## 3 Results
### Point Cloud Evaluation Based on M3C2 Distance  
The M3C2 distance between the reference point cloud (16 GCPs of the BA process) and point cloud obtained from different projects was calculated. Mean and standard deviation were used as indicators to evaluate the accuracy and precision, respectively, of the point clouds of different projects (Table 3 and Figure 7). The mean distance between point clouds obtained by zero GCPs and the reference point cloud was 0.062 m. Adding a GCP at any position can reduce the mean distance, and the improvement is most obvious after adding K8 GCP in the lower right corner. Regardless of the distribution, when using two GCPs, the mean error can be reduced by about 50% compared with 0 GCPs. With an increase in the number of GCPs, the mean distance decreased, and the point cloud became more accurate. The mean distance decreased to a minimum of 0.01 m at eight and nine GCPs. The standard deviation decreased to 0.01 m at eight and nine GCPs.  
deviations of different projects were distributed between 0.02 and 0.03, showing no obvious difference. Several projects achieved a minimum standard deviation of 0.021 m.  
The error space distributions of the M3C2 distance between the reference point cloud and the point clouds of the 0-, 3-, 6-, and 9-GCP projects were calculated, and the results are displayed with the same legend and histogram distribution of the distance (Figure 8). A significant dome effect (red area) can be observed at zero GCPs in Figure 8a; the upper left corner of Figure 8b shows a clear error compared with the reference point cloud due to a lack of control; the error from Figure 8c to Figure 8d is not significantly reduced, which is only reflected in the mean distance (from 0.013 to 0.01 m).  
Overall, the accuracy near the boundary of the study area is not as high as that in the center, which is related to the low overlap of images near the boundary (Figure 3e). There are obvious errors along the north-south road and the boundaries of buildings. The reasons for the large M3C2 distance error along the road are as follows: (1) tall plants on both sides of the road block the ground; (2) sample data from the road area are extracted, and the calculated point cloud density is approximately 12 per m\({}^{2}\), which is less than the average density of approximately 35 per m\({}^{2}\) in the overall study area. The uniform surface of the road (asphalt pavement) lacks adequate feature points, resulting in a sparse area within the point cloud. The M3C2 distance increased at the boundaries of buildings due to the sudden change in topographic characteristics.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Project** & **Mean (m)** & **std (m)** & **Project** & **Mean (m)** & **std (m)** \\ \hline
16-0 & 0.062 & 0.025 & 16-3 & 0.027 & 0.022 \\
16-1/k1 & 0.046 & 0.023 & 16-4/k2,k5,k9,k12 & \(-\)0.015 & 0.028 \\
16-1/k8 & 0.023 & 0.022 & 16-4/k1,k6,k8,k15 & 0.023 & 0.021 \\
16-1/k15 & 0.053 & 0.025 & 16-5 & 0.021 & 0.021 \\
16-1/k10 & 0.041 & 0.023 & 16-6 & 0.013 & 0.021 \\
16-1/k6 & 0.031 & 0.022 & 16-7 & 0.016 & 0.021 \\
16-2/k1, k10 & 0.032 & 0.023 & 16-8 & 0.01 & 0.023 \\
16-2/k6, k15 & 0.024 & 0.028 & 16-9 & 0.01 & 0.021 \\ \hline \end{tabular}
\end{table}
Table 3: M3C2 distance values between reference point clouds and point clouds obtained by GCPs with different numbers and distributions. Refer to Figure 4 to identify these points.  
Figure 7: M3C2 distance between reference point clouds (16 GCPs) and point clouds obtained from different photogrammetric projects. (**a**) Average difference (accuracy). (**b**) Standard deviation (precision). Add error bars at +/\(-\)1 standard error at GCP number of 1, 2 and 4, respectively.