## 5 Results and Discussions
Figure 5: Boxplots of performance metrics grouped by the base architectures. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dependent** & **Source** & **Sum of Squares** & **Degrees of Freedom** & **Mean Square** & **F** & **Sig.** \\ \hline \multirow{4}{*}{IoU score} & Backbone & 0.0099 & 4 & 0.0025 & 33.367 & 0.000 \\  & Backbone and & 0.0007 & 2 & 0.0004 & 4.978 & 0.010 \\  & Base Architecture & 0.0011 & 8 & 0.0001 & 1.924 & 0.073 \\  & Error & 0.0044 & 60 & \(7.41\times 10^{-5}\) & & \\  & Total & 0.0162 & 74 & & & \\ \hline \multirow{4}{*}{F1 score} & Backbone & 0.0096 & 4 & 0.0024 & 40.382 & 0.000 \\  & Base Architecture & 0.0005 & 2 & 0.0002 & 4.166 & 0.020 \\ \cline{1-1}  & Backbone and & 0.0008 & 8 & 0.0001 & 1.726 & 0.111 \\ \cline{1-1}  & Error & 0.0036 & 60 & \(5.94\times 10^{-5}\) & & \\ \cline{1-1}  & Total & 0.0140 & 74 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ANOVA results for the effects of backbones and base architectures on performance. Studying the relationships between the values of the confusion matrix, we can see that the best performing hybrid model correctly classified 97.87% of the samples, while incorrectly classifying 2.13% of the samples. Configuration 5 correctly detected 94.95% of the pixels as \"No Road\" instances (True Negatives) and 2.92% of the \"Road\" instances (True Positives or TP), while incorrectly labelling 0.94% instances of \"Road\" (False Positives-FP) and missing 1.19% instances of \"No road\" labels (False Negatives-FN). To gain a better interpretation of these results, in Figure 7, we can find examples of predictions (pixel labels) compared to the ground-truth labels. Figure 6: Confusion matrix obtained by configuration 5 trained on the full dataset. Note: The IoU score metric monitored during training does not consider True Negatives (TN); therefore, TN had no impact on the network’s training evolution. Figure 7: Predictions (third row) and IoU scores obtained by configuration 5 trained on 7750 tiles together with their correspondent ground-truth masks (second row). Note: The probability scores are plotted using a colormap that allows a better identification of ambiguous areas (where probability scores are close to 0.5). This way, purple is used to represent pixels labelled with “No road”, yellow is used to represent pixels belonging to the class “Road Exists”, and scales of blue were chosen to represent pixels with probability scores in proximity to 0.5. The hybrid configuration is capable of correctly recognizing secondary roads in aerial images and has learned that the expected representation of road surfaces is a continuous line of fixed width (as seen in the examples from Figure 7a,b). Analysing the predictions' errors, we found that the proposed model generally predicted more False Positive labels in areas where the materials used in the road pavement have a similar spectral signature with their surroundings (ambiguous scenarios where the roads are not clearly delimited, as seen in Figure 7d), or in areas where geospatial objects with similar features are present (such as dry riverbeds, railroads or irrigation canals). On the other hand, a higher rate of False Negatives was encountered in sections where other objects cover large portions of the roads (trees, dense vegetation, etc). However, in Figure 7c, we can see that by training with real-life data, the model was capable of inferring small road segments in scenes where obstructions are present. In general, the segmentation classifier seems to favour the detection of road labels, at the cost of a higher rate of False Positives. However, this metric trade-off is preferable given that, in our case, false negatives yield a lower importance (we want to avoid losing misclassified \"Road\" labels at the cost of adding additional checks to verify the legitimacy of the FN labels). The rates might also be affected by the fact that a considerable number of secondary roads have not been mapped yet and some of them might be present in our dataset.