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.

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.

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.

Following the completion of the Great Wall Resource Survey in 2012, numerous landscape heritage resources along the Great Wall remained undiscovered, highlighting the limitations of conventional survey methods. This study aimed to conduct in-depth investigations of Great Wall signal sites through digital fieldwork methods, unveiling a crucial signaling structure—the auxiliary beacon—and presenting genuine historical scenes of the Great Wall signal network. Through the retrieval of the image database of the entire Great Wall and the utilization of UAVs (drones) for low-altitude remote sensing surveys, 252 auxiliary beacon sites were identified in diverse environments (e.g., deserts, mountains, plains) in Xinjiang, Gansu, Inner Mongolia, Qinghai, Ningxia, and other 10 regions. These case studies enable the categorization of layout types and the proposal of reconstruction hypotheses for the signal network of the Great Wall of China. The findings demonstrate that the beacon fire signals are not lit on the beacon tower tops, but through the ignition of various signals by auxiliary beacons, expressing pre-arranged information. Beacon towers and auxiliary beacons together form an efficient signal network along the Great Wall. This study explores how to use digital survey methods to unearth unknown landscape heritage resources of the Great Wall, enhancing the accuracy of observation for cross-regional and large-scale cultural heritage.

In the discovering, identifying and mapping work of heritage objects in forest or desert areas, LiDAR ensures work efficiency and can provide the most complete and accurate 3D data. In the field of heritage documentation in China, the integration of LiDAR and small UAV is highly desirable. However, due to issues on the vibration of flying platform, load capacity, safety and other factors, not all UAVs can be used as LiDAR carriers. Therefore, the selection and design of suitable UAVs are very important. Little research has been done in this area and related experiments, complete test data and clear conclusions are hard to find. After long-term selection, design, trial-manufacturing and testing, the authors compare the vibration, capacity, reliability, stability of many UAV types, and finally develop two UAV platforms which are most suitable for carrying LiDAR for heritage mapping projects.

Hollow Enemy Towers, as iconic structures of the Ming Great Wall, are renowned for their roles in defense surveillance, weapon storage, and firearm operation. Recent studies have indicated that certain Hollow Enemy Towers along the Ji Town section of the Ming Great Wall also serve the function of Beacon Towers for beacon signaling. However, previous studies have not definitively determined if these towers were distinctively marked, nor have they provided a comprehensive account of their current distribution and original historical appearance. This paper initially examined the historical documentation of white lime markings employed on the outer walls of certain Hollow Enemy Towers, which served as Beacon Towers during the middle and late Ming periods. Utilizing multidisciplinary methodologies, this research identified remains of lime markings of the Beacon-Equipped Hollow Enemy Towers along the Ji Town section of the Ming Great Wall, illustrating their extensive distribution. We analyzed the material composition and construction techniques of the lime mortar. This analysis clarifies the scope of lime plastering on the exterior walls of these towers and offers a point of reference for restoring their original historical appearance. The results make a significant supplement to the types of signaling structures on the Great Wall, enriching existing understanding of the original appearance of the Great Wall’s historical landscape.