## 1 Introduction
[PERSON] and [PERSON] [28] divided visual indicators into four categories: visual preferences landscape indicators, landscape visual character indicators, GIS-based landscape appreciation model indicators, and indicators based on the digital panoramic view analysis. [PERSON] [29] developed a method for registering the visual landscape on an element level, shape level, and space level, with the aim of describing changes in landscape structure and shape according to their intensity and character on different complexity levels. Built on the conceptual framework of four levels [13], [PERSON] et al. [2] present an approach for capturing the visual character of a landscape, which is chosen as the method of this study. Based on previous studies on visual concepts, landscape preferences and landscape aesthetics, [PERSON] et al. 's theoretical framework has represented the level at which the landscape could be quantified and measured, taking both empirically tested and suggested visual indicators into account. Furthermore, [PERSON] et al. [2] have stated a filter approach that can help select an appropriate set of visual indicators for application within a specific landscape context. For all these reasons, the concept-indicator framework developed by [PERSON] et al. is a suitable approach for describing visual indicators, identifying physical attributes, and laying the basis for landscape visual quality evaluation. As the purpose of the study changes, the theoretical base and data source will vary depending on the information they can provide about a landscape. [PERSON] et al. [30] state that the most exhaustive approach to assessment includes direct observation in the field. Some authors explore landscape preferences by evaluating cognitive aspects such as openness, coherence, harmony, and others [31; 32]. Hence, for preference studies conducted through the perception of the evaluator, photographs are frequently used as representations of landscape [30; 33; 34]. In such studies, what is being rated is the character the photos represent. However, photographs may have limitations because it is difficult to capture the diversity of potential views and evaluate certain factors by using photographs [35]. In addition, the difficulty of offering a representative photo sample is stressed by several researchers [36]. Some authors suggest that a combined method using several data sourcesincluding field observations, landscape photographs, aerial photographs, and land cover data--will be the most appropriate [36; 37; 38; 39]. Since the emergence of Web 2.0 technology [40], the rise of social media has opened up new paths for landscape visual character studies. The number of photos containing location-related information is increasing on social media platforms, leading to the fast development of crowdsourced geospatial data [40]. Uploading and sharing photographs on social networks such as Flickr, Panoramio, and Instagram have become prominent [41; 42; 43]. According to [PERSON] et al. [44], there are three main approaches for the application of online photo data in research: the study of text attached by photos, the study of metadata embedded by photos, and the study of the photos' visual content itself. As [PERSON] and [PERSON] argued [45], content analysis of landscape photographs is an observational approach to coding and analyzing the frequency of certain visual attributes and elements. To date, previous studies have analyzed crowdsourced geotagged photographs for assessing landscape features at different levels. At regional scales, [PERSON] et al. [46] perform a content analysis of 1404 photos uploaded on Flickr and Panoramio platforms, and analyze the landscape features across five sites in Europe. At local scales, authors use content analysis of spatial photos to pinpoint what it is in a landscape to attract people [47; 48].