## 1 Introduction
Unlike two-dimensional indicators, such as the greening coverage rate and the green space ratio, GVI represents a higher standard of urban greening. It is widely used to evaluate the walkability and greening level of streets [22; 30]. Extracting feature information from street view images using deep learning models is a mainstream method for assessing general street environments [22; 27; 31]. However, differences in the equity of visible street greenery have received less attention. Several metrics have been applied to assess spatial equity and social justice in green space distribution. For instance, the Gini coefficient [20] and location entropy [32] are often combined to assess the overall and local characteristics of spatial equity. Methods like the share index [33] and supply-demand index [34] further highlight differences in access to green space resources among various social groups. Researchers have found that disparities in green resource distribution are often directly linked to factors such as population density, age, income, race, and education level [19; 34]. [PERSON] et al. [13] found that in Hartford, Connecticut, people with higher incomes and education levels tend to live in communities with more street greenery. Similarly, [PERSON] et al. [31] also pointed out that in Los Angeles County, California, communities with higher proportions of low socioeconomic status and minority populations have significantly lower levels of street greenery. Studies in Chinese cities have also concluded that densely populated [35] and socioeconomically disadvantaged communities [36, 37, 38], as well as those with older adults [33], may suffer from street greening inequity. These studies often use the original regional divisions within their research scope, such as subdistricts [33, 35], neighborhoods (juweihui) [36, 38], or communities (xiaoqu) [37], However, these divisions may introduce significant computational errors due to their irregular shapes when simulating residents' needs. Using regularly shaped scales, such as fishnet grids or hexagonal honeycomb grids, can largely reduce these errors [39]. Furthermore, most studies on street visibility are limited by data and remain single-time-point analyses, focusing on the evaluation of current conditions. Research on the long-term changes in green visibility and equity in communities under rapid urbanization is still rare [40], leading to a lack of guidance for urban planners and policymakers on how to reasonably plan for, and finely regulate, street greening. To address these issues, this study employed a regular hexagonal grid as the evaluation unit and utilized the \"Time Machine\" feature introduced by Baidu Maps to obtain time-series street view images. By applying deep learning, the Gini coefficient, and location quotient methods, we investigated changes in green visibility and equity in residential communities. Wuhan's central urban area, a rapidly urbanizing Chinese city, was selected as a case study. The novelty of our research lies in two key aspects: First, we provide valuable evidence for small-scale studies by using residential aggregation grids as the basic research unit, allowing for a more comprehensive and in-depth diagnosis of local supply and demand gaps caused by residential differentiation. More importantly, we innovatively incorporated a dynamic perspective into the evaluation of green visibility equity, monitoring spatio-temporal differences in visibility equity across residential areas. This allows for the timely identification of communities in need of improvement. This study served three main purposes:  
1. To evaluate the level and spatio-temporal patterns of street-level greening around residential communities from 2014 to 2021;
2. To assess the spatial equity of green visibility in residential communities across different regions from 2014 to 2021, revealing the dynamic characteristics of equity changes;
3.