## 4 Methodology
### Deep Learning Network for Traditional-Style Building Segmentation  
Image segmentation is a necessary step for extracting pixel-level urban features from street-level images. Recent progress in image segmentation based on deep learning enables researchers to extract objects more accurately. However, the existing deep learning network model cannot obtain the fine-grained segmentation of building subclasses, such as those of Chinese traditional-style buildings. To realize the automatic segmentation of traditional-style buildings, we proposed a deep learning network model: Traditional Building Mask Region-based Convolutional Neural Networks (TBMask R-CNN), which is based on pixel-level semantic segmentation, Mask R-CNN [39] and transfer learning. The framework of TBMask R-CNN is shown in Figure 4. The network architecture of TBMask R-CNN is composed of three modules: the backbone architecture, region proposal network (RPN) and head architecture. The backbone architecture uses a deep convolution neural network (CNN) to better extract features from each street view images. The residual network (ResNet) is a CNN architecture that revolutionizes the CNN architectural race and devises an efficient methodology for training deep convolution neural networks. ResNet101 is a 101 layers residual network and compared with ResNet50, has a higher accuracy in this experiment. Therefore, we used the convolution layer weights of the ResNet100 pre-training model based on the Microsoft Common Objects in Context (MS COCO) dataset as the backbone architecture of TBMask R-CNN. The convolution kernels of the first and second layers of ResNet101 mainly extract the low-level features of the building, such as edges, colors, pixels, contours, gradients, etc. The third layer extracts the larger and more complex local features of the building, which is derived from the low-level features of the second layer. By analogy, layer by layer corresponds to more and more complex concepts, that is, higher-level networks recognize higher-level semantic features of building categories. Low-level features are universal features, and they are also suitable for traditional style buildings. Therefore, we used the fine-tuned approach based on transfer learning to train our model. The specific steps are as follows: first, the first and second layers' weights of the pre-training model were frozen; then, the traditional-style building training set and test set were used to train the top-level weights of the backbone network to obtain the features of the official-style buildings, folk-style buildings and nontraditional-style buildings. \begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Full Name** \\ \hline TSV & Tencent Street View \\ TBMask R-CNN & Traditional Building Mask Region-based Convolutional Neural Networks \\ TBVI & Traditional Building View Index \\ STBVI & Street of Traditional Building View Index \\ RPN & Region proposal network \\ ResNet & Residual network \\ ROI & Region of interest \\ AP & Average precision \\ MAP & Mean average precision \\ O-TBVI & The TBVI of official-style buildings \\ F-TBVI & The TBVI of the folk-style buildings \\ O-STBVI & The STBVI of official-style buildings \\ F-STBVI & The STBVI of the folk-style buildings \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of Abbreviations. The region proposal network was used to sample multiscale regions of interest (ROI) for the head network, which was implemented with a full convolution network to input feature map images and output a set of rectangular target proposals with object scores. The region proposal network uses a \(3\times 3\) convolution kernel network to slide on the feature map output by the last shared convolution layer of ResNet101, and maps the features of each sliding window to a low-dimensional feature, which is input to two \(1\times 1\) fully connected network layers: box regression layer and box classification layer.