Examination of the applicability of deep learning models trained on emotional images for landscape image evaluation

Abstract

This study examines the evaluation of landscape images using a deep learning model trained on emotioninducing images. The regression models for pleasant and unpleasant emotions, trained using OASIS and NAPS emotion-inducing images, demonstrated high performance, suggesting their applicability in inferring the quality of landscapes. Inferring the emotional values of landscape images with and without utility poles showed that areas with poles induce displeasure, while areas without them bring about pleasure. Statistical significance was confirmed when comparing these findings with existing landscape evaluations based on fractal dimensions. No correlation was found between the inferred values from the regression model and fractal dimensions, implying the potential to evaluate areas that were difficult to assess using conventional methods, such as color assessment. This suggests the potential for diversification in landscape evaluation.