{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T14:11:42Z","timestamp":1771596702721,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T00:00:00Z","timestamp":1771545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang A&F University Scientific Research Development Fund","award":["2023LFR076"],"award-info":[{"award-number":["2023LFR076"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Visual perception serves as a crucial interface connecting human psychology with the built environment. However, current studies on urban riverscapes often rely on static 2D imagery, failing to capture the spatial depth and immersive experience essential for ecological validity. Furthermore, the \u201cblack box\u201d nature of traditional machine learning models hinders the understanding of how specific environmental features drive public perception. To address these gaps, this study proposes an innovative framework integrating high-fidelity 3D models, computer vision (CV), and interpretable artificial intelligence (XAI). Using the River Thames (London) and the River Seine (Paris) as diverse case studies, we constructed high-precision 3D digital twins to quantify 3D spatial metrics (e.g., Viewshed Area, H\/W Ratio) and applied the SegFormer model to extract 2D visual elements (e.g., Green View Index) from water-based panoramic imagery. Subjective perception data were collected via immersive Virtual Reality (VR) experiments. Random Forest models combined with SHAP were employed to decode the non-linear driving mechanisms of perception. The results reveal three universal principles: (1) Sense of Affluence and Vibrancy are primarily driven by high building density and vertical enclosure, challenging the traditional preference for openness in waterfronts; (2) Scenic Beauty is determined by a synergy of high Green View Index and quality artificial interfaces, suggesting a preference for nature-culture integration; (3) Sense of Boredom is significantly positively correlated with Viewshed Area, indicating that empty prospects without visual foci lead to monotony. This study demonstrates the efficacy of integrating Digital Twins and XAI in revealing robust perception mechanisms across different urban contexts, providing a scientific, evidence-based tool for precision urban planning and riverside regeneration.<\/jats:p>","DOI":"10.3390\/ijgi15020091","type":"journal-article","created":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T13:08:23Z","timestamp":1771592903000},"page":"91","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Decoding Urban Riverscape Perception: An Interpretable Machine Learning Approach Integrating Computer Vision and High-Fidelity 3D Models"],"prefix":"10.3390","volume":"15","author":[{"given":"Yuzhen","family":"Tang","sequence":"first","affiliation":[{"name":"College of Landscape and Architecture, Zhejiang A&F University, No. 666 Wusu Street, Lin\u2019an District, Hangzhou 311300, China"},{"name":"Logistics Service Center, Zhejiang A&F University, No. 666 Wusu Street, Lin\u2019an District, Hangzhou 311300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shensheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Logistics Service Center, Zhejiang A&F University, No. 666 Wusu Street, Lin\u2019an District, Hangzhou 311300, China"},{"name":"Zhejiang Provincial Institute of Rural Revitalization, No. 666 Wusu Street, Lin\u2019an District, Hangzhou 311300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhui","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Landscape and Architecture, Zhejiang A&F University, No. 666 Wusu Street, Lin\u2019an District, Hangzhou 311300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinxuan","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Architecture, Tianjin University, No. 92 Weijin Street, Nankai District, Tianjin 300100, China"},{"name":"Taishun County Bureau of Agricultural and Rural Affairs, No. 202 Pingxi Street, Wenzhou 325500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjie","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Landscape and Architecture, Zhejiang A&F University, No. 666 Wusu Street, Lin\u2019an District, Hangzhou 311300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"133066","DOI":"10.1016\/j.jclepro.2022.133066","article-title":"Prediction of riverside greenway landscape aesthetic quality of urban canalized rivers using environmental modeling","volume":"367","author":"Li","year":"2022","journal-title":"J. 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