Evaluation of Rural Visual Landscape Quality Based on Mul-Ti-Source Affective Computing

The visual quality assessment of rural landscapes is vital for quantifying ecological ser-vice functions and cultural heritage, yet traditional ecological indicators fail to capture emotional and cognitive experiences. Therefore, this study introduces a meth-od for assessing rural landscape visual quality that integrates a multi-modal emotion classification model to enhance the quantitative drive towards sustainability. The experiment selected four types of landscapes from three representative villages in Dalian, China, collecting physiological data (EOG, EEG) and subjective evaluations (beauty assessment and SAM scale) from participants. Binary, ternary, and five-element classification models were constructed. The results indicate that the bi-nary and ternary classification models yielded the highest accuracy in emotion valence and arousal, while the five-element model demonstrated the lowest performance. Additionally, ensemble learning models outperformed single classifiers in binary and ternary tasks, with an average accuracy improvement of 7.59%. Moreover, the collaborative fusion of subjective and objective data enhanced the accuracy of the ternary classification by 7.7% compared to existing research, confirming the efficacy of multisource features. The findings suggest that the framework based on multi-source affective computing can serve as a quantitative tool for assessing the emotional quality of rural landscapes and promoting the sustainable development of rural areas.