Restorative Evaluation of Rural Waterside Landscapes in Mountainous Areas Integrating Semantic Segmentation and Machine Learning

Rural waterfront landscapes connect ecological environments with residents’ recreational experiences, and assessing their perceived restorativeness can support improvements in rural living environments. However, complex terrain and limited data availability constrain the quantitative assessment of their visual characteristics. This study examined the Changjiang River waterfront in Jinzhai County, Anhui Province, China. Panoramic imagery, semantic segmentation, and random forest regression were integrated to derive 12 objective visual indicators of landscape composition and color characteristics and to predict four dimensions of perceived restorativeness: fascination, being away, coherence, and scope. The R2 values for the hold-out test set were 0.732, 0.730, 0.722, and 0.719, respectively (n = 200). The green view index, blue view index, contrast, color count, and color complexity showed significant positive correlations with perceived restorativeness, whereas the road visibility index, mountain visual ratio, and artificial structure visibility index showed significant negative correlations. Visible blue-green elements and visual richness were associated with higher perceived-restorativeness scores, whereas images with more visually dominant road surfaces, mountains, and artificial structures tended to receive lower perceived-restorativeness scores. These findings are specific to the dry-season, low-water-level conditions observed during the November survey and should not be generalized to other seasons or hydrological conditions.