## 1 Introduction  
The maintenance of roads is a procedure that seeks to deliver optimal service conditions for the routes, generating the least possible impact on the regularity of vehicle flow during operation [1]. Prior to maintenance, it is necessary to know the physical and functional condition of the road, which requires extensive inspection work, focused on knowing its level of damage. It aims to provide measures to assess the magnitude of various types of pavement deterioration, delivering the overall condition of the pavement and providing an estimate of the amount of resources needed to preserve the infrastructure [2]. Inspection is crucial to decide the type and extent of repair a road should receive, and thus provide the desired level of service to maintain acceptable conditions for vehicle traffic [3].  
Traditional manual road inspection is time consuming, labor intensive and subjective [2]. Some automated methods, which use equipment with cameras or laser technology, have also been implemented in pavement inspection, greatly improving the efficiency and objectivity of inspection [4]. However, multiple trips may be required to cover the entire width of the road with the inspection vehicle. In addition, the on-the-spot inspection process affects traffic flow, which is particularly unfavorable on high-traffic roads. Unmanned aerial vehicles (UAVs) have the advantage of high flexibility, relatively low cost compared to inspection vehicles, easy maneuverability and less work on the ground [5]; therefore, they are promising in the task of assessing the condition of the pavement.  
On the other hand, recent advances in both high-resolution optical cameras and image processing techniques have allowed three-dimensional (3D) models of UAV images to be derived with sufficient accuracy and high efficiency, making image-based 3D models a possible substitute or more economical supplement to 3D laser scanning. Currently, 3D models can easily be generated from images obtained with UAVs. There are several existing 3D modeling programs on the market which can do this automatically.  
This article focuses on the measurement of potholes in asphalt pavements, namely depth, width and volume, using a 3D model derived from UAV images. This document aimed to use photographs obtained with a UAV platform and subsequently process them with the Structure from Motion-MultiView Stereo (SfM-MVS) method, in order to perform a better inspection with the primary goal of detecting potholes in pavements. The main contribution of this document is the proposal of recommendations for measuring the geometric characteristics of potholes based on 3D models.  
In order to develop these recommendations, 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, relative to a study surface that simulates deteriorating pavement with bumps of different sizes, with the specific purpose of extrapolating the results to actual pavement. The accuracy was evaluated from the comparison of the actual values against the measurement made in each of the 3D models reconstructed from the data obtained by each flight, according to each of the defined mission plans. Bumpy pavement is modeled using the SfM-MVS technique with photographs acquired using the recommendations and via a UAV platform. A comparison of the geometric characteristics (depth, width and volume) of pavement potholes--measured in a 3D model versus those collected in field inspection--demonstrates that the use of the recommendations when extrapolated to real cases is applicable in the practice of pavement inspection.