## 2 Materials and Methods
### Experimental Details
#### 2.5.2 Data Processing Steps and Analysis  
In this study, UAV-based photogrammetry products were generated using indirect georeferencing, RTK, PPK, and PPP-AR techniques. Accuracy analyses were performed on 12 stable test points selected in the study area and six models with different point distributions. Test points were homogenously chosen from the study area (Figure 4). \begin{table}
\begin{tabular}{c c c} \hline
**Equipment** & **Category** & **Feature** \\ \hline \multirow{6}{*}{UAV} & Model & DJI Mavic 3M \\  & Camera Sensor & 20 Megapixels \\  & Max Flight Time (without wind) & 43 min \\  & Max Takeoff Weight & 1050 g \\  & Max Flight Speed (at sea level, no wind) & 15 m/s (Normal Mode) \\  & GNSS & G + R \({}^{1}\) + E + C \\  & & RTK Fix: \\  & RTK Positioning Accuracy & H: 1 cm + 1 ppm \\  & V: 1.5 cm + 1 ppm \\  & Operating Temperature & 10\({}^{\circ}\) to 40\({}^{\circ}\) C (14\({}^{\circ}\) to 104\({}^{\circ}\) F) \\ \hline \end{tabular} \({}^{1}\)GLONASS is supported only when the RTK module is enabled.  
\end{table} 
Table 1: Features of UAV [54]. Figure 3: The study area is located on the Ayazaga Campus of Istanbul Technical University, Türkive. The GCPs used in the models are different from the test points. In this way, the same test points were used in each model (Figure 5). The models were created using 35 existing GCPs. Combinations and the number of points were selected according to criteria such as homogeneity, center, and edge. The accuracy of UAV-based photogrammetry was analyzed with different techniques with a total of 47 points, including check points (CPs) and GCPs. Model-1 is the model in which 35 GCPs are homogeneously distributed over the land. Model-2 contains 20 homogeneously distributed GCPs. Model-3 contains 5 homogeneously distributed GCPs. Model-4 includes 15 GCPs located at the edges of the study area. Model-5 includes 4 GCPs selected from the center of the study area in addition to Model-4. Model-6 consists of 4 GCPs located in the center of the study area. The RINEX data collected in non-RTK mode were then analyzed using PPP-AR and PPK techniques. For RTK, it was connected to the ISKI-UKBS network (Figure 6). ISKI-UKBS network was accessed via mobile internet from the UAV console with a username and password. Real-time correction data were transmitted to the UAV through the network, allowing for the recording of precise coordinate data within the generated images. This enhances the accuracy of the position of the aerial imagery. PPK and PPP-AR techniques were used to obtain camera coordinates. Precise orbit and clock products improved the accuracy of the photogrammetric results. The workflow for both techniques is illustrated in Figure 7.  
Figure 4: Distribution of test points (on **left**) in the study area and GCP sample (on **right**). Figure 5: GCP distribution models generated in the study area. PPK was employed to determine the camera positions based on the RINEX data obtained from the UAV. REDtoolbox software was used with a free trial version [56]. REDtoolbox provides a GNSS PPK processing solution when RTK is not available. The PALA station, part of the ISKI-UKBS network, served as the base station for this study (Figure 8). It is located approximately 5 km from the study area. The distance between the two stations is \(\Delta t=0.001\) and \(\Delta t=0.0001\). The distance between the two stations is \(\Delta t=0.0001\) and \(\Delta t=0.0001\). The distance between the two  
base and the rover is within the suitable range for applying PPK. The solution focuses on determining the relative position between the base station and the UAV's GNSS. In the following stage, camera positions were acquired utilizing the PPP-AR technique, which is based on absolute positioning. The technique was implemented using the open-source software PRIDE PPP-AR version 3.0.5 [57]. This software results from collaborative efforts by numerous GNSS experts at the GNSS Research Center at Wuhan University.