## 4 Conclusions  
The literature review process allowed us to gather a history of the use of UAVs as a tool for data acquisition. This tool is positioned as an economical substitute compared to more sophisticated emerging technologies, such as the terrestrial laser scanner, used for pavement inspection. It also served as the basis for developing a workflow for photo acquisition and further processing using the SfM-MVS technique. It also understands the importance of variables that influence flight planning. This research focused on developing a practical method for measuring geometric characteristics of potholes, using 3D models generated with photographs acquired with a UAV and processed through software based on the SfM-MVS technique. To develop this methodology, an experiment was conducted to evaluate the accuracy in the reconstruction of 3D models using images obtained from the variation and combination of different parameters of flight planning and data capture (digital photographs). Specifically, variations in the combinations of camera angles (vertical-oblique), image overlaps and flight height. The experiment concludes that the incorporation of oblique photographs into a set of vertical photographs does not guarantee a significant reduction in error. Therefore, the research focused on error assessment for different combinations of height and overlap with vertical angle of photo acquisition. Within the geometric characteristics of the pavement studied, the width is the characteristic that has the lowest level of error, followed by depth and last by volume. After the evaluation of the error, both for depth, width and volume, practical recommendations were generated according to the level of severity of potholes. It was obtained that the methodology is \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Pothole** & **Width [mm]** & **Error [\%]** & **Depth [mm]** & **Error [\%]** & **Volume [cm\({}^{3}\)]** & **Error [\%]** & **Severity** \\ \hline (a) & 537 & 1.5 & 44 & 12 & 4812.26 & 32.7 & Middle \\ (b) & 732 & 1.1 & 63 & 13.7 & 11,346.65 & 20.4 & High \\ (c) & 864 & 0.7 & 64 & 8.6 & 7578.54 & 24 & High \\ (d) & 824 & 1.1 & 65 & 12.2 & 9641.74 & 36.2 & High \\ (e) & 348 & 2 & 35 & 12.5 & 987.47 & 20.7 & Middle \\ (f) & 564 & 1.1 & 27 & 10 & 1814.25 & 22.5 & Low \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Dimensions of potholes measured in model at 15 m and error. applicable for heights of 10 to 15 m, since at higher heights the error levels do not allow to represent well the characteristics sought, while at lower heights the process becomes extremely laborious when having to perform manual flights; additionally, the GPS becomes sensitive and imprecise at these heights. These recommendations were validated in a real case study, demonstrating that they are extrapolatable to the regular practice of engineering, specifically, in the inspection of asphalt pavements. The future work of this research should focus on improving the accuracy of the models generated by incorporating ground control points (GCPs), mainly to apply it on road pavements that have milestones or benchmarks that are accessible prior to the acquisition of photographs. The GCPs improve accuracy because they are the first mooring points that improve relative accuracy levels while increasing the absolute position of the model. Moreover, another line of research could include the automatic extraction of characteristics of pavement models generated by SfM-MVS in conjunction with photographs acquired based on the recommendations of this research. This paper represents the results of teamwork. [PERSON], [PERSON] and [PERSON] designed the research methodology. [PERSON] and [PERSON] carried out the literature review, methods, experiments and results. All of the authors worked on the discussions and conclusions the manuscript. Finally, [PERSON] and [PERSON] reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.