## 3 Study Area and Data
### Research Data Acquisition and Processing
#### 3.2.4 Streetscape Image Data Processing  
In previous research, most scholars would apply traditional machine learning models (e.g., decision trees, convolutional neural networks) to classify street view images at the image level until the emergence of FCN [18] in 2015, which transforms the last layers of convolutional networks from the last few fully connected layers to the fully convolutional layers, realizing a breakthrough from image-level to pixel-level classification. In this study, we will use FCN, a classical semantic segmentation model, to recognize the pixels in street view images, including more than 100 features such as trees, buildings, billboards, roads, sidewalks, signals, and vehicles, and on the basis of which we will calculate the percentage of each feature in each group of street view images. \begin{table}
\begin{tabular}{c c} \hline \hline
**District** & **Number of Traffic Black Spots** \\ \hline Wong Tai Sin District & 24 \\ \hline Kowloon City District & 140 \\ \hline Kwun Tong District & 49 \\ \hline Sham Shui Po District & 80 \\ \hline Yau Tsim Mong District & 294 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Table showing the number of traffic black spots distributed in various districts of the Kowloon Peninsula. Figure 12: Schematic diagram of sampling points of streetscape image. The basic structure of the entire FCN network is shown in Figure 13.  
In this study, a deep learning fully convolutional network trained on the ADE_20K dataset, which has a pixel contrast accuracy of 81% in the training dataset [47], is used to perform semantic segmentation on the pixel level of the acquired street view images to obtain more than 100 street-level environmental features such as green trees, buildings, roads, and sky. The final input result image of the semantic segmentation model is at \(512\times 512\) pixels and the semantic segmentation result image is shown in Figure 14.  
As can be seen from the result of semantic segmentation, in Figure 14, the model has a better recognition effect, but there is still a slight error. For large-scale street scene recognition, such an error is acceptable. At the same time, the model will also obtain a picture element feature percentage data table, the attributes of which are shown in Table 8.  
The results of the feature occupancy of some elements of the semantic segmentation model for street scene images are displayed in Table 9.  
Figure 14: Graph of semantic segmentation results. Figure 13: FCN semantic segmentation network graph. As shown in Table 9, the syid attribute is the unique number of the street scene sampling point; while each sampling point has four pictures, according to the different direct attributes to differentiate the direction, in the process of data processing, it is necessary to calculate the average of the four-picture occupancy of the same sampling point, to obtain the unique elemental feature occupancy data of each sampling point. At the same time, zero is assigned to the picture occupancy of the feature to avoid null values. The final elemental feature ratio of the semantic segmentation data of street view images for each sampling point is shown in Table 10.  
The coordinates of each sampling point are imported into the road intersection buffer established in the previous section, and the number of sampling points falling into the buffer and the percentage of each element's characteristics are counted as a means of depicting the built environment of the road intersection, and Figure 15 shows the distribution of the sampling points of the streetscape in the road intersection buffer.