Three-Dimensional Intelligent Understanding and Preventive Conservation Prediction for Linear Cultural Heritage
Year: 2025
Type: article
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.
Source: Buildings
Authors: Ruoxin Wang, Ming Guo, Yaru Zhang, Jiangjihong Chen, Yaxuan Wei, Li Zhu
Language: English
Subfield: Geology
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Award: G8218299626
OpenAlex ID: W4413120122