DeepLabV3 + neural network model, we trained the system for semantic segmentation of panoramic streetscape images to improve the accuracy of Panorama View Green View index (PVGVI) while accurately identifying the street greenery structure. To verify the accuracy and stability of the method, we conducted an empirical study in the Livingston area of western New York City, USA, using multiple neural network models and comparing the results. The findings indicate that the proposed method can better visualize the greening structure at the urban street level, offering a valuable tool for urban planners and policymakers to assess the ecological quality of urban areas.