Application of an Integrated Model for Analyzing Street Greenery through Image Semantic Segmentation and Accessibility: A Case Study of Nanjing City

Abstract

Urban street greening, a key component of urban green spaces, significantly impacts residents’ physical and mental well-being, contributing substantially to the overall quality and welfare of urban environments. This paper presents a novel framework that integrates street greenery with accessibility, enabling a detailed evaluation of the daily street-level greenery visible to residents. This pioneering approach introduces a new measurement methodology to quantify the quality of urban street greening, providing robust empirical evidence to support its enhancement. This study delves into Nanjing’s five districts, employing advanced image semantic segmentation based on machine learning techniques to segment and extract green vegetation from Baidu Street View (BSV) images. Leveraging spatial syntax, it analyzes street network data sourced from OpenStreetMap (OSM) to quantify the accessibility values of individual streets. Subsequent overlay analyses uncover areas characterized by high accessibility but inadequate street greening, underscoring the pressing need for street greening enhancements in highly accessible zones, thereby providing valuable decision-making support for urban planners. Key findings revealed that (1) the green view index (GVI) of sampled points within the study area ranged from 15.79% to 38.17%, with notably better street greening conditions observed in the Xuanwu District; (2) the Yuhua District exhibited comparatively lower pedestrian and commuting accessibility than the Xuanwu District; and (3) approximately 139.62 km of roads in the study area demonstrated good accessibility but lacked sufficient greenery visibility, necessitating immediate improvements in their green landscapes. This research utilizes the potential of novel data and methodologies, along with their practical applications in planning and design practices. Notably, this study integrates street greenery visibility with accessibility to explore, from a human-centered perspective, the tangible benefits of green landscapes. These insights highlight the opportunity for local governments to advance urban planning and design by implementing more human-centered green space policies, ultimately promoting societal equity.