## 3 Results
### Data Integration and Cross-Validation  
Integration of data from multiple measurement platforms and sensors has a temporal and spatial dimension. Integration is performed first within the measurement platforms: aerial (aircraft, UAV), terrestrial (measurement vehicle with MLS), and surface (measurement boat). Integration of data collected using these platforms is performed after their mutual calibration and time synchronisation. In the first stage, data integration was performed through the time server (Figure 11). As a result of the time integration, a uniform DGNSS timestamp was assigned to all measurements, making it possible to determine the coordinates of the measured points in the local systems associated with the measurement platforms. In the next step, geospatial data from all measurement platforms are fused (Figures 3 and 11). For spatial integration of the data (the next steps after Time Alignment in Data Fusion Algorithm--Figure 4), a modified Q-ST algorithm was used along with a homology point selection algorithm, which is resistant to perturbations in the data, for point cloud transformation. Coordinate transformation was performed based on Formulas (5)-(8), and uncertainties were determined based on Formulas (9) and (10). Geospatial data fusion shown in Figure 11 is performed based on checkpoints: GCP (Ground Control Points) and ICP (Independent Control Point--checkpoint). Nomenclature familiar from photogrammetry and geodesy was adopted for these points. GCP points participated in the Q-ST transformation for calculating the parameters of this transformation, while ICP points were used to check the accuracy of the transformation. GCP and ICP points were measured using GNSS RTK and Total Station measurements. In addition, very high-resolution orthophotos (0.02-0.05 m) were developed from the data acquired from the aerial ceiling, aircraft, and UAVs, which were then used to acquire further control points (ICPs), except that they were 2D. This resulted in a dataset of 3D and 2D control points, which were used for geospatial fusion of the data and evaluation of the accuracy. Figure 11: Spatial integration of data using reference control points. For the acquired images and compiled data in the form of orthophotos from the area around the Stepnica Port (study area e), the Orli Przesmyk (study area f), and the Clowy and Debska Struga Bridge (study area d), an accuracy analysis was carried out. For this purpose, 27 control points were measured. The average pixel size of the orthophotos compiled from UAV images was 0.03 m. The accuracies obtained using the control points are shown in Table 5. The accuracies were approximately within pixel size (GSD), and for the GCP, the average error was 0.7 GSD. For the ICP, the average error was 1.1 GSD, which confirms the accuracy correctness of the orthophoto processing. Table 5 shows the mean errors averaged for all study areas. Orthophoto development accuracies within 1 pixel are difficult to achieve in practice but possible for studies of flat areas. For the acquired and compiled data into a point cloud from the area near the Stepnica Port (study area e), Orli Przesmyk (study area f), Szczecin Venice, and Waly Chrobrego (study area c), 28 control points were measured and used to develop and evaluate the accuracy of MLS and MSLS. Field checkpoints to develop and evaluate the accuracy of MLS and MSLS were (a) the study area Szczecin centre and (b) Brodowski Bridge. The accuracies are collected in Table 6.  
The accuracies shown in Table 6 are at a similar level for both MLS and MSLS. The accuracy of the X and Y coordinates is slightly higher than that of the Z coordinate, which is influenced by the accuracy of DGNSS measurements of control points, which is lower for the Z coordinate.