## 3 Results and Discussion  
Once the photo set was uploaded and processed, the 3D virtual model was obtained, which corresponds to the virtual reconstruction performed using Bentley's Context Capture line of computer programs with cloud processing service (Bentley Cloud Service). Table 2 shows the flight parameters, Table 3 presents the setting model parameters, and Figure 4 shows the model generated in Context Capture. \begin{table}
\begin{tabular}{c c} \hline
**UAV:** & **DJI Phantom 4 Pro+** \\ \hline Satellite positioning: & GPS/GLONASS \\ Max. Resistance Wind & 10 m/s \\ Speed: & 1388 g \\ Weight: & 30 min \\ Max. time flight: & 20 MP \\ Sensor: & 20 MP \\ Lens: & FOV 84\({}^{\circ}\) 8.8 mm/24 mm /28-\(f\)/11 auto focus 1 m \\ Shutter Speed: & 8–1/2000 s \\ \hline \end{tabular}
\end{table} 
Table 1: Technical specifications unmanned aerial vehicles (UAV) equipment used. In total, 733 of the 734 images uploaded to the program were correctly calibrated, resulting in 99.9% of the images correctly calibrated, which is considered sufficient to believe that the image capture campaign was successful. The processing time for a model generation was 25 min 56 s. A GSD value equal to 1.93 mm/pixel is obtained, and the model scale is 1:6. Regarding the number of \"tie points\" that were earned, on average, an amount of 1519 points per image was reached, obtaining a total of 253,520 mooring points in the model. Figure 5 shows the number of photos each mooring point considered for virtual reconstruction, where YX axes correspond to a plan view, ZX a cut view, and ZY an elevation view. Each color represents the number of photos that share the same mooring point is considered for virtual rebuilding. The minimum number of photos considered a mooring point was 3, while the maximum value reached 66 photos. The average number of photos considered per mooring point is four photos, and the vast majority are located in the area corresponding to the pavement. However, there is an area at the end of the road, where an appropriate density of mooring points was not observed. \begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline Flight height & 1–10 m \\ Flight speed & 1–1.5 m/s \\ Minimum overlap (longitudinal/transversal) & 80\%/80\% \\ Photo capture timer & 1–2 s \\ Camera angle & Nadir–45\({}^{\circ}\) \\ Maximum GSD considered & 0.26 cm/px \\ \hline \end{tabular}
\end{table} 
Table 2: UAV—Flight parameters. Figure 4: Virtual model “MIRADOR1” generated in Context Capture. The graph shown below represents the root mean square (RMS) reprojection error calculated for the tie points generated in the 3D rebuild, where an average value of 0.56 pixels or 1.08 mm of error was reached. Figure 6 identifies each mooring point with its respective RMS value. Figure 7 shows the distance between the UAV equipment's position obtained through the metadata and the position of the image calculated in the reconstruction. The arrows indicate the \"gap\" or distance between the metadata information and the calculated position of the image; all arrows start from the metadata position and end at the calculated position of the image. The shortest distance obtained was 0.31 m, while the largest was 8.22 m. The average distance reached a value of 2.43 m, where the main distances occurred around the Z-axis, which corresponds to the elevation; the distances between metadata and image calculated in the XY plane are minimal. The results are related due to the lower accuracy that GPS equipment has concerning elevation. The uncertainty of the position of the tie points is shown in Figure 8, represented with different colors according to the greater or lesser uncertainty that existed for each point.