Three-Dimensional Intelligent Understanding and Preventive Conservation Prediction for Linear Cultural Heritage
Abstract
1. Introduction
2. Low-Altitude Oblique Image-Based Intelligent Gaussian Splatting Generation
2.1. Principles of Gaussian Splatting for Linear Cultural Heritage Scenes
- (1)
- Source Point Generation and Influence Control
- (2)
- Multi-source Influence Accumulation Modeling
- (3)
- Region-of-Interest (ROI) Density Control Strategy
2.2. Intelligent Generation of 3D Gaussian Splatting Models
2.3. Model Evaluation, Comparison, and Applicability Analysis
2.3.1. Evaluation of Preservation Accuracy and Cultural Feature Fidelity
Evaluation of Preservation Accuracy
Evaluation of Cultural Feature Fidelity
2.3.2. Performance Comparison and Resource Consumption
3. Intelligent Understanding Based on 3D Gaussian Splatting Model
3.1. AHLLM-3D Network Design for Linear Cultural Heritage Understanding
3.2. Multimodal Understanding and Generation of the Great Wall Heritage Based on 3DGS
3.2.1. Dataset Construction
3.2.2. AHLLM-3D Model Distributed Training
3.3. Experimental Results and Analysis
3.3.1. Staged Training Metrics Analysis and Parameter Optimization Process
3.3.2. Evaluation of Command Comprehension Effectiveness and Verification of Semantic Reasoning Ability
4. Intelligent Understanding Based on 3D Gaussian Splatting Model
4.1. Research Methods and Technical Routes for Intelligent Forecasting
4.1.1. Data Acquisition for the Study Area
4.1.2. Technical Lines of Research
4.1.3. Principles of Snake Optimization Network Prediction Model Based on Variational Modal Decomposition
4.2. Synthetic Aperture Radar (SAR) Monitoring Time Series Analysis
4.3. Linear Cultural Heritage Deformation Displacement Prediction
4.3.1. Prediction Model Design for Variational Modal Decomposition Networks Incorporating Snake Optimization Algorithm
4.3.2. Forecast Results and Analysis
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Data Source | Submodule | Specific Use | Processing Method |
---|---|---|---|
InSAR | Deformation Monitoring and Prediction | Provides large-scale surface deformation data | Generates time series deformation information using SBAS-InSAR technology for large-scale region monitoring. |
UAV | Low-altitude Tilt Photogrammetry and Modeling | Provides high-resolution 3D reconstruction models | Uses SfM and MVS technologies for feature extraction, sparse point cloud generation, and texture mapping. |
Point Cloud | 3D Modeling and Semantic Analysis | Provides 3D geometric information of cultural heritage | Combines high-density sampling and 3DGS technology, integrates point cloud data with large language models for semantic understanding and spatial analysis. |
Multimodal Data | Intelligent Understanding and Generation | Conducts semantic understanding and multimodal data fusion for cultural heritage | Combines 3DGS with the AHLLM-3D model; integrates point cloud data and text for dual-level annotation and semantic analysis. |
SBAS-InSAR | Deformation Monitoring and Trend Analysis | Monitors ground subsidence of linear cultural heritage | Uses SBAS-InSAR technology for high-precision surface subsidence prediction and analysis. |
Metric | 3DGS | PhotoScan |
---|---|---|
Points/Faces | 20.48 M Gaussians | 145 M dense points, 28.98 M faces |
Model Size | 2.6 GB | 3.2 G |
Rendering Speed | 32 FPS (4090) | No real-time rendering |
Total Modeling Time | 3 d 15 h (4090)/ 2 h 32 m (H800) | >5 days |
Rendering Type | Real time; interactive | Static, non-interactive |
Best Use Case | Visualization + AI | Static documentation and measurement |
Model | Iterations | Model Size (GB) | GPU Use | Time | |||
---|---|---|---|---|---|---|---|
3DGS | 30k | 22.91 | 0.650 | 0.357 | 3.48 | 74.34 | 2 h 19 min |
CityGaussian | 30k | 22.88 | 0.647 | 0.360 | 3.58 | 76.89 | 2 h 39 min |
gsplat | 30k | 21.10 | 0.476 | 0.718 | 1.92 | 55.35 | 2 h 17 min |
Ours | 30k | 22.94 | 0.655 | 0.357 | 3.57 | 78.86 | 2 h 32 min |
k | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
---|---|---|---|---|---|---|---|---|---|---|---|
k = 3 | 0.0044 | 0.0716 | 0.1691 | ||||||||
k = 4 | 0.0023 | 0.0272 | 0.0820 | 0.1959 | |||||||
k = 5 | 0.0021 | 0.0247 | 0.0774 | 0.1470 | 0.2164 | ||||||
k = 6 | 0.0020 | 0.0238 | 0.0758 | 0.1356 | 0.1929 | 0.3144 | |||||
k = 7 | 0.0029 | 0.0226 | 0.0728 | 0.1141 | 0.1558 | 0.2197 | 0.3755 | ||||
k = 8 | 0.0019 | 0.0225 | 0.0706 | 0.1025 | 0.1509 | 0.2044 | 0.2676 | 0.3802 | |||
k = 9 | 0.0019 | 0.0226 | 0.0703 | 0.1013 | 0.1499 | 0.2006 | 0.2538 | 0.3241 | 0.3877 | ||
k = 10 | 0.0019 | 0.0225 | 0.0700 | 0.0998 | 0.1485 | 0.1935 | 0.2250 | 0.2675 | 0.3395 | 0.3913 | |
k = 11 | 0.0019 | 0.0225 | 0.0697 | 0.0982 | 0.1470 | 0.1855 | 0.2062 | 0.2345 | 0.2720 | 0.3922 | 0.3471 |
Predictive Model | RMSE (mm) | MAE (mm) | R2 |
---|---|---|---|
VMD-SO-AT-CNN-LSTM (ours) | 3.5209 | 2.7584 | 0.9932 |
VMD-SSA-CNN-LSTM | 9.5036 | 4.3827 | 0.9670 |
CNN-LSTM-MATT | 8.4487 | 6.8381 | 0.9610 |
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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
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 StyleWang, 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 StyleWang, 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