# Evaluation of Rural Visual Landscape Quality Based on Multi-Source Affective Computing
### Rural Visual Landscape Quality Assessment  
As a spatial-cultural composite that embodies the evolving human-land relationship, the assessment of rural landscape visual quality plays a pivotal role in quantifying ecological service functions and preserving cultural memory [42; 43]. In contrast to the homogenizing trends observed in urban landscapes, the regional heterogeneity of rural visual landscapes, coupled with the interplay between natural and human elements, facilitates a paradigm shift in evaluation--from traditional aesthetic criteria to interdisciplinary collaborative analysis [44]. The theoretical foundation for visual landscape quality assessment can be traced to [PERSON]'s concept of \"the comparative relationship between the perception and evaluation of two or more landscapes\" [43]. In the rural context, this concept  
Figure 1: Multi-source affective computing framework. The outer white characters are two themes, and the inner five circles are five aspects. The yellow arrows signify a correlation between the two themes and the five dimensions.  
is further extended to encompass \"the systematic decoding of the visual characteristics and emotional value of local spaces\". Methods for assessing the quality of rural visual landscapes can be categorized into two distinct approaches [45]. The first, \"externalist\" evaluation, treats space as an object of observation. Common methods, such as GIS [46] and surveys [47], are employed to analyze stimuli including remote sensing images, classified maps, photographs, and actual landscapes. The second approach is \"egocentric\" assessment, which typically begins with direct human experience and employs physiological sensors--such as eye trackers [48] and EEG [47]--along with evaluative tools such as the Seenic Beauty Estimation (SBE) method [49], Analytic Hierarchy Process (AHP) [50], and Semantic Differential (SD) method [51]. This approach combines intuitive perception and assessment of photos, simulations, and real landscapes, incorporating both objective and subjective evaluations (through questionnaires). In addition to these two evaluation methods, the interdisciplinary integration of neuroscience, psychology, computer science, and visual landscape research has led to the development of techniques for random visual landscape perception, evaluation, and emotion classification prediction. For example, [PERSON] et al. [52] analyzed the preferences of villagers and university students using eye tracking and EEG to explore the appeal of plant organ structures. They found that plants with distinct features and vibrant colors were preferred in rural landscapes, while simpler structures were favored in campus settings. [PERSON] et al. [53] developed a predictive model based on the correlation between eye-tracking metrics and 19 different emotional responses to rural landscapes, highlighting the effectiveness of eye-tracking technology in capturing and predicting emotional reactions to various landscape types. [PERSON] et al. [54] innovatively employed aerial video and EEG technologies to assess rural landscapes, selecting seven representative landscape types, extracting EEG features, and classifying them with four classifiers. They demonstrated that SVM and Random Forest (RF) classifiers exhibited high accuracy, achieving 98.24% and 96.72%, respectively, and identified distinct classification patterns across different features and bands, thereby advancing novel methodologies for quantifying human perception. Thus, in the assessment of rural visual landscape quality, perception refers to the behavioral patterns of visual or brain activity, while evaluation pertains to preferences and ratings of the landscape. Both processes are intrinsically linked to the emotional experience of the visual landscape.