The accelerating deterioration of Chinese historical villages necessitates advanced digital approaches for systematic documentation and conservation. The present research proposes a novel Digital Heritage Framework that integrates UAV-based 3D oblique photogrammetry, LiDAR point cloud modeling, and computer vision. Unlike single-technology approaches, our methodology solves modeling issues for complex terrain mapping. This especially applies to the interior and roof works of buildings. The framework implements a customized Rhino-Grasshopper. The 3D model is able to resolve issues of shadow occlusion and spatial discontinuity by integrating aerial and ground-based datasets into spatially coherent formats. This makes use of the Meta-AI-SAM2 deep learning model for semantic segmentation and identification of materials. The computer vision (CV) approach gives semi-automated façade analysis. It enables documentation of complex architectural features non-invasively. We developed a Unity-based visualization platform. It features multiscale representations, ranging from village-scale layouts to centimeter-accurate scans of heritage structures such as the Qinchuan Ancestral Hall. Integration with the Unity platform optimizes dataset organization and hierarchical structuring. This significantly enhances database operational efficiency. This integration reduces manual processing complexity and hardware demands. Demonstrating documented efficiency and precision, this workflow presents a scalable solution for endangered heritage sites. Future research will explore AI-assisted detail reconstruction and cross-cultural adaptations. It potentially establishes this framework as a comprehensive tool for sustainable digital conservation.
Fan et al. (Fri,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: