A digital twin-driven enhanced visualization method for high-steep slope scene

• Published in: International journal of digital earth Vol. 18; no. 1
• Main Authors: Zhu, Jun, Ren, Ying, Guo, Yukun, Lu, Jingyi, Wu, Jianlin, Guo, Zhihao, Gu, Hengchao, Zhao, Hongyue
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 31.12.2025; Taylor & Francis Group
• Subjects: attention guidance; digital twin; fusion modeling; High-steep slope; scene-enhanced visualization
• ISSN: 1753-8947; 1753-8955
• DOI: 10.1080/17538947.2025.2509082

Research on enhanced visualization of high-steep slope twin scenes is crucial for accurately depicting relationships between entities and improving the efficiency and accuracy of scene cognition. However, existing studies focus on static slope model displays, facing challenges such as slow model reconstruction, lack of integration with 3D scenes, and low cognitive efficiency. To address these issues, this paper proposes a digital twin-driven enhanced visualization method for high-steep slope scenes. Firstly, the slope model is rapidly reconstructed using an improved QEM algorithm combined with multi-level semantic constraints. Secondly, the concept of digital twins is introduced to construct a domain knowledge graph with three-domain associations, and dynamic-static fusion methods are employed to build the slope twin scene. Then, an attention-guided enhanced visualization method is proposed based on human visual cognition. Finally, a case study of a high-steep slope in Luding County, China, is conducted. Experimental results demonstrate an 80% increase in model reconstruction speed, with scene fusion stabilized within 1 s and errors under 1 decimeter. Enhanced visualization reduced users' average task completion time by 87.5% and increased accuracy to 91.5%. This method meets the user's performance needs and rapid scene information retrieval, contributing to efficient management of high and steep slopes.