# Selection of Grid Road Networks Based on Raster Data
## 3 Method for Multiscale Representation of High-Density Transportation Networks
### Multilevel Division of Raster Road Networks Based on the LSC Method  
According to their different semantic characteristics, such as their status, effect, traffic function, and service function, roads typically have ranges in their properties, which can lead to different road widths. To realize the multiscale visualization of high-density raster road networks, the first step is the multilevel division of raster road networks with different widths based on superpixel segmentation. Superpixels are made up of adjacent, regular pixel patches with similar characteristics [31; 32] and can be generated by many algorithms [33; 34; 35; 36; 37]. Within the realm of superpixel segmentation strategies, there are various methods for generating superpixels, such as discrete clustering-based superpixel segmentation [33], geometric flow-based superpixel segmentation [34], and entropy rate superpixel segmentation [35], in which three key algorithms stand out: Simple Linear Iterative Clustering (SLIC) [38], Superpixel Extraction via Energy-Driven Sampling (SEEDS) [39], and LSC (Linear Spectral Clustering) [36], which were developed as application interfaces in the Open Source Computer Vision Library (OpenCV) and are widely used because of their individual advantages. For example, the k-means clustering-driven SLIC (Simple Linear Iterative Clustering) algorithm, which is a commonly used unsupervised learning algorithm that partitions data into k clusters to minimize the variance within each cluster, exhibits enhanced computational efficiency. The SEEDS (Superpixel Extraction via Energy-Driven Sampling) methodology yields superpixels with increased smoothness and homogeneity. In the proposed ARWS method, the LSC superpixel generation algorithm is used for the multilevel division of raster road networks because it can preserve the boundaries of the raster road network originally, which can be shown as follows:  
Step 1: Performing the superpixel segmentation of raster road networks using the LSC method, the segmentation size of the superpixels is approximately consistent with the minimum road width. For example, Figure 1(a) shows original road networks with different widths. The original image size of Figure 1(a) is \(1264\times 1264\) pixels, with LSC superpixel segmentation with a super-oversize of 20 pixels. The division results of the original network are shown in Figure 1(b);  
Step 2: Label the superpixels that need to be further LSC-segmented based on neighborhood analysis. The process of neighborhood analysis is as follows: assuming that \(A\) is a superpixel with an area larger than a fixed value, when any two adjacent superpixels exist in superpixel set \(B\) that belong to the adjacent superpixels of superpixel \(A\), superpixel \(A\) should be further segmented using the LSC method. As can be seen in Figure 1(c), the red superpixel has four adjacent superpixels: two yellow superpixels and two blue superpixels. The yellow superpixels are not adjacent to each other, while the blue superpixels are adjacent to each other. Thus, the red superpixel should be further segmented. When no adjacent superpixel exists in superpixel set \(B\) that belongs to the adjacent superpixels of superpixel \(A\) with an area lower than a fixed value, superpixel \(A\) should not be further segmented using the LSC method. For example, as shown in Figure 2c, the green superpixel has four adjacent yellow superpixels. The yellow superpixels are not adjacent to each other. Thus, the green superpixel should not be further segmented. The above process can be expressed by the following formula:  
\[\text{S}_{\text{A}}= \begin{cases}1,\text{ when }\text{Num}_{\text{AB}}\geq 2 \\ 0,\text{ when }\text{Num}_{\text{AB}}=1\end{cases}  \tag{1}\]  
where \(\text{Num}_{AB}\) represents the number of adjacent superpixels that exist in superpixel set \(B\) that belong to the adjacent superpixels of superpixel \(A\).