## 2 Materials and Methods  
### Oblique Photography 3D Semantic Segmentation  
Implementation Pathway for the Semantic Classification of 3D Mesh Data  
The basic principles and objectives of semantic segmentation of 3D data are consistent with those valid for 2D image data [19; 20; 21; 22; 23; 24; 25]. However, unlike raster 2D images, urban 3D models are characterized by spatial dispersion, rich information, and large data scales. It is necessary to consider the characteristics of urban data and research the corresponding solutions in data organization, feature selection, and spatial retrieval for the semantic segmentation of 3D model mesh data from oblique photography. This study considered the following three key problems:  
1. The organization and formatting of 3D data. Each minimum semantic unit needs to be marked and classified. For the 3D modeling results of oblique photography, the data units that can be used for semantic segmentation comprise triangular vertices and facets. Data can also be reorganized to meet the needs of the semantic segmentation of large-scale 3D urban scenes;
2. The classification and calculation of features. Feature selection is critical and normally evaluated manually; however, fusion can also compute features. Overall, the features of 3D data can be categorized into textural, color, spatial geometric, and fusion features. Another key issue in feature calculation is identifying and selecting the feature neighborhood space;
3. Classifier training and classification optimization. The last step of semantic segmentation is classification calculations. Existing machine learning and deep learning algorithms train the classifier using sample data and then classify the test data [26]. An effective classifier must be selected to optimize classification by considering the global continuity of geospatial data. Based on the three challenges above, this study proposes the following path for 3D semantic segmentation of oblique photographs (Figure 1). The main processes are as follows:  
* Step 1: Mapping an oblique photography 3D mesh model to point cloud data. To facilitate machine learning calculations, we performed point aggregation mapping of a 3D mesh model of oblique photography to transform the 3D mesh model into a point cloud model. In point cluster mapping, more abundant spatial location data are obtained while retaining topological relationships between points in the mesh. As a result, this type of point set provides richer semantic information than conventional point cloud data;
* Step 2: Rough geometric segmentation of point cloud data based on spatial clustering. The 3D point dataset obtained in Step 1 was mapped and divided into different geometric regions. Each region is called a superpoint, and all the points that constitute the superpoint possess the same semantic attributes. A spatial clustering method based on regional growth was adopted to conduct the subsequent semantic segmentation based on superpoints, reducing the overall computational complexity;
* Step 3: Point cloud spatial neighborhood search and geometric feature calculation. Feature extractions were performed for the adjacent 3D point regions for which the neighborhood search methodology is key. This study proposes using a geodesic distance calculation based on a surface model that considers the topological constraints of the grid (rendering it a more reasonable method than one that employs Euclidean distances);
* Step 4: Clustering center feature solving. Feature extraction and computation are prerequisites for semantic segmentation. In this study, the geometric features of individual points were first obtained, and then feature fusion was performed on superpoints based on the features of individual points. Compared to individual points, the feature dimension of superpoints was increased. Figure 1: Realization path of semantic classification based on 3D mesh data. * Step 5: Semantic segmentation calculation. Semantic segmentation depends on the characteristics of the classifier.