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Article

Three-Dimensional Intelligent Understanding and Preventive Conservation Prediction for Linear Cultural Heritage

1
School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
2
Beijing Key Laboratory for Architectural Heritage Fine Reconstruction & Health Monitoring, Beijing 100044, China
3
Engineering Research Center for Representative and Ancient Building Database of the Ministry of Education, Beijing 102616, China
4
International Joint Laboratory of Safety and Energy Conservation for Ancient Buildings, Ministry of Education, Beijing 100044, China
*
Author to whom correspondence should be addressed.
Buildings 2025, 15(16), 2827; https://doi.org/10.3390/buildings15162827
Submission received: 1 July 2025 / Revised: 25 July 2025 / Accepted: 30 July 2025 / Published: 8 August 2025

Abstract

This study proposes an innovative method that integrates multi-source remote sensing technologies and artificial intelligence to meet the urgent needs of deformation monitoring and ecohydrological environment analysis in Great Wall heritage protection. By integrating synthetic aperture radar (InSAR) technology, low-altitude oblique photogrammetry models, and the three-dimensional Gaussian splatting model, an integrated air–space–ground system for monitoring and understanding the Great Wall is constructed. Low-altitude tilt photogrammetry combined with the Gaussian splatting model, through drone images and intelligent generation algorithms (e.g., generative adversarial networks), quickly constructs high-precision 3D models, significantly improving texture details and reconstruction efficiency. Based on the 3D Gaussian splatting model of the AHLLM-3D network, the integration of point cloud data and the large language model achieves multimodal semantic understanding and spatial analysis of the Great Wall’s architectural structure. The results show that the multi-source data fusion method can effectively identify high-risk deformation zones (with annual subsidence reaching −25 mm) and optimize modeling accuracy through intelligent algorithms (reducing detail error by 30%), providing accurate deformation warnings and repair bases for Great Wall protection. Future studies will further combine the concept of ecological water wisdom to explore heritage protection strategies under multi-hazard coupling, promoting the digital transformation of cultural heritage preservation.
Keywords: preservation of the Great Wall; InSAR deformation monitoring; 3D Gaussian splattering model; low altitude tilt photogrammetry; multimodal intelligent understanding preservation of the Great Wall; InSAR deformation monitoring; 3D Gaussian splattering model; low altitude tilt photogrammetry; multimodal intelligent understanding

Share and Cite

MDPI and ACS Style

Wang, R.; Guo, M.; Zhang, Y.; Chen, J.; Wei, Y.; Zhu, L. Three-Dimensional Intelligent Understanding and Preventive Conservation Prediction for Linear Cultural Heritage. Buildings 2025, 15, 2827. https://doi.org/10.3390/buildings15162827

AMA Style

Wang R, Guo M, Zhang Y, Chen J, Wei Y, Zhu L. Three-Dimensional Intelligent Understanding and Preventive Conservation Prediction for Linear Cultural Heritage. Buildings. 2025; 15(16):2827. https://doi.org/10.3390/buildings15162827

Chicago/Turabian Style

Wang, Ruoxin, Ming Guo, Yaru Zhang, Jiangjihong Chen, Yaxuan Wei, and Li Zhu. 2025. "Three-Dimensional Intelligent Understanding and Preventive Conservation Prediction for Linear Cultural Heritage" Buildings 15, no. 16: 2827. https://doi.org/10.3390/buildings15162827

APA Style

Wang, R., Guo, M., Zhang, Y., Chen, J., Wei, Y., & Zhu, L. (2025). Three-Dimensional Intelligent Understanding and Preventive Conservation Prediction for Linear Cultural Heritage. Buildings, 15(16), 2827. https://doi.org/10.3390/buildings15162827

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