# Selection of Grid Road Networks Based on Raster Data
## 2 Related Work  
Based on the different techniques applied, the approaches designed for road network selection can be summarized in six categories: semantics-based [8], graph-based [11; 13; 16], stroke-based [9; 14; 17], mesh-based [15], integrated [18; 19; 20], and machine learning [21; 22; 23] approaches. Semantic road selection methods came earliest, and they can remove secondary road segments according to road grades [8] but cannot consider other important road features, for instance, global structural and local connectivity features. Road selection techniques leveraging graph technology can adequately take into account the contextual information [13], distribution pattern [16], and connectivity [11] characteristics and are relatively popular. The stroke technique is another method that is widely used in road network selection. In stroke-based road network selection methods, the continuity, hierarchical structures [9], and important functions [24] of road networks can be preserved well. The mesh-based selection method has a good ability to maintain the connectivity, attribute, geometry, and topology characteristics [15] by analyzing the road density and variations. In addition, some methods of integrating technologies have been developed for road network selection. For example, [PERSON] and [PERSON] [17] integrated mesh and stroke techniques to perform selection for street networks, and the advantages of mesh-based and stroke-based selection could both be preserved. [PERSON], et al. [25] developed a selection approach for railway marshaling lines by designing multiple characteristic constraints, and this approach shows a good ability to preserve the structure, connectivity, geometry, and topology characteristics. Recently, with the rise of machine learning, this technology has been increasingly applied to road selection for map generalization. [PERSON], et al. [21] propose an intelligent road network selection method based on a graph neural network (GNN), which simultaneously considers semantic, geometric, and topological features, achieving over 90% accuracy. [PERSON], et al. [22] propose an automatic road selection method for small-scale maps, combining machine learning with data enrichment to address contextual information scarcity. [PERSON], et al. [23] propose an automatic road network selection method using graph convolutional networks (GCNs), incorporating functional semantic features like the travel path selection probability and regional attractiveness. In addition to those for road networks, some selection approaches have also been developed for river networks [26; 27; 28]. Since river networks are usually natural, while road networks are artificial, the design of the river network selection method is quite different from that of the road network selection method. For example, [PERSON], et al. [29] applied the watershed division technique to the progressive selection of river networks, and [PERSON], et al. [30] used simplification and pruning operations for the multiscale representation of river networks. However, in all the above selection methods, although many important characteristics, such as the attributes, structure, connectivity, geometry, and context, are preserved well, three problems still exist, as follows:  
1. Basically, all the extant selection methodologies are designed for vector data, whereas the exploitation of raster data for road network analysis has yet to be investigated. This is mainly because vector data are abstract points, lines, and areas, but raster road networks are unstructured matrix forms, which makes it difficult to select raster road networks without high-level abstract information;
2. Since vector road networks are linear features that lose the width structure, the road grade information that plays an important role in selection cannot be effectively considered without manual statistics, which can affect the credibility of the outcomes.