## 5 Results  
### Accuracy of Building Segmentation  
We used the training set and the validation set to build the TBMask R-CNN model, the training set is used to train the network model to adjust the weights, and the validation set is used to minimize overfitting. The test set is only for testing the trained models in order to confirm the actual predictive power of the network.  
Here, two training strategies are designed based on transfer learning. Strategy 1: freeze the convolutional base, which directly uses the model pre-trained on the COCO data set to extract features of the buildings, training set and validation set are only used for the retraining of the pre-trained model's head layer to obtain the classifier of the building categories; Strategy 2: fine-tuning, which trains some layers and leaves others frozen. Here, we freeze the weights of the low-level convolutional layer of the pre-training model ResNet101, and start fine-tuning the parameters from the third layer to obtain the parameters of TBMask R-CNN model.  
The test set is used to evaluate the performance of the trained models by two strategies. We use the evaluation performance indicators of the average precision (AP) of each category and the mean average precision (MAP) of all categories. Table 4 present the evaluation performance indicators of the two trained models on test dataset, which show that the second model using Strategy 2 is better than the first model using Strategy 1. With the second model, the MAP was 0.8, AP of the official-style and folk-style buildings were 0.78 and 0.81, respectively, which indicated that the MAP of the segmentation building could reach 0.8, AP of the segmentation of official-style buildings could reach 0.78, and AP segmentation of folk-style buildings could reach 0.81, which indicated that the second model can predict the architectural style categories. Finally, we applied the second model to predict 272,244 TSV images and complete the pixel-level image segmentation, and the results provide the data source for calculating the TSVI of traditional-style buildings from the pedestrian perspective.  
Figure 5 shows the original TSV images and the manual segmentation and TBMask R-CNN segmentation results for one random location on Dafengxiang Hutong within the Second Ring Road. The first and second columns in Figure 5 present the TSV images in the four horizontal directions and the artificial annotation for the buildings, and the last column shows the results identified by using the pixel-level segmentation model (TBMask R-CNN), which had an accuracy of 0.983. The TSVI value of the location was 1.0 according to Formula (1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strategy** & **Building Style** & **AP** & **MAP** \\ \hline \multirow{3}{*}{Strategy 1} & Official style & 0.53 & \\  & Folk-style & 0.55 & 0.54 \\  & Non-traditional-style & 0.54 & \\ \hline \multirow{3}{*}{Strategy 2} & Official style & 0.78 & \\  & Folk-style & 0.81 & 0.80 \\ \cline{1-1}  & Non-traditional-style & 0.82 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average precision (AP) and mean average precision (MAP) of the TBMask R-CNN model on test dataset.  
Figure 5: Comparison of the segmentation results (116.381335, 39.937614).