# Selection of Grid Road Networks Based on Raster Data
## 5 Conclusions  
The width information is crucial for preserving the main streets during the selection of route networks. The traditional road selection methods cannot handle unstructured raster road networks and fail to automatically identify width information; thus, the selection results without manually assigning attributes have a certain degree of unreliability. This research introduces a raster-focused approach for selecting road networks, termed AWRS, which autonomously categorizes roads based on the detected width data. In this AWRS method, at the beginning the original raster route, net lines are divided into multilevel superpixels of different sizes according to the road width by neighborhood analysis. Then, road superpixel matching and selection are performed based on Hough line detection and the calculation of the shortest path. Finally, the selection results of road networks are generated by eliminating road intersection pixels. Compared to traditional route network methods, some advantages and disadvantages of the proposed raster-based AWRS method were found, as follows:  
1. Since the proposed raster-based AWRS selection method has the ability of the multilevel division of raster road networks, it can automatically identify the widths of roads, and the semantic feature and local connectivity can always be preserved well, However, these outcomes are unattainable through conventional vector-oriented route selection techniques;
2. Compared with the ArcGIS method, the proposed AWRS method can effectively maintain the global structure, such as the grid pattern of the original road networks;
3. The AWRS raster-based selection technique successfully retains the proportional distribution densities of road networks across various scales of visualization, which performs better at achieving uniform selection compared to the traditional ArcGIS method.  
The proposed AWRS method contributes to the advancement of road network selection by enabling more accurate and reliable road categorization, with potential applications in map generalization, urban planning, and geographic information systems (GISs). The approach offers a significant step forward in cartographic science, facilitating automated, scale-independent road network selection that can be applied in a range of practical fields, from infrastructure development to spatial data analysis.  
However, the limitations of the developed raster-based road network selection method should be addressed. First, the proposed road selection is only applied for the selection of road networks with grid patterns, which means that the road network patterns need to be identified before using it. In the future, a raster-based road selection method that can adaptively select road networks with all different patterns should be further studied. Second, in the multilevel division of raster road networks based on the LSC method, the segmentation parameters need to be manually set; thus, further improvement to automatically determine these parameters is needed.  
**Author Contributions:** Conceptualization, Y.S. and Y.Z.; data curation, R.L.; formal analysis, Y.Z.; investigation, R.L.; methodology, Y.Z. and Y.S.; project administration, Y.S.; resources, R.L.; supervision, Y.S.; visualization, Y.Z.; writing--original draft, R.L.; writing--review and editing, Y.Z. and Y.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was supported by the National Natural Science Foundation of China [Grant Number 42001402].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.