The Great Wall, the world’s largest linear cultural heritage, faces rapid erosion due to natural conditions. Erosion damage starting from the wall’s base can lead to structural instability and collapse, posing the most destructive threat. This study conducts the first digital survey of erosion damage to the Ming Great Wall in Gansu using drone. Erosion damage was quantified through kernel density and spatial autocorrelation analysis. An optimal XGBoost machine learning model was developed to assess the contributions of 14 environmental factors. Combining SHAP value decomposition and partial dependence plots (PDP) revealed nonlinear interactions among these factors. Significant spatial aggregation of erosion damage was observed with salinization, topographic fluctuation, and annual average precipitation identified as primary influencing factors. Each factor exhibited either promoting or inhibiting effect. This study enhances understanding of the spatial dynamics driving mechanism of erosion on the Great Wall, providing a scientific basis for its conservation and restoration.

Traditional villages are important carriers of cultural heritage, and the quantitative study of their spatial characteristics is an important approach to their preservation. However, the rapid extraction, statistics, and estimation of the rich spatial characteristic indicators in these villages have become bottlenecks in traditional village research. This paper employs UAV (unmanned aerial vehicle) and handheld laser scanners to acquire three-dimensional point cloud data and construct a spatial feature three-dimensional calculation workflow of “field data collection—data processing—data analysis and application”, which enables the rapid acquisition, processing, and analysis of three-dimensional village data. Typical case studies are conducted in Liukeng Village, China, focusing on the quantification of village spatial characteristics at three levels: topography, streets and alleys, and individual buildings, as well as comparative studies of multiple villages across different regions. The quantification of three-dimensional data reveals the regularity of village spatial characteristics and uncovers the spatial wisdom embedded in the site selection and spatial structure of traditional villages. This paper establishes a complete technical route for the quantitative analysis of villages, deepens public understanding of the diverse value of traditional villages, and provides technical support for research and practice in related fields.

The Great Wall is currently facing a significant amount of erosion damage that are rapidly causing its decay and destruction. Among these, erosion damage caused by saltwater intrusion, are the most devastating, as they undermine the foundation of the wall, leading to rapid collapse and disappearance. This study utilizes low-altitude oblique photography technology with unmanned aerial vehicles (UAVs) to construct a digital method route for rapid survey and precise quantification of the damage. We conducted a comprehensive survey of erosion damage along the Ming Great Wall in Gansu, China. A three-dimensional point cloud model was utilized to quantitatively assess the morphological characteristics of the erosion damage. Additionally, the distribution patterns of saline erosion were examined through a combination of field sampling and physicochemical experimental analysis. Our findings indicate that the spatial distribution of erosion damage exhibits an aggregative characteristic and is closely related to natural geographical elements. There are regional differences in the morphological characteristics of erosion, with high concentrations of erosion height and depth data in different sections. The degree of saltwater intrusion and the distribution of soluble salt content vary in different parts of the wall, showing a regular pattern of higher levels on the south side, decreasing from bottom to top. The paper conducts a basic survey of the spatial distribution and patterns of erosion damage, which decrease from a macroscopic distribution to key sections, and produces a risk map of erosion damage based on the survey results, providing reference and scientific support for the protection and restoration of the Great Wall.