## 2 Methods
### Construction of the Subway Station Space Model Point Cloud Database
#### 2.3.2 Pre-Processing of Datasets  
The 3D point cloud data required for this study and its labeling work mainly relies on the re-modeling of the existing functional zoned floor plans and the export and labeling of the point cloud data based on the point cloud segmentation of the color labels. The pre-processing of the dataset used the platforms Rhinoceros and Grasshopper. First, the volume box model for each metro station is established using Rhinoceros modeling software  
Figure 3: Parallel projection view of the box model of the volume for some subway stations. \begin{table}
\begin{tabular}{c c c} \hline \hline
**City** & **Line** & **Station** \\ \hline \multirow{6}{*}{Beijing} & Line 8 & Yuzhi Road Station, Olympic Park Station \\  & Line 2 & Xizhimen Station, Xuanwumen Station, Yonghegong Station \\  & Line 10 & Suzhou Street Station, Haidian Huangzhuang Station \\  & Line 13 & Xizhimen Station, Great Bell Temple Station \\  & Line 5 & Songjiazhuang Station, CiQikou Station \\  & Line 1 & Babaoshan Station, Yuquan Road Station, \\ \hline \multirow{6}{*}{Shanghai} & Line 1 & Xinzhuang Station, Waihuan Road Station, Lianhua Road \\  & Line 2 & Luijiazui Station, People’s Square Station \\  & Line 3 & Shilong Road Station, Caoxi Road Station \\  & Line 4 & Hongqiao Road Station, Century Avenue Station \\  & Line 5 & Chunusen Road Station, Yindu Road Station \\  & Line 6 & East Sports Center Station, Huoxia West Road Station \\  & Line 7 & Huamu Road Station, Fanghua Road Station \\  & Line 8 & Shendu Road Station, China Art Palace Station \\  & Line 9 & Gutang Road Station, Jinhai Road Station \\  & Line 10 & Keelung Road Station, Gangcheng Road Station \\  & Line 11 & Disneyland Station, Kangxin Road Station \\  & Line 12 & Jiangpu Park Station, Dalian Road Station \\  & Line 13 & Natural History Museum Station, Fengzhuang Station \\  & Line 16 & Longyang Road Station, Huoxia Middle Road Station \\  & Line 17 & Hongqiao Railway Station, Chuguang Road Station \\  & Pujiang Line & Shendu Road Station, Sanlu Road Station \\ \hline \multirow{2}{*}{Chengdu} & Line 1 & Shengxian Lake, Wenshu Monastery, Tianfu Square \\  & Line 2 & Longping Road, Chengdu Academy of Governance \\ \hline \multirow{2}{*}{Xi’an} & Line 2 & Exhibition Center Station, North Street Station \\  & Line 4 & Wenjing Road Station, Dayan Pagoda Station \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of some metro station data sources. according to the functional division of the floor plan, as shown in Figure 4a,b. The layers are segmented according to the functional division, with each color representing a function The color labels of the box model are the same as the color labels of the subsequent point cloud (Figure 4), which are the non-payment area, payment area, entrance/exit, platform floor, track, accessible lift, escalator, and staircase eight spatial partitions. The box surface is then subdivided into dot matrices with intervals of about 1 m \(\times\) 1 m using the Grasshopper plug-in, as shown in Figure 4c. These point layers are consistent with the original box layers. The point cloud is then exported. The exported point cloud is in the format XYZRGBc, where XYZ are the coordinates of the point cloud, RGB is the color of the point cloud, and c is the label. These point clouds with color labels are then exported to a tkt file, the encoding format is UTF-8, and the exported tkt point cloud file is used as the point cloud dataset for subsequent neural network training. The functional zoning is divided into eight categories, which cover the main functional areas of the metro station and are simplified to a certain extent. For example, the space of entrances and exits is simplified to serve only as the spatial identification of entrances and exits, and no more entrance and exit corridors are added. This is because the regularity of these corridors themselves is too difficult to generalize. In this paper, the point cloud is exported based on a single subway station. Each subway station model has a corresponding txt file.