Scenic roads, as linear landscapes integrating ecological, cultural, and recreational values, require systematic landscape character assessment (LCA) to evaluate their quality and attractiveness. This study develops an experience-oriented LCA framework that integrates perceptual and cognitive dimensions, balancing objective quantification and subjective experience. Using image and text datasets from scenic roads across six continents, deep learning-based semantic segmentation extracted perceptual characteristics, while thematic analysis identified cognitive dimensions. Results show that, at the perceptual level, sky, plants, and mountains are core perceptual elements, highlighting the dominance of natural components in landscape composition. Regionally, Europe and Australasia exhibit higher naturalness, North America greater landscape diversity, and South America higher openness. Cognitively, three key dimensions evaluated practicalities, place imagery, and affective attitudes, define scenic road cognition, with contextual factors such as road quality and accessibility shaping regional differences. Integrating perception and cognition, the proposed framework provides a complementary experience-oriented approach for assessing scenic roads. This methodology advances theoretical understanding and offers practical guidance for planning, designing, and managing linear and regional landscapes, supporting evidence-based and human-centered landscape evaluation.
Ge et al. (Wed,) studied this question.