Green visibility is a key indicator to characterize the spatial distribution of urban green space and is closely related to the visual perception and actual experience of the residents. So it can be an important indicator to link the ecosystem services of green space with human well-being and an important tool to develop the human-centered high-quality urban green space. The rapid development of remote sensing and ground observation technology has promoted the study of urban greenness to expand from two-dimensional to three-dimensional, but the availability of data has limited the development of green visibility research. In this paper, we focus on two most commonly used photo-based and 3D model-based constructions of quantitative green visibility. Then, we comprehensively compare the advantages and disadvantages of the two methods in terms of public data accessibility, spatial coverage, data authenticity, operational difficulty and accuracy of results. The paper proposes that future research should explore the interaction mechanism between people and green space environment, carry out multi-scale hierarchical analysis, pay attention to the coupled modeling of multi-source data, control the cost of model training and use, and construct a method evaluation system. It is argued that green visibility is a typical interaction process between people and green space environment, and geographic information science should incorporate spatial heterogeneity, scale, distance attenuation, and other geospatial effect modes into the analysis based on incorporating computational visual computing, to improve the reasonableness of the model and the usability of the results, to promote the development of the research on green visibility both methodology and application.