Urban green spaces are a key component of urban ecosystems, not only improving ecological buffers and maintaining environmental quality, but also ensuring the health and well-being of residents. As urban areas continue to expand, the scientific and rational planning of green spaces becomes increasingly vital for advancing sustainable urban development. The use of the Landsense Green View (LGV) indicator, derived from the principles of Landsenses Ecology, offers a valuable quantitative method for assessing the dimensionality of green cover within urban blocks. This metric is used to capture the level of greenery that affects environmental and psychological health outcomes. This study focuses on the central urban district of Nanjing, utilizing advanced methodologies including artificial intelligence and street view big data to meticulously quantify LGV and assess its distribution. Additionally, the study employs Geographic Information System (GIS) technology to analyze spatial patterns and utilizes econometric tools to identify and discern the factors influencing LGV values in this region. The regression model yields a high coefficient of determination of 0.869, which demonstrates the reliability of the employed model in predicting and understanding the Landsense Green View within the city. The GIS analysis conducted in the study unveils key insights: 1) Natural landscapes at the block scale prominently enhance LGV, conversely, artificial landscapes serve as impediments, decreasing LGV significantly. 2) The analysis shows that the function and diversity of land use within urban blocks have profound impacts on LGV values. 3) It reveals a concerning uneven distribution of LGV across Nanjing, characterized by an urban-rural gradient with LGV diminishing from the urban center towards rural areas. This gradient suggests a disparity in access to ecological benefits provided by urban green spaces which might influence urban planning and policy making. The implications of these findings are manifold. By understanding the spatial determinants and impacts of LGV, city planners and environmental managers can better strategize the allocation of green space resources and optimize urban layouts to amplify the ecological services and social benefits they confer to urban dwellers. In conclusion, this study fortifies the understanding of the current status and spatial patterns of ecological green spaces from a human-centered perspective. It highlights the importance of integrating advanced quantitative tools and spatial analysis techniques in urban green space planning. Furthermore, the research framework introduced in this study provides a robust foundation for optimizing urban planning and promoting sustainable green management approaches. By enhancing the quality and functionality of urban green spaces, our framework is geared towards achieving the objectives of sustainable development and urban resilience in the context of green space distribution.