## 5 Experiments and Results
### Results Analysis
#### 5.2.1 Localization Models  
First of all, the basic UNet introduced in [37] was used as the starting point. After that, the Attention-Unet [35] which was powered by attention gates on Unet skip-connections, was used. Attention-Unet is powerful at focusing on more important spatial structures of varying shapes and sizes. For the third model, a Residual-Unet [74] was utilized. Although the number of parameters of the Residual-UNet model was almost the same as those of the basic UNet, its performance was improved with the help of residual blocks added to both encoder and decoder paths. Shortcut connections added in each layer of Residual-UNet improved gradient flow through the network [75; 76]. In addition, we tried to empower our networks with pre-trained backbones. Accordingly, for the next model, we replaced the encoder path of the Base-UNet with a pre-trained ResNet34 backbone. Deep features extracted by pre-trained backbones could be beneficial in faster learning and convergence of the network. In the same way, in order to evaluate the performance of more powerful backbones, we selected one of the most accurate pre-trained backbones, the EfficientNetB7 [77], and replaced it with ResNet34. EfficientNetB7 utilizes convolutional blocks that contain Squeeze-and-Excitation modules in their heart, resulting in a competing performance for extracting fine-grained structures. Finally, the best-performing model, EfficientNetB7-UNet in our case, was selected, and the Selective-Kernel Module was added to its endpoint. The addition of the SKM could improve the feature extraction process by applying adaptive receptive field learning to the network. Table 1 presents the details of the six models and their corresponding number of parameters. The overall architectures of these models are also visualized in Figure 13. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Localization Models** & **Extensions** & **Number of Parameters** & **References** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{Base-UNet} & - Fully convolutional encoder-decoder, with skip-connections & 15.6 M (trainable) & [PERSON] et al., 2015 \\  & & & & [37] \\ \hline \multirow{3}{*}{2} & \multirow{3}{*}{Attention-UNet to skip-connections} & \multirow{3}{*}{15.8 M (trainable)} & [PERSON] et al., 2021, \\  & & & & [35, 46] \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{Residual-UNet added to each layer} & \multirow{3}{*}{15.6 M (trainable)} & [PERSON] et al., 2022, \\  & & & [74, 78] \\ \hline \multirow{3}{*}{4} & \multirow{3}{*}{ResNet34-UNet} & - ResNet34 pre-trained weights on ImageNet were used as the backbone & 3 M (trainable) + 21 M (non-trainable) & [PERSON] et al., 2016 \\ \cline{1-1} \cline{3-5}  & & & [75] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The six models that were used for comparison in the first stage of our methodology; the building localization. Figure 13: Schematic diagram showing various architectures that were used for comparison. The xBD benchmark dataset was used in all experiments in a similar way to provide a fair comparison between networks. After training the localization networks for 70 epochs, binary segmentation maps were produced for the Test set, and the evaluation metrics, i.e., precision, recall, F1-score, and IoU, were calculated for all the images. Subsequently, box plots, which indicate the minimum, maximum, and median values, along with the lower and upper quantiles of each metric, were generated. Figure 14 illustrates the box plots of each model, separated by evaluation metrics. In order to generate this plot, all the models mentioned in Table 1 were tested on the same set of images. For all the images, TP, FP, TN, and FNs were obtained based on their building localization results, and then evaluation metrics were obtained. Focusing on boxplots over all the metrics shows that the performance of the first three models (i.e., Base-UNet, Attention-UNet, and Residual-UNet), which did not utilize pre-trained backbones was approximately the same, with a weaker performance for the Attention-UNet.