## 5 Results and Discussions  
We evaluated the prediction capability of the networks on the testing set (1550 tiles and ground-truth masks) using common performance metrics for binary operations: the IoU score, IoU = TP/(TP + FP + FN), and the F1 score, F1 = 2 \(\times\) [(P \(\times\) \(R\))/(\(P\) + \(R\))]. For analysis purposes, we also accounted the loss on the testing set as a performance indicator (a lower loss value being desired). We compared the performances of the proposed hybrid configurations and the standard architectures trained from scratch using one-way analysis of variance (ANOVA) with the metrics IoU score, F1 score and loss as dependent variables and the configurations as fixed factors. To test the null hypothesis that the performances of all models are equal, we computed the \(F\)-statistic from the ANOVA table (results are reported in Table 1 for each of the metrics). The alternative hypothesis is that the performance of at least one configuration is different than the others. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Configuration**} & \multicolumn{2}{c}{**IoU Score**} & \multicolumn{2}{c}{**F1 Score**} & \multicolumn{2}{c}{**Loss**} \\ \cline{2-7}  & **M  
In Table 1, we can see that all \(p\)-values are smaller than 0.001 (a \(p\)-value \(<\) 0.05 implies that the null hypothesis is to be rejected at a level of confidence of 95%); therefore, configurations are significantly different in terms of the IoU score as well as the F1 score and loss. The results obtained on our dataset are homogenous and show a significant improvement over the standard state-of-the-art models (2.76-3.45%), proving that more complex hybrid semantic segmentation models have a stronger ability to extract complex geospatial elements such as secondary roads. However, the analysis of variance of \(F\)-statistics and their \(p\)-values does not reveal which configurations are different from the others when there is a significant difference. To have a detailed comparison of the hybrid configurations in terms of performance metrics, in Figure 3 we can find the boxplots for the fifteen proposed configurations. The performance of the configurations seems to follow a similar pattern. Figure 3 shows that Configuration 7 (with LinkNet as base architecture and EfficientNet as backbone) has the highest median IoU score and median F1 score, and is closely followed by configurations 5 and 2. However, configuration 7 also has the highest variability in the performance metrics. Next, we applied [PERSON]'s HSD (Honestly Significant Difference) test to compare the performances in terms of the IoU Score and identify the best performing ones. The test conducts a comparison between two configurations at a time using a t-test adjusted for overall variability of the data, while maintaining the level of significance (or the probability of type I error) at 5%. In Table 2, the post-hoc test results are presented in terms of homogenous subsets of configurations for the IoU Score using [PERSON]'s HSD test described above. The homogenous subsets reported in Table 2 contain proposed configurations whose performances are not significantly different from each other at a level of significance of 5%. For example, configurations 12, 4, 15, 10, 2 and 7 do not have a significantly different IoU Score. On the other hand, configurations that are not common in two homogenous subsets have significantly different performance. These post-hoc test results support our observations in Figure 3's boxplots. When grouping the proposed networks by their backbones, we observe significant differences in the performance scores. The highest average IoU score and the lowest average loss is attained by SEResNeXt50, closely followed by EfficientNet, while the highest average F1 score is attained by EfficientNet, closely followed by SEResNeXt50. These two networks seem to have a similar  
Figure 3: Boxplots of performance metrics obtained by the proposed hybrid configurations.