## 1 Introduction
However, based on computer vision technology related to deep learning, most research focuses on cultural ecosystem services and people's perception of landscape through the clustering of photo locations and recognition of different scenes [44; 49; 50]. Only a few researchers have explored the exact visual indicators in social media photos. For example, [PERSON] et al. [51] applied the geotagged photos uploaded on Flickr to analyze the characteristics of the landscape through a viewshed-based approach. Recent studies have not been able to precisely link the photo analysis of social media data to landscape visual quality theories as well as visual indicators. Consequently, compared to traditional data collection, big data has advantages. It is an unstoppable tendency to use deep-going mining for applying user-generated photos to landscape visual quality research in the social network-occupied era [44]. At present, there are two main methods of research related to visual content analysis: one is the traditional way, which manually recognizes the visual contents of photographs, and the other is an emerging way based on computer vision technology. Though the research using the emerging approach has an advantage in the size of the sample and relatively precise analysis, most of the prior studies based on machine learning lack a mature theoretical and conceptual framework. From this overview, it appears that the photo analysis of landscape visual indicators based on geotagged social data needs more evidence to understand the mapping of the observer's perception of the landscape. In the present study, the theoretical framework of [PERSON] et al. [2] and the landscape attributes of [PERSON] et al. [16] served as bases to determine appropriate visual indicators reflected in social media photographs. This study aims to analyze user-generated digital photographs uploaded on a famous Chinese social platform Sina Weibo (hereinafter referred to as Weibo), to select the most popular scenic spot in a cultural landscape and characterize the content itself in terms of its landscape visual indicators. In the following, we present an attempt at our methodology, starting from its very first step in exploring the most popular scenic spots through online hotness data, up to (a) the density and diversity analysis of landscape attributes in each scenic spot, (b) the analysis of the depth of view in each scenic spot, and (c) the analysis of color in each scenic spot as reflected in the user-generated photographs. In the evolution of our work description, we shall also demonstrate some intermediate steps, such as defining a close-up view, medium view, and long-range view through a vision in the photograph and applying MATLAB to the analysis of color. The key contribution of this work is that the mapping relationship between landscape visual attributes reflected in photographs and public perception was reported. Moreover, we linked traditional landscape theories with computer vision technology related to social media data, combining both the theoretical base and emerging techniques. Another contribution is that we expanded the fixed-pointphotography experiment into a more accurate way of measuring the visual scale, on the basis of the distance-to-height ratio. In sum, the analysis focuses on the following questions:  
1. Where are observers interacting with the cultural landscape? 2. What is it in a landscape that attracts people? Which landscape visual attributes are the most significant in observers' perception?