# Selection of Grid Road Networks Based on Raster Data
###### Abstract  
In cartography, generalization is a key process used to simplify complex geographic information, making it suitable for display at different scales while maintaining its essential meaning. When representing high-density road networks on a fixed screen area, overcrowding and loss of clarity often occur. To solve these problems, a road selection operation can be applied. However, traditional methods have primarily focused on structured vector road networks, leaving unstructured raster road networks largely unaddressed. This study introduces a novel technique, Adaptive Road Width Selection (ARWS), designed to improve the multiscale visualization of compact road systems using unstructured raster datasets. The ARWS method begins by segmenting the original raster road network into multilevel superpixels of varying sizes, reflecting the road widths, through neighborhood analysis. Next, road superpixel matching and selection are performed based on the minimum angle and maximum distance rules, alongside shortest-path calculations. Finally, redundant intersection pixels are eliminated to generate the selection results. The proposed ARWS method was evaluated using road network data from Shenzhen, China, producing effective multiscale visualization outcomes. Unlike conventional techniques relying on structured vector data, ARWS excels in preserving the semantic attributes, overall structure, local connectivity, and integrity of road networks. It addresses the challenges of multiscale visualization in dense road networks, offering a robust solution for unstructured raster data. high-density road networks; multiscale visualization; road selection; raster data; superpixel segmentation +
Footnote †: journal: Applied sciences  
structural characteristics and to eliminate short secondary roads while preserving good connectivity and relative distribution density [8; 9; 10]. By the selection operations, high-density roads can be displayed at different levels of detail well, and thus the overcrowding and unrecognizable phenomena can be effectively avoided [11; 12]. Depending on the format, spatial data can be divided into vector and raster data. As shown in Figure 1a, the vector format of road representation can be viewed as a collection of coordinate points. The definition of vector data has many formats, such as.shp and.osm, which are quite complex. Different formats of vector data need to be converted when they are being calculated, which is not convenient for data exchange. However, as shown in Figure 1b, the raster data can be viewed as a regularly arranged collection of pixels, which can be uniformly stored and expressed using matrices, which is helpful for versatility. Traditional road selection methods, such as graph-based [13], stroke-based [14], and mesh-based [15] methods, cannot handle raster road data because they are all designed for vector data. In this case, some raster road data sources, such as remote sensing images (Figure 1b), aerial images, and scanned images, must be converted to vector data before performing selection. During the process of data conversion, the accuracy of the road data can be destroyed, and additional software or algorithms need to be designed. Furthermore, road grade information plays a very important role in road selection. Higher-grade roads always need to be preferentially selected due to their larger widths and more important service roles. Traditional road selection methods based on vector data cannot automatically identify width information; thus, much manual work is required to determine and assign attributes to roads. Therefore, in this study, we propose a novel road selection method based on raster data, which integrates techniques such as neighborhood analysis, graph theory, and minimum spanning trees, along with an adaptive superpixel segmentation strategy tailored for unstructured raster datasets.