Traditional village landscapes have a cultural and regional significance, and the visual aesthetic quality of the landscape is widely regarded as a valuable resource to benefit the health and well-being of urban residents. Although the literature has analyzed the influential mechanism of landscape features on aesthetic senses, most were from a single dimension. To improve the precision of the landscape aesthetic evaluation method, this study expanded the indicators for landscape characteristics of public spaces in traditional villages by incorporating multiple dimensions, such as landscape visual attraction elements and landscape color. It explored their associations with sensory preferences in a case study in Dongshan (a peninsula) and Xishan (an island) of Taihu Lake. We used multi-source data, a semantic segmentation model, and R language to identify landscape characteristic indicators quantitatively. The research results indicated that the accuracy of the aesthetic sensory assessment model integrating multi-dimensional landscape characteristic indicators was significantly improved; in the open space of traditional villages, the public preferred a scenario with a high proportion of trees, relatively open space, mild and uniform color tones, suitability for movement, and the ability to produce a restorative and peaceful atmosphere. This study can provide a guarantee for the efficient use of village landscape resources, the optimization of rural landscapes, and the precise enhancement of traditional village habitat.

Traditional villages are historically, culturally, scientifically and aesthetically valuable, and a beautiful landscape is the primary embodiment of a traditional village environment. Urbanization and modernization have had a great impact on village landscapes. As an important aspect of traditional village landscapes, creating beautiful public spaces is an effective way to attract tourists and improve the well-being of residents. Landscape aesthetic activities are the result of the interaction between landscape objects and aesthetic subjects. Research on the relationship between the form of traditional village public spaces and subjective aesthetic preferences has long been neglected. This research examined 31 public spaces in traditional villages in the Dongshan and Xishan areas in Lake Taihu, Suzhou. An index system of public spatial forms in traditional villages was created, basic data of spatial forms were collected using a hand-held 3D laser scanner, and the value of the spatial forms index was calculated using R language. The scenic beauty estimation (SBE) method was improved, with the estimation of the beauty of the scenic environment based on VR panorama rather than traditional photo media. Parameter screening was performed using correlation analysis and full subset regression analysis, and four models were used to fit the SBE scores and grades. The results show that the majority of public spaces had lower than average SBE scores, and the four key indicators of average contour upper height, solid-space ratio, vegetation cover, and comprehensive closure predicted SBE. In addition, the linear model (R² = 0.332, RMSE = 64.774) had the most accurate SBE level prediction and the stochastic forest model (R² = 0.405, RMSE = 63.311) was better at predicting specific SBE scores. The model provides managers, designers, and researchers with a method for the quantitative evaluation of visual landscape preferences and quantitative landscape spatial forms and provides a reference for the protection and renewal of traditional village landscapes.

There is a complicated and contradictory relationship between landscape functions and human activities, especially in the suburban rural communities of metropolises. Previous studies focused on human interference to landscape function, ignoring the impact of landscape functions on human activities. Hence, the present study is focused on the impact of landscape function (based on ecosystem services) on human activities in suburban rural communities of China. The study evaluated the intensity of human activities based on big data; furthermore, the authors analyzed the spatial distribution characteristics through spatial autocorrelation, and probed into the spatial variations in the relationship between human activities and landscape functions using ordinary least squares (OLS) and geographically weighted regression (GWR) models. The result indicates that there are obvious spatial distribution differences in the intensity of human activities in suburban rural communities; that is, the intensity decreases from the inner to the outer suburban areas. Positive influencing factors of human activities are construction area, bus station, road network density, and leisure entertainment, among which, construction area is the principal driver; cultural heritage, hydrological regulation, and provision of aesthetics are negatively or positively correlated with human activities in various regions. The results offer insights for the sustainable development of rural environment in suburban areas and the big data-driven rural research.