## 6 Discussion
During the monitoring of the city of \(\dot{\text{Z}}\)iar nad Hronom, we analyzed the economic aspects and cost-effectiveness of the various technologies used for data collection. The terrestrial laser scanner Leica RTC360 represents an investment of approximately 80,000 \(\epsilon\). Although it is a costly device, its high accuracy, scanning speed, and ability to collect detailed data make it suitable for professional projects with high data quality requirements. The Lidaretto mobile laser scanner, priced at around 60,000 \(\epsilon\), offers the advantage of greater flexibility for data collection in urban areas and is suitable for projects where extremely high accuracy is not essential. The Stonex X120 GO scanner, with a price of approximately 40,000 \(\epsilon\), provides a more affordable alternative with competitive results, especially in environments with medium accuracy requirements. On the other hand, a combined setup consisting of an iPhone 13 Pro with an Emlid scanning kit and a GNSS antenna Reach RX, priced at approximately 8000 \(\epsilon\), offers a cost-effective solution. Despite lower costs, it provides sufficient accuracy for basic monitoring applications. The discussion on the cost-effectiveness of these approaches suggests that for projects with limited budgets, the combined smartphone and GNSS antenna setup can be a viable option, especially when accuracy requirements are less stringent. However, for projects with high demands for accuracy and efficient data processing, investing in professional solutions such as the Leica RTC360 or Lidaretto is justified. The final technology selection should consider not only costs but also the project's scope and objectives. The authors in [47] compared Lidar point clouds obtained using two different measurement methods in an urban setting: TLS was performed with a FARO Focus3D X130 laser scanner and a mobile laser scanning device based on SLAM technology, named GeoSLAM ZEB-REVO. The point cloud obtained via TLS from the FARO scanner was generated from 40 scanning positions, with a nominal cloud resolution of 7.6 mm at a distance of 10 m. The average registration error for the 40 scanning positions was 23.1 mm, with a maximum of 38.6 mm. For this laser scanner, the moving noise is defined as 0.3-0.4 mm at 10 m and 0.3-0.5 mm at 25 m. The lower values correspond to surfaces with 90% reflectivity, while the upper values correspond to surfaces with 10% reflectivity. The resulting TLS point cloud contained more than 480 million points. The data obtained by the mobile laser scanner ZEB-REVO consisted of nearly 16 million points, and the measurement took about 18 min. The authors used three CPs for MLS, with RMSE values of 3.3 cm, 4.4 cm, and 4.7 cm. The point clouds from both methods were compared in terms of point density. The average density for the TLS point cloud reached 23,993 points per square meter, while the MLS point cloud had a density of only 806 points per square meter. The density was also verified using histograms; however, the differences in point cloud density are significant. Regardless of how density is calculated, the TLS data are at least 20 times denser than the MLS data. In publication [48], the authors employed the ROAMER mobile laser scanning system mounted on various mobile platforms in the city of Espoo in southern Finland. For monitoring urban structures, they used a mobile laser scanner mounted on the roof of a vehicle. During this measurement, they found that when the mapping unit's speed was maintained below 40 km/h, the point resolution achieved an accuracy of 2-5 cm, which is sufficient for practical applications in an urban environment. Subsequently, they placed the ROAMER mobile laser scanner on a trolley and scanned the urban environment, achieving a density of 244,000 points/s. The MLS system was also tested in non-urban areas when the scanning system was installed on a boat. Thanks to the rapid laser scanning, detailed river topography data were obtained for fluvial applications.