Abstract: To explore the visual preference characteristics of landscapes integrating natural and artificial elements in urban parks, and to verify the applicability of eye tracking technology and the segment anything model (SAM)in landscape quantitative analysis, this study selected Guilin Shanshui Park as the research object. A multi-dimensional evaluation system combining landscape features, visual behavior and subjective assessment was constructed byusing eye-tracking technology, the scenic beauty estimation (SBE) method and the SAM. Forty participants were recruited, and their eye movement data and aesthetic scores were collected. Automatic segmentation and quantitative analysis of landscape elements were carried out with the SAM. Differences in landscape visual behavior among different functional zones, landscape elements and groups in Shanshui Park, as well as their correlation with aesthetic evaluations were systematically compared. The results showed that: 1) Two main high-value cores in the park were revealed by Kernel density analysis, which were located in the south-central part of the Cultural and Sports Entertainment Zone and the northeastern part of the Sightseeing Zone. A highly significant positive correlation was observed between average fixation duration and SBE scores. The highest fixation count (25.34 times) was found in the Sightseeing Zone, while the lowest (22.72 times) was recorded in the Leisure Strolling Zone. 2) Significant differences in visual preferences for landscape elements were detected among different participant groups. The long-term resident group exhibited significantly longer fixation durations in artificial areas such as the Urban Theme Square, while the short-term stay group showed longer fixations on characteristic elements such as landscapesketches. 3) Plants were identified as the dominant landscape element in the park, accounting for 39.0%. A significant correlation between the landscape element segmentation results of the SAM and eye tracking attention indicators was confirmed, which indicates that the quantitative indexes of the model can effectively reflect human visual attention distribution and provide a new approach for objective and efficient landscape feature analysis. This study provided empirical evidence for the landscape optimization of Guilin′s landscape city parks and expanded the application paths of eye-tracking technology and the SAM in the research on landscapes of specific region.

Key words: scenic beauty estimation (SBE), segment anything model (SAM), landscape visual preference, eye tracking, Guilin Shanshui Park, China