## 2 Materials and Methods
### Image Acquisition and Processing
This requires predefining a spatial resolution on the ground or the ground sampling distance called the Ground Sample Distance (GSD) used to define the resolution of the digital image, as it is the representation of the pixel in the terrain [48; 56]. The correlation between the flight, sensor and image parameters implies inevitable trade-offs between the different factors. That is, features based on the UAV platform, such as altitude and its resistance (flight time), but also geometry and image quality, and processing time. For example, it is clear that the higher the altitude, the more area is covered on the ground and the more area you can fly on one battery charge. At the same time, higher altitudes lead to fewer images per unit area that need to be processed. Unfortunately, altitude also directly affects achievable GSD and therefore the details that can be detected from images. It follows that lower altitudes lead to more images per unit area, resulting in other compensation related to data processing. The higher the spatial resolution (increase in the accuracy) of the sensor, i.e., the larger the images and the more images are processed per unit area and the higher the processing times [57]. The workflow (2) of Figure 2 is based on the SfM-MVS approach to 3D reconstruction models from a set of images in which intrinsic calibration parameters (or internal parameters, are focal length and lens distortion) and extrinsic (used to describe the transformation between the camera and the world) are unknown. To apply the SfM-MVS technique, it is key to start with the feature detection process. A feature is a relevant part of an image that can refer to specific structures in the image itself, ranging from simple structures such as points or edges to more complex structures such as objects. These must be detected by robust methods in order to be able to locate the same characteristics in successive images regardless of image rotation, scale or changes in lighting [45]. Once key points (relevant characteristics) have been located in each image, their matches must be determined in the successive images of the captured set [43; 58]. SfM aims to simultaneously reconstruct the 3D scene structure, positions and orientations of the camera, along with intrinsic calibration parameters. Initially, camera positions are estimated roughly, as photographs with different orientation are mapped and correlated along with common feature sets, assuming that the camera's internal geometry is constant throughout the process [59]. The initial positions of the camera are refined iteratively as more and more solutions are available, which generates the initial point cloud [60]. The point cloud reference system is relative because at this point only relative locations of the camera and scene geometry are obtained [59]. If you need to get a georeferenced SfM model, there are two options: provide camera positions or use ground control points (GPPs). GPPs are artificial targets or natural objects within the area to be mapped with a known position, which can then be identified and used to orient the final model in the computerized work environment. Alternatively, georeferencing and scaling can be performed from known camera positions derived from differential GPS (dGPS) measurements and an inertial measurement unit [47; 61]. Even the latter tools are complementary to using control points (a visually identifiable point, which is known to be exactly positioned) on land, as direct georeferencing is used to provide approximate camera locations and then uses external GCP to obtain a more accurate solution [47; 62]. Before you apply MVS to the point cloud, there is an optional additional step that may be required in projects with large image sets. As the number of images increases, the computational load of such a focus increases rapidly [63]. RAM requirements increase with the number of images used in rebuilding and set a practical limit on the number of images that can match simultaneously.