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In the field of cartography and geographic information systems, eliminating self-intersection problems is a key step in improving the reliability and robustness of spatial data representations, especially for the multipart polygon with one outer ring (MPOOR). In traditional studies, self-intersected MPOORs were identified by the topological intersection relationships between the line segments, but during map generalization or map spatial calculations, identifying only topological self-intersections is insufficient. Elements (vertices and line segments) that are topologically separated but have a distance smaller than the minimum visible distance (MVD) on the map also need to be considered to avoid graphic conflicts. The former condition is known as a topological self-intersection (TS), while the latter is known as a cartographic self-intersection (CS). Both the above two cases are called visual self-intersections in this paper, and a method for the classification and cleaning of visually self-intersected MPOORs is proposed. First, four visual self-intersection patterns are distinguished and defined, namely, intersecting, separating, collinear and combined, which are further divided into ten refined patterns that can be identified automatically based on the topological and distance relationships between the vertices and line elements. Second, general displacement and shrinkage algorithms are proposed to clean visually self-intersected MPOORs by taking the conflict regions and the MVD as constraints. Finally, real data from the Second National Land Survey of five counties in Guizhou Province in China are used for validation. The experimental results show that all the TS patterns identified by the Martinez-Llario method were successfully captured by the proposed method, and two TS patterns and four CS patterns were newly identified. Moreover, the proposed method yields more reasonable cleaning results that are not only topologically correct but also morphologically consistent and visually distinguishable.

In the geographic information system field, identifying and cleaning self-intersected polygons to achieve simple polygons is a basic and core issue. In traditional research, self-intersected polygons were identified by the topological relationship between the line segments that form polygons. However, in the process of map generalization or map spatial overlay calculations, the elimination of topological self-intersection is not sufficient. Elements (vertices and line segments) that are topologically separated but whose distance is smaller than the minimum visible distance on the map also need to be considered to avoid graphic conflicts. Both the above two cases are defined as visual self-intersection in this article. Aimed at monocyclic polygons, a method of classification and cleaning for visual self-intersection is proposed. First, three basic visual self-intersection patterns, intersecting, separating and collinear, were distinguished and defined and then further divided into ten refined types. Second, the automatic recognition of different visual self-intersection types was achieved by calculating the topological and distance relationships between the vertices of the polygon and the line segments of the polygon, and corresponding cleaning algorithms for different visual self-intersection types were proposed. Finally, the real topographical map data of a typical area in Jiangsu were used for validation. The experimental results indicate that all self-intersections identified by the advanced Martinez-Llario method were successfully captured by the proposed method, and it identified eight more types, accounting for 40% of the total number of visual self-intersection polygons. After human-computer interaction verification, the identification rate of the proposed method was found to reach 100%. In addition, the visual perception of human cartographers and the closure property of polygons were considered simultaneously during the cleaning for each visual self-intersection pattern, so the proposed method also obtained better cleaning results.

Merging is an important operation in the map generalization of land-cover and other coverages. We define structured geographic objects as collections of adjacent areas with homogeneous semantics that are regularly arranged as spatial structures. Existing studies have concentrated on unstructured objects, which will lead to the structured ones losing part or even most of the typical characteristics during merging. Therefore, as a supplement to the existing mature merging method, a targeted method was proposed in this paper to address the merging problem of structured geographic objects. First, structured geographic objects were classified into four typical patterns, and they were identified automatically according to seven spatial structure parameters. Second, a Miter-type buffer transformation was introduced to extract the overall boundary of structured geographic objects, and areas inside the overall boundary were processed with the most appropriate merging operations for their pattern. Finally, the corresponding merged results of structured geographic objects were inserted back into the merged result of the original land-cover data by using the NOT operation, and the spatial conflicts near the boundary were adjusted. We test our method for a dataset of geographical census data for a city in China. The experimental results revealed that compared with state-of-the-art method, the proposed method produces more reasonable generalization result by effectively identifying and maintaining the typical spatial structures; moreover, the proposed method also preserves the planar tessellation characteristic of land-cover data and the balance of area variation in each land-cover class.

As the coding of a dendritic river system can be used to represent the stream order and spatial-structure of a river network, it is always used in river selection, which is a key step in topographic map generalization. There are two categories of conventional hydrological coding systems, one is the top-down approach, and the other is the bottom-up approach. However, the former does not accurately reflect the hierarchies of a dendritic river network, which is produced by catchment relationships, and it is not appropriate for the stream selection of river networks with uniform distributions of tributaries. The latter cannot directly indicate the subtree depth of a stream, and it is not favorable to stream selection of river systems that have topologically deep structures. Therefore, a selection method for dendritic river networks based on hybrid coding is proposed in this paper. First, the dendritic river network is coded through classical top-down Horton coding. Second, directed topology trees are constructed to organize the river network data, and stroke connections are calculated to code the river network in the bottom-up approach. Third, the river network is marked through hybrid usage of the top-down approach and bottom-up approach, and based on the spatial characteristics of the river network, the river network is classified into three kinds of subtrees: deep branch, shallow branch and modest branch. Then, appropriate coding is assigned automatically to different subtrees to achieve river selection. Finally, actual topographic map data of a river system in a region of Hubei Province are used to comparatively validate the hybrid coding system against two existing isolated coding systems. The experimental results demonstrate that the hybrid coding method is very effective for river network selection, not only in highlighting hierarchies formed by catchment relationships but also in the uniform distribution of tributaries.

Automatic thematic map generalization always needs to deal with large datasets whose processes are computationally intensive, and partitioning strategy has been proved to be one of the effective ways to make the generalization of massive data scalable and useable in practice. Nevertheless, when partitioning strategy is introduced to the dissolution operation of long and narrow patches (LN patches) in land-use data, traditional partitioning methods are not conducive to balancing the computation load on machines for each cell in the grid, and grid boundaries easily affect dissolution accuracy and stability of the process. Hence, this paper proposes an improved partitioning method for dissolving LN patches. First, massive patches are meticulously partitioned into varieties of cells based on the area balance of LN patches. Second, the grid boundaries are corrected considering the shape characteristics of the adjacent LN patches. Finally, the actual data from the national geographic census at 1:10000 scale in Guizhou and Yunnan Province in China are used for validation. The experiment shows that the proposed method can improve the computation efficiency and preserve the accuracy at the boundary for dissolution operation of massive data.

The extraction of terrain feature lines is an important yet challenging problem in the processing and usage of contour lines. Most research has focused on identifying terrain feature points by using methods such as the Douglas–Peucker (D–P) algorithm and on determining contour adjacent relations for closed contour lines by using methods such as Delaunay triangulation and its dual Voronoi diagram, with few attempts made to address open contour lines and identify the direction of contour lines. Here, we propose a novel method for terrain feature line extraction based on adjacent relation trees. First, a contour adjacent relation tree is constructed. The tree is used to determine the directions of closed and open contour lines and thereby form a “directed adjacent relation tree” (DART). Terrain feature points are then extracted using the classic D–P algorithm, followed by the identification and connection of valley and ridge points via the aforementioned DART. Finally, integrity compensation for terrain feature lines is performed based on a slope extension algorithm. The effectiveness and accuracy of this method is verified by experimental results obtained from 1:10,000 topographic map data.

Extraction of the skeleton line of complex polygons is difficult, and a hot topic in map generalization study. Due to the irregularity and complexity of junctions, it is difficult for traditional methods to maintain main structure and extension characteristics when dealing with dense junction areas, so a skeleton line extraction method considering stroke features has been proposed in this paper. Firstly, we put forward a long-edge adaptive node densification algorithm, which is used to construct boundary-constrained Delaunay triangulation to uniformly divide the polygon and extract the initial skeleton line. Secondly, we defined the triangles with three adjacent triangles (Type III) as the basic unit of junctions, then obtained the segmented areas with dense junctions on the basis of local width characteristics and correlation relationships of each Type III triangle. Finally, we concatenated the segments into strokes and corrected the initial skeleton lines based on the extension direction features of each stroke. The actual water network data of Jiangsu Province in China were used to verify the method. Experimental results show that the proposed method can better identify the areas with dense junctions and that the extracted skeleton line is naturally smooth and well-connected, which accurately reflects the main structure and extension characteristics of these areas.

Extracting the split line of narrow and long patches is important for the generalization of land-use thematic data. There are two commonly used methods for extracting the split lines: One is based on Delaunay triangulation and the other is based on straight skeletons. However, it is difficult for the straight skeleton method to preserve geometric structure and topological consistency with the original data when dealing with polygons that have irregularity and complexity of junctions. Therefore, we propose an improved jitter elimination and topology correction method for split lines based on a constrained Delaunay triangulation. First, a split line adjustment algorithm based on the geometric structure of the polygon is proposed to eliminate the jitters. Second, a split line topology correction algorithm is proposed for nodes with degree 1 or degree 2, considering the boundary topological constraint. The reliability of the proposed method is verified by comparing it with the straight skeleton method using sample data and the superiority of the proposed method is verified by using actual data from China&rsquo, s geographical conditions census in the Guizhou province.