## 3 Materials and Methods
### Case Study  
Under the conditions described above, a mobile laser scanner of the GeoSLAM ZEB-REVO type was employed for the various interior surveys. This scanner is capable of capturing 43,200 measurement points per second, with an instantaneous error margin of \(\pm\)15 mm. These error values refer to the dimensional accuracy at each individual point. However, as noted previously, general acquisition errors--resulting from the scanning environment and the paths taken--can range from 3 to 30 cm across the complete dataset (Figure 11). As already mentioned, the tower is located at a considerable elevation in a position that is difficult to access, and it is only possible to observe it externally either from close proximity or from a much greater distance. This situation necessitated the adoption of alternative technical solutions for the exterior survey, specifically the use of unmanned aerial vehicles (UAVs), which could capture images from all external viewpoints for subsequent processing. For the exterior survey, we primarily relied on photographic imagery, supplemented by a limited number of ground-based measurements obtained with a total station. This led to the application of photogrammetric techniques to complement the data acquired from the interior scans. In the present case study, photogrammetry not only enabled the reconstruction of the element's exterior but also provided color data for the point cloud (Figure 12). Furthermore, this technique allowed the generation of orthophotographs and other derivative products that were instrumental in producing the final survey. Although not a primary objective, this method also facilitated the reconstruction of the surrounding area, thereby enabling a broader contextual analysis of the tower's location at the summit of the mound. It should be noted, however, that successful reconstruction using this technique requires a certain level of expertise, as the quality and accuracy of the results can vary significantly depending on the cameras used and prevailing lighting conditions [22]. In our case, the images were captured using a Mavic Air drone and its integrated camera. Special attention was paid to the fact that the camera employs a so-called \"fisheye\" lens, which introduces image distortion. These distortions had to be calibrated and corrected during the processing phase in order to achieve accurate results. Similarly, the position and scale of the point cloud were approximately correct due to the UAV's GPS capabilities, but they were precisely adjusted using data collected by a total station, which enabled the accurate measurement of various dimensions and reference points on the exterior of the tower (Figure 13). This allowed the structure to be accurately positioned and scaled within the overall model. Additionally, the total station facilitated the measurement of both interior and exterior points, making it possible to align and integrate the external and internal surveys. Figure 11: Resulting alignment between the interior and exterior point cloud datasets. Figure 12: Virtual reconstruction of the Lopera Tower using drone-based photogrammetry. Lastly, photogrammetry was also applied to the interior of the tower in addition to laser scanning, as the point cloud generated by the scanner lacked color information. This step was therefore necessary to provide enhanced visual data of the interior wall surfaces (Figure 14). The resulting photogrammetric model was aligned with the laser scan using shared reference points recorded by the total station, enabling the projection of textures onto the laser-derived point cloud. This comprehensive integration also allowed for a comparative analysis of the outcomes generated by each method (Figures 15 and 16). The interior photographs were captured using a Nikon D5300 fitted with an 18-55 mm Nikkor standard lens. Due to the good lighting conditions inside the tower, no additional equipment, such as flash units, was required. However, a tripod was used to stabilize the camera, thereby minimizing image noise and motion blur.