## 3 Materials and Methods
### Training Model and Building Footprint Extraction  
Mask R-CNN, with ResNet101 as a backbone, was trained with a bigger dataset in this step to increase the model accuracy. Due to the lack of a public training dataset for the study area, 8201 building footprints were created manually and labeled as building in the ArcGIS Pro 2.8 to prepare data for the training process. The orthophotos were used to export image chips using the geoprocessing tool Export Training Data for Deep Learning in ArcGIS. The rotation angle was set at 90 degrees for data augmentation. The training dataset consisted of 23,217 image chips, each with a size of 512 \(\times\) 512 pixels, processed on a Windows 10, Deep Learning workflow in ArcGIS with the following computer specification: GPU NVIDIA GTX 2080 Ti, 32 GB RAM. As noted above, the experimental model selected was Mask R-CNN with ResNet101 as the backbone, epochs = 20, batch size = 2, and the framework was Pytorch. The performance of the Mask R-CNN model was measured using three of the accuracy metrics used in the previous step, namely Recall, F1-Score, Average Precision. However, there was no major increase in the trained model's performance, largely due to the variation in buildings' characteristics in different areas, as missing building features leads to low model performance. Thus, in the next step, we used this trained model as a pre-trained model to fine-tune a new model with a dataset consisting of part of Zone B, including 3135 image chips. This step was designed to update the model by enabling it to learn building features that were not present in the Zone A dataset and thus improve the model accuracy. The results indicate that the model performance significantly improved after training with the previously established pre-trained model. Table 3 compares the performance achieved by the trained models after the first and second training step. Although the precision of the model after the second training step was lower than that after the initial training, the scores for Recall, F1 score, and average accuracy are all considerably higher for the model that had undergone the second training step. The fine-tuned DL model was then used to detect building footprints. The geoprocessing tool in ArcGIS, Detect Object Using Deep Learning, generated the polygon feature class showing the building footprint for the BCR calculations. All the buildings in the test area of over 10 square kilometers were detected in less than 5 min, indicating the speed \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Backbone** & **Input Size** & **Precision** & **Recall** & **F1\_Score** & **AP** & **Training Time** \\ \hline \multirow{3}{*}{**ResNet 50**} & 256 & 0.5353 & 0.3163 & 0.3977 & 0.1693 & 3 h 51 m \\ \cline{2-6}  & 512 & 0.7192 & 0.3571 & 0.4773 & 0.2641 & 8 h 33 m \\ \cline{2-6}  & 768 & **0.8360** & **0.3597** & **0.5030** & **0.3008** & 16 h 53 m \\ \hline \multirow{3}{*}{**ResNet 101**} & 256 & 0.6137 & 0.3648 & 0.4576 & 0.2239 & 4 h 39 m \\ \cline{2-6}  & 512 & 0.8318 & **0.4668** & **0.5980** & **0.3976** & 9 h 56 m \\ \cline{2-6}  & 768 & **0.8534** & 0.4405 & 0.5810 & 0.3808 & 19 h 35 m \\ \hline \multirow{3}{*}{**ResNet 152**} & 256 & 0.6573 & 0.4175 & 0.5107 & 0.2748 & 5 h 21 m \\ \cline{2-6}  & 512 & 0.7980 & **0.4065** & **0.5386** & **0.3244** & 11 h 33 m \\ \cline{1-1} \cline{2-6}  & 768 & **0.8216** & 0.3759 & 0.5156 & 0.3128 & 23 h 50 m \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparing the performance of Mask RCNN with ResNet models and input sizes. Here, epochs = 30, batch size = 2, and 10% of the 4402 training samples were used for validation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1 Score** & **AP** \\ \hline
**1 st Training** & **0.8080** & 0.5718 & 0.67697 & 0.4664 \\ \hline
**2 nd Training** & 0.7646 & **0.7493** & **0.7569** & **0.5829** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluating the accuracy of the DL model. of DL for building footprint extraction.