###### Abstract
With the ongoing advancement of big data technology, the use of big data evaluation methods, particularly those utilizing streetscape images, for assessing urban street quality is on the rise. Consequently, the quality of street spaces must be measured and evaluated with continuity and precision, integrating research on street quality and accessibility [4]. The physical composition and environmental characteristics of street spaces serve as the foundation for human activities, and their variations directly influence the extent to which street spaces meet the needs of individuals and their interactions [5]. Previous studies have established a strong correlation between crowd perception and street quality, exemplified by concepts such as [PERSON]'s \"space score [6]\" and [PERSON]'s \"sensory quality [7]\". [PERSON] introduced the concept of \"street eyes [8]\" from a sociological perspective, while [PERSON] [9] emphasized the interaction between the environment and human behavior, stating that activities reflect human needs and that the physical environment and street morphology significantly impact street vitality and human interactions. In street image data evaluation, early studies by [PERSON] [10] and colleagues used manual assessments to develop indicator systems, employing qualitative and quantitative methods to analyze environmental features. [PERSON] and [PERSON] et al. (2023) [11] employed the ELO scoring mechanism alongside the random forest algorithm to assess the spatial quality of streetscapes in Fuzhou City. [PERSON] and [PERSON] et al. (2024) [12] developed a three-dimensional evaluation framework integrating the Pyramid Scene Parsing Network (PSPNet) and Spatial Design Network Analysis (sDNA), leveraging multi-source big data including Points of Interest (POIs) and streetscape imagery. While these studies laid a foundation for modern research on street quality, the lack of quantitative data and detailed evaluation indicators limits design optimization and control measures. Therefore, future research should focus on quantitative data and developing comprehensive evaluation indicators. With the growing diversity of urban open data acquisition methods and the widespread use of multi-source data, including fundamental geographic information and streetscape images, data collection techniques have transcended the limitations of traditional methods concerning scale and accuracy [13]. The integration of machine learning algorithms, such as SegNet [14], DeepLab [15], and YOLO [16], with Deep Convolutional Neural Network (DCNN) [17] architecture has facilitated more accurate and in-depth processing of street view images. Presently, numerous scholars utilize the combination of street view images and machine learning to investigate the spatial quality of streets. For instance, [PERSON] et al. [18] employ computer image recognition technology to segment material elements and evaluate street quality based on four objective criteria: green visibility rate, street openness, interface enclosure, and motorization level. [PERSON] et al. [19] leverage street view data and machine learning to propose a method and framework for measuring the spatial quality of streets from a human-centered perspective, successfully integrating \"quality evaluation\" with \"accessibility analysis\" through the application of sDNA analysis. Regarding street perception, [PERSON] et al. [20] employed multi-source data to quantitatively assess individual perceptions and the physical characteristics of street space on a large scale, analyzing the relationships between objective indicators and street aesthetics and security through mathematical modeling. Although current explorations in this field are somewhat fragmented [21]--ranging from data acquisition and spatial analysis to impact measurement and result visualization--this research is anticipated to foster comprehensive innovations in urban planning and design, thereby better supporting the development of large-scale pedestrian-friendly cities [22].