Tourists’ Landscape Preferences of Luoxiao Mountain National Forest Trail Based on Deep Learning

Abstract

Assessing visitor usage and understanding visitor experiences are key components of the sustainable management of the natural environment. In the study of the relationship between people and landscapes, the combination of deep learning and traditional classification is rarely used to study the landscape preferences in the natural environment, and the research perspective of vertical space at altitude is lacking. This article is based on photo data voluntarily uploaded by tourists on the 2bulu outdoor assistant platform. A combination of deep learning and traditional manual classification was used to divide the collected 43234 landscape photos into 9 categories. Then, the time stamp, latitude, longitude, altitude, and other attribute information about the geotagged photos were used to study the specific landscape preferences of the main nature reserves along the Luoxiao Mountain National Forest Trail at different time scales, protected areas, and altitudes. The research results showed that the capture ability of the landscape reduced the representativeness of social media in terms of the preferences of tourists. Roads and identification facilities were particularly preferred by tourists in this subcategory. The preferences for various landscape types exhibited an obvious festival effect, which was affected by the peculiarities of the landscape in a particular season. Spatially, it exhibited an agglomeration pattern characterized by the concentration in the central area and scattering in the northern and southern regions. The preference for landscape types in the southern section was more abundant than that in the northern section, and there were significant differences at different altitudes. Tourism facilities and characters were the most preferred by tourists in each altitude range.