## 3 Results  
In this study, the effect of different positioning techniques on the accuracy of UAV-based photogrammetric products was compared. Different GCP distribution models were created using the GCP network created in Istanbul Technical University Ayazaga Campus. The effect of GCP distribution on the accuracy of UAV-based photogrammetry was investigated. Georeferenced orthomosaic and DEM were produced using the SfM algorithm with the DJI Terra software. There is no difference between the positioning techniques and GCP distribution models in terms of visualization of the final products. The obtained photogrammetric products are presented in Figure 9.  
RMSE values of different dispersion models are presented in Table 3. According to Table 3, 3D position accuracy improves significantly when GCP is added for RTK, PPK, PPP-AR. When the image coordinates obtained by RTK are balanced with the GCPs selected in Model-3, an RMSE of 1.4 cm is obtained. Vertical error is significantly reduced when GCP is added to all three methods. When applying indirect georeferencing, no results were produced without using GCP. This is because indirect georeferencing is a model that depends on GCPs measured with GNSS on the ground instead of the precise positioning module of the UAV. The impact of different GCP distribution models on accuracy is limited. With the RTK method, horizontal accuracy varies between 1.4-2.1 cm and vertical accuracy varies between 1.9-2.5 cm in all distribution models. The horizontal accuracy of the model generated with PPK is between 1.2-2.8 cm, and the vertical accuracy is between 1.3-1.9 cm. Similar to RTK, vertical accuracy increases significantly when GCP is used in the model generated with PPP-AR. Horizontal accuracy has values between 1.2-2.5 cm, and vertical accuracy is between 1.8-3.4 cm. The lowest horizontal RMSE was achieved using Model-3 in all techniques. Although there are similar numbers of GCPs in Model-6 to Model-3, only a small number of GCPs selected from the central region result in a lower horizontal accuracy than Model-3. Although Model-1 includes 35 points homogeneously distributed in the study area, similar RMSE values were obtained with other GCP distribution models. Additionally, for each CP, the difference of the casting, northing, and height values from the reference values is presented with box plot graphs (Figure 10). Difference values  
Figure 9: Orthomosaic (on **left**) and DEM (on **right**) produced as a result of photogrammetric evaluation. are generally normally distributed. In the RTK method, there are no outliers in both horizontal and vertical differences for all models. The median value for horizontal differences is around 2.5 cm for all models, while it varies between 4.5-6 cm for vertical differences. In the PPK method, there are outlier values in vertical differences in each distribution model. The median values of the vertical differences of the distribution models are around 3 cm. However, horizontal differences are gathered in a narrower range except for Model-5. There are no outliers in the horizontal differences, which are spread over a wide range in Model-1, Model-4, and Model-6. The lowest median value for horizontal differences was 1.9 cm in Model-4, while the highest median value was 3.15 cm in Model-6. The lowest horizontal difference value is 0.2 cm in Model-2, while the highest horizontal difference value is 5.6 cm in Model-4 and Model-5. The lowest vertical difference value is 0.1 cm in Model-2, and the highest vertical difference value is 8.5 cm in Model-4. When GCP is not used in the PPP-AR method, there are points with a vertical difference of more than 14 cm. The effect of Model-6 on vertical values is less than the other models. The lowest value in horizontal differences is 0.6 cm in Model-6, and the highest value is 4.8 cm in Model-1. The lowest values in the vertical difference were obtained in Model-1 and Model-2, which have homogeneous distributions.