# Disseminating the Past in 3D: O Corro dos Mouros and Its Ritual Landscape (Galicia, Spain)
## 2 Materials and Methods
### Fusion of LiDAR-UAV and SfM Photogrammetry for High-Resolution 3D Archaeological Documentation  
The archaeological site was documented using a combined approach of aerial SfM-MVS and UAV-LiDAR, complementary techniques where the former provides photorealistic textures while the latter delivers precise geometric data beneath vegetation. In simple terms, this means that buried structures can now be detected even through dense forest cover with centimetre-level precision. This integration through 3D registration produces models with centimetre-level accuracy and high visual detail. The fieldwork commenced with a georeferencing phase, implementing a minimal-intervention protocol to preserve the archaeological context. Rather than using conventional artificial markers, we established a network of 28 natural ground control points (GCPs) based on landscape features, such as distinctive rock outcrops and stable geomorphological elements, strategically distributed to ensure optimal coverage of the site and its immediate surroundings. Georeferencing was performed using a Stonex S900T GNSS receiver (Stonex Srl, Monza, Italy). For data acquisition, we employed a DJI Matrice 300 RTK UAV (Da-Jiang Innovations Science and Technology Co., Ltd., Shenzhen, China) equipped with real-time kinematic  
Figure 3: Workflow of the methodology implemented in this study. positioning (RTK-GNSS), guaranteeing 1.0-1.5 cm accuracy (\(\pm\)1 ppm, or parts per million) under optimal conditions. This professional quadcopter features an aerodynamic design with an optimised centre of gravity and a critical redundancy system, including three parallel inertial measurement units (IMUs), three independent barometers, and two compasses, ensuring operational continuity in case of component failure. The UAV was equipped with a Zenmuse L1 sensor (Da-Jiang Innovations Science and Technology Co., Ltd., Shenzhen, China), combining a LiDAR module with a 20.4 MP RGB camera (1\" CMOS sensor) capable of simultaneous high-precision geospatial data capture (\(\pm\)5 cm with RTK) and high-resolution imaging (5472 \(\times\) 3648 pixels). The optical configuration includes a 3-axis stabilised gimbal (\(\pm\)0.01\({}^{\circ}\)), an 8.8 mm lens (24 mm equivalent) with a f/2.8 aperture, optimised for variable lighting conditions (ISO 100-6400, shutter speed 1/8000 s-8 s). This technological solution proves particularly effective in dense vegetation environments where conventional photogrammetric systems face limitations. LiDAR data were processed in DJI Terra v4.0.10 (Da-Jiang Innovations Science and Technology Co., Ltd., Shenzhen, China) to generate classified 3D models with centimetre precision, while the RGB images, enhanced with EXIF metadata and RTK coordinates (\(\pm\)1 cm), were processed using SfM-MVS techniques in Agisoft Metashape v2.0.3. The SfM-MVS workflow was executed in three main stages. First, the internal and external camera orientations were calculated, generating a georeferenced sparse point cloud that established the fundamental geometry of the dataset. Second, a dense point cloud was generated, significantly improving the spatial resolution and point density. Both phases were carried out with high-quality processing parameters to take full advantage of the original image resolution, thus ensuring optimal photogrammetric accuracy in the 3D reconstruction. Finally, a triangulated mesh was derived from the dense point cloud and textured using high-resolution RGB images. As part of the LiDAR data processing workflow, noise filtering and advanced point cloud classification techniques were applied to remove irrelevant points and automatically distinguish vegetation from ground surfaces. This process greatly enhances the accuracy of the point cloud, enabling a clearer and more precise identification of underlying archaeological features. A high-resolution LiDAR system was employed to improve terrain representation and reduce the interference caused by vegetation.