## 3 Methodology
### Dataset  
A DJI M300 RTK drone equipped with a ZENMUSE P1 camera was employed to capture high-resolution images of the tailings pond and its surroundings (DJI, Shenzhen, China). A detailed flight plan was designed before conducting the drone survey. Due to the large scope of the tailings pond, it was divided into four areas for image acquisition. The flight path is shown in Figure 4a, with a flight height of 120 m, 70% forward overlap, and 30% in sidelap. A total of 6542 clear images of the tailings pond were collected, with a resolution of 8192 \(\times\) 5460 pixels. SfM is a technique for 3D reconstruction using captured unordered images. SfM reconstructs the 3D structure of the object and estimates camera motion from the image sequence. In this study, the entire process of 3D reconstruction was performed using Metashape (version 1.6.4, available at [[https://www.agisoft.com/](https://www.agisoft.com/)]([https://www.agisoft.com/](https://www.agisoft.com/)), (accessed on 15 June 2024)) software on a computer equipped with an Intel Core i5-10400F processor, an NVIDIA GeForce GTX 1650 graphics card, and the Windows 10 operating system. The entire process of 3D reconstruction of 6542 images took more than 10 h. The following steps were followed.  
Figure 4: Three-dimensional reconstruction process. (**a**) Flight planning; (**b**) photo screening; (**c**) feature matching (the blue line is a correct match, and the red line is an incorrect match); (**d**) sparse clouds; (**e**) dense clouds.  
Figure 3: The flow chart of this research.  
Photo screening: Metashape can automatically estimate the photo quality. Quality values range from 0 to 1, as shown in Figure 4b. Images with a quality value of less than 0.5 are considered substandard and are not used in subsequent processing. It is up to the user to decide whether or not to perform this step.  
Feature matching and bundle adjustment: Feature detection and the matching of two-dimensional images are performed using algorithms, such as the scale invariant feature transform (SIFT), to detect and match feature points in each image, filter outliers or mismatches, and generate an initial sparse point cloud, as shown in Figure 4c. Then, bundle block adjustment is performed to iteratively optimize the camera pose and reconstruct the 3D spatial coordinates of the feature points by minimizing reprojection errors to generate more accurate sparse 3D point clouds [34; 35], as shown in Figure 4d.  
Dense geometry reconstruction: The multi-view stereo (MVS) algorithm can make use of the camera pose and sparse point cloud generated by the SfM process to reconstruct a dense point cloud [36; 37]. The depth map is calculated through pixel matching across multiple views based on the camera pose, and the depth information is converted into three-dimensional points to generate high-precision dense point clouds, as shown in Figure 4e.  
In this study, we used a DJI M300 RTK drone, which is equipped with a high-precision Real Time Kinematic Global Navigation Satellite System (RTK-GNSS) system capable of providing centimeter-level real-time positioning accuracy. Because of the high accuracy of RTK technology, we can use a small number of control points (GCPs). Five non-collinear ground control points (GCPs) were measured using a GNSS device in RTK mode. The GCP observations were used as constraints to improve the model reconstruction during the SfM processing step. The accuracy assessment was conducted using control targets (distinct geometric objects such as building walls, landmark objects, or large joint surfaces) instead of checkpoints (CKPs), as shown in Figure 5. The direction and length errors of five targets were calculated by comparing field measurements with point cloud model measurements. Table 1 presents the mean error (ME) and root mean square error (RMSE) for both ground control points (GCPs) and control targets.  
Figure 5: Control targets used for the accuracy assessment.