Decoding Visual Acceptability in Ecological Restoration: A Multi-Modal Explainable AI Approach

While ecological restoration revitalizes degraded urban lands, biophysical recovery does not inherently guarantee public visual acceptance. This study introduces a survey-free computational framework to evaluate the visual acceptability of restored landscapes, analyzing 685 post-restoration photographs from South Korea’s Ecosystem Conservation Levy Return Projects. We integrated SegFormer and MiDaS to extract 22 depth-weighted, eye-level landscape metrics and categorized sites into three distinct visual-composition typologies via K-means clustering. To approximate aesthetic response, a CLIP vision–language model generated CLIP-derived, image-level preference scores. A Random Forest model (in-sample R2 = 0.882) and SHAP analysis were then applied to decode the non-linear relationships between physical landscape elements and the CLIP-derived scores. The results revealed an asymmetric association structure: visual evidence of degradation (bare soil) and urban intrusion (buildings) were strongly associated with lower estimated preference, whereas care-signaling elements (unpaved trails) were associated with a markedly positive effect. SHAP dependence analysis identified favorable conditions for higher estimated preference, including bare soil exposure below 10%, minimal building visibility, and a baseline visual diversity (SHDI) above 1.0–1.1—a positive association that was sustained up to the dataset’s maximum observed complexity (~2.0). By translating visual landscape composition into quantitative, interpretable thresholds, this survey-free framework offers a scalable and explainable foundation for integrating aesthetic considerations into evidence-based ecological restoration design.