CN112199460B - Vector map long and narrow arc segment identification method - Google Patents
Vector map long and narrow arc segment identification method Download PDFInfo
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Abstract
The embodiment of the invention provides a vector map long and narrow arc segment identification method, which comprises the following steps: obtaining a polygon vertex list by obtaining polygons in a vector map; generating a boundary constraint triangulation network of polygon vertexes, and obtaining skeleton line nodes and associated polygon vertexes through the boundary constraint triangulation network; then, connecting and dividing the polygon to obtain an end point sub-polygon and a branch skeleton line after division; judging the concavity and convexity of the terminal polygon to obtain a convexity and convexity mark list of vertexes; obtaining a dividing line list through skeleton line nodes and end point sub-polygon concave points; selecting standard dividing lines meeting a preset weighted base height ratio from the dividing line list and adding the standard dividing lines into the long and narrow arc segment candidate set; and selecting a dividing line from the long and narrow arc segment candidate set as a final selection result through a compactness standard, and writing the dividing line into the line element layer. By adopting the method, the long and narrow arc segment in the vector map can be directly obtained, and the relevant working personnel can conveniently perform corresponding processing on the long and narrow arc segment.
Description
Technical Field
The invention relates to the technical field of geographic information science, in particular to a vector map long and narrow arc segment identification method.
Background
Vector maps are widely applied to military purposes, daily life and various industries related to resource management, environmental monitoring and space decision, but due to a plurality of uncertain factors in the production, processing and application processes of map data, a phenomenon that the shape characteristics of part of arc segments of some polygonal primitives are completely different from the shape characteristics of the rest arc segments of the polygonal primitives (hereinafter, the phenomenon is referred to as an abnormal problem of the polygonal arc segments) exists in the vector maps, and the phenomenon is expressed as local long and narrow (hereinafter, the arc segments are referred to as long and narrow arc segments), so that the quality of the vector map data is reduced due to the existence of the long and narrow arc segments, the application effect is severely limited, and related work needing map data support is indirectly influenced.
At present, researchers have few researches on how to identify the long and narrow arc segment in the vector map, and no complete solution is proposed in related documents, so that how to identify the long and narrow arc segment in the map data is an important technical problem which needs to be solved in the map making research and practice.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a vector map long and narrow arc segment identification method.
The embodiment of the invention provides a vector map long and narrow arc segment identification method, which comprises the following steps:
acquiring polygons in a vector map, reading vertex information of the polygons, and sorting the vertex information to obtain a polygon vertex list;
generating a boundary constraint triangulation network of polygon vertexes through the polygon vertex list, and obtaining skeleton lines, skeleton line nodes and associated polygon vertexes related to the skeleton line nodes of the polygons through the boundary constraint triangulation network;
dividing the polygon through the connection line of the skeleton line node and the associated polygon vertex to obtain a divided endpoint sub-polygon and a branch skeleton line;
judging the concavity and convexity of the end point sub polygon, and obtaining a concavity and convexity mark list of the vertex of the end point sub polygon according to the judgment result mark arrangement;
acquiring end point sub-polygon concave points in the concave-convex mark list, and acquiring a dividing line list of the end point sub-polygon through the skeleton line nodes and the end point sub-polygon concave points;
selecting a standard dividing line meeting a comparison index from the dividing line list by taking a preset weighted base height ratio as the comparison index, and adding the standard dividing line into a long and narrow arc segment candidate set;
and acquiring a preset compactness standard, selecting the dividing lines meeting the compactness standard from the long and narrow arc segment candidate set through the compactness standard as final selection results, and writing the final selection results into the line element layer.
In one embodiment, the method further comprises:
and combining the boundary constraint triangulation network, solving through a Delaunay growth algorithm, generating the skeleton lines by adopting different strategies according to different triangles in the boundary constraint triangulation network, and recording corresponding skeleton line nodes and associated polygon vertexes associated with the skeleton line nodes through the skeleton lines.
In one embodiment, the method further comprises:
and judging the concavity and convexity of the terminal point polygon by a vector product method.
In one embodiment, the method further comprises:
sequentially drawing circles by taking the skeleton line nodes as circle centers and taking the distances between the skeleton line nodes and the end point sub-polygon concave points as radii;
and acquiring intersection points of the circle and the end point sub-polygon, and forming the partition line list by taking the shortest partition line of the concave point of the end point sub-polygon and the intersection points.
In one embodiment, the method further comprises:
the preset weighting base height ratio is a shape significance index and is used for selecting an optimal segmentation line in the segmentation line list.
In one embodiment, the method weights the formula for calculating the base-to-height ratio, and comprises the following steps:
wherein, PwThe weighted base height ratio P0 represents the base height ratio of the segmentation result, L is the length of the skeleton line in the area surrounded by the segmentation result, and W is the length of the segmentation line.
In one embodiment, the calculation formula of the preset compactness criterion includes:
wherein C is compactness, S is the area of the surface area surrounded by the long and narrow arc section, and P is the perimeter of the surface area surrounded by the long and narrow arc section.
The method for identifying the long and narrow arc segment of the vector map, provided by the embodiment of the invention, comprises the steps of obtaining polygons in the vector map, reading vertex information of the polygons, and sorting the vertex information to obtain a polygon vertex list; generating a boundary constraint triangulation network of polygon vertexes through a polygon vertex list, and obtaining skeleton line nodes of the polygon and associated polygon vertexes related to the skeleton line nodes through the boundary constraint triangulation network; dividing the polygon through the skeleton line nodes and the connection lines of the related polygon vertexes to obtain divided end point sub-polygons and branch skeleton lines; judging the concavity and convexity of the end point sub-polygon, and obtaining a concavity and convexity mark list of the vertex of the end point sub-polygon according to the mark arrangement of the judgment result; acquiring end point sub-polygon concave points in the concave-convex mark list, and acquiring a dividing line list of the end point sub-polygon through skeleton line nodes and the end point sub-polygon concave points; selecting a standard dividing line meeting the comparison index from the dividing line list by taking a preset weighted base height ratio as the comparison index, and adding the standard dividing line into the long and narrow arc segment candidate set; and acquiring a preset compactness standard, selecting a dividing line meeting the compactness standard from the long and narrow arc segment candidate set through the compactness standard as a final selection result, and writing the final selection result into the line element layer. Therefore, the long and narrow arc segment in the vector map can be directly obtained, and relevant workers can conveniently perform corresponding processing on the long and narrow arc segment.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a vector map long and narrow arc segment identification method according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
Fig. 1 is a schematic flowchart of a method for identifying a long and narrow arc segment of a vector map according to an embodiment of the present invention, and as shown in fig. 1, an embodiment of the present invention provides a method for identifying a long and narrow arc segment of a vector map, including:
step S101, obtaining polygons in a vector map, reading vertex information of the polygons, and sorting the vertex information to obtain a polygon vertex list.
Specifically, after a vector map needing to be identified is acquired, polygons in the vector map are acquired, and then vertex information of the polygons in the vector map is read to obtain a polygon vertex list, wherein the vertices can be recorded in the polygon vertex list in a clockwise order.
Step S102, generating a boundary constraint triangulation network of polygon vertexes through the polygon vertex list, and obtaining skeleton line nodes of the polygon and associated polygon vertexes associated with the skeleton line nodes through the boundary constraint triangulation network.
Specifically, a boundary constraint triangulation network of polygon vertices is generated through a polygon vertex list, then, skeleton line nodes of a polygon and associated polygon vertices associated with the skeleton line nodes are obtained through the boundary constraint triangulation network, the specific method is to combine the boundary constraint triangulation network, solve through a Delaunay growth algorithm, generate skeleton lines by adopting different strategies according to different triangles in the boundary constraint triangulation network, and obtain corresponding skeleton line nodes and associated polygon vertices associated with the skeleton line nodes through the skeleton lines.
Step S103, the polygon is divided through the connecting line of the skeleton line node and the related polygon vertex, and the divided endpoint sub-polygon and the branch skeleton line are obtained.
Specifically, the polygon is preliminarily segmented through connecting lines of skeleton line nodes and associated polygon vertexes to obtain the segmented endpoint sub-polygons and the branch skeleton, and the identification of the long and narrow arc segment is completed based on the preliminarily obtained segmentation result, so that the processing object is decomposed into a plurality of simple endpoint sub-polygons from a complex polygon, a large number of unrelated polygon vertexes can be prevented from being brought into the calculation process, and the operation efficiency of the algorithm is improved.
And step S104, judging the concavity and convexity of the terminal sub-polygon, and obtaining a convexity and convexity mark list of the vertexes of the terminal sub-polygon according to the judgment result mark arrangement.
Specifically, after the end point sub polygon is obtained by division, the concavity and convexity of the end point sub polygon are determined, and the concavity and convexity mark list of the vertices of the end point sub polygon is obtained by marking and sorting the determination result.
And 105, acquiring end point sub-polygon concave points in the concave-convex marking list, and acquiring a dividing line list of the end point sub-polygons through the skeleton line nodes and the end point sub-polygon concave points.
Specifically, the method for obtaining the concave points in the vertices of the end point sub-polygon in the concave-convex marking list and obtaining the partition line list of the end point sub-polygon through the skeleton line nodes and the concave points in the vertices of the end point sub-polygon may be: sequentially drawing circles by taking the skeleton line nodes as the circle centers and taking the distances between the skeleton line nodes and the polygonal concave points of the end points as the radii; and acquiring the intersection points of the circle and the end point sub-polygon, and forming a partition line list by taking the shortest partition line of the intersection points and the concave points of the end point sub-polygon.
And step 106, selecting a standard dividing line meeting the comparison index from the dividing line list by taking a preset weighted base height ratio as a comparison index, and adding the standard dividing line into the long and narrow arc segment candidate set.
Specifically, the preset weighted base height ratio is a shape significance index and is used for selecting an optimal segmentation line in a segmentation line list, selecting a standard segmentation line meeting the comparison index from the segmentation line list by taking the preset weighted base height ratio as a comparison index, and adding the standard segmentation line into the long and narrow arc candidate set, wherein the preset weighted base height ratio can be calculated by the following formula:
wherein, PwThe weighted base height ratio P0 represents the base height ratio of the segmentation result, L is the length of the skeleton line in the area surrounded by the segmentation result, and W is the length of the segmentation line.
And 107, acquiring a preset compactness standard, selecting a dividing line meeting the compactness standard from the long and narrow arc segment candidate set through the compactness standard as a final selection result, and writing the final selection result into a line element layer.
Specifically, a preset compactness standard is obtained, a dividing line meeting the compactness standard is screened from the long and narrow arc segment candidate set through the compactness standard to serve as a final selection result, the final selection result is written into the line element layer, and the final selection result is stored in a shp file form when the line element layer is written into, and in addition, a preset calculation formula of the compactness standard comprises the following steps:
wherein C is compactness, S is the area of the surface area surrounded by the long and narrow arc section, and P is the perimeter of the surface area surrounded by the long and narrow arc section.
The embodiment of the invention provides a method for identifying a long and narrow arc segment of a vector map, which comprises the steps of obtaining polygons in the vector map, reading vertex information of the polygons, and sorting the vertex information to obtain a polygon vertex list; generating a boundary constraint triangulation network of polygon vertexes through a polygon vertex list, and obtaining skeleton line nodes of the polygon and associated polygon vertexes related to the skeleton line nodes through the boundary constraint triangulation network; dividing the polygon through the skeleton line nodes and the connection lines of the related polygon vertexes to obtain divided end point sub-polygons and branch skeleton lines; judging the concavity and convexity of the end point sub-polygon, and obtaining a concavity and convexity mark list of the vertex of the end point sub-polygon according to the mark arrangement of the judgment result; acquiring end point sub-polygon concave points in the concave-convex mark list, and acquiring a dividing line list of the end point sub-polygon through skeleton line nodes and the end point sub-polygon concave points; selecting a standard dividing line meeting the comparison index from the dividing line list by taking a preset weighted base height ratio as the comparison index, and adding the standard dividing line into the long and narrow arc segment candidate set; and acquiring a preset compactness standard, selecting a dividing line meeting the compactness standard from the long and narrow arc segment candidate set through the compactness standard as a final selection result, and writing the final selection result into the line element layer. Therefore, the long and narrow arc segment in the vector map can be directly obtained, and relevant workers can conveniently perform corresponding processing on the long and narrow arc segment.
In addition, the long and narrow arc segment in the vector map long and narrow arc segment identification method has the following characteristics: firstly, the skeleton line is often positioned on a branch structure at the tail end of a polygonal skeleton line; secondly, the skeleton lines are always distributed on two sides of the polygonal skeleton line; at least one concave end point in the two end points; and fourthly, the area enclosed by the four layers is long and narrow. Therefore, the spatial relationship between the polygonal main body and the branch structure is described by using the skeleton line reflecting the extension direction and the shape characteristics of the planar elements, and the long and narrow arc segment identification method of the vector map is formed by identifying the long and narrow arc segment according to the spatial relationship.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (6)
1. A vector map long and narrow arc segment identification method is characterized by comprising the following steps:
acquiring polygons in a vector map, reading vertex information of the polygons, and sorting the vertex information to obtain a polygon vertex list;
generating a boundary constraint triangulation network of polygon vertexes through the polygon vertex list, and obtaining skeleton line nodes of the polygons and associated polygon vertexes associated with the skeleton line nodes through the boundary constraint triangulation network;
dividing the polygon through the connection line of the skeleton line node and the associated polygon vertex to obtain a divided endpoint sub-polygon and a branch skeleton line;
judging the concavity and convexity of the end point sub polygon, and obtaining a concavity and convexity mark list of the vertex of the end point sub polygon according to the judgment result mark arrangement;
acquiring end point sub-polygon concave points in the concave-convex mark list, and acquiring a dividing line list of the end point sub-polygon through the skeleton line nodes and the end point sub-polygon concave points;
selecting a standard dividing line meeting a comparison index from the dividing line list by taking a preset weighted base height ratio as the comparison index, and adding the standard dividing line into a long and narrow arc segment candidate set;
acquiring a preset compactness standard, selecting a dividing line meeting the compactness standard from the long and narrow arc segment candidate set through the compactness standard as a final selection result, and writing the final selection result into a line element layer;
wherein, the calculation formula of the preset compactness standard comprises:
wherein C is compactness, S is the area of the surface area surrounded by the long and narrow arc section, and P is the perimeter of the surface area surrounded by the long and narrow arc section.
2. The vector map long and narrow arc segment identification method according to claim 1, wherein the obtaining, by the boundary-constrained triangulation, skeleton line nodes of the polygon and associated polygon vertices associated with the skeleton line nodes comprises:
and combining the boundary constraint triangulation network, solving through a Delaunay growth algorithm, generating skeleton lines by adopting different strategies according to different triangles in the boundary constraint triangulation network, and acquiring corresponding skeleton line nodes and associated polygon vertexes associated with the skeleton line nodes through the skeleton lines.
3. The vector map long and narrow arc segment recognition method according to claim 1, wherein the determining the concavity and convexity of the terminal polygon comprises:
and judging the concavity and convexity of the terminal point polygon by a vector product method.
4. The vector map long and narrow arc segment identification method according to claim 1, wherein said obtaining the partition line list of the end point sub-polygon through the skeleton line node and the end point sub-polygon pit comprises:
sequentially drawing circles by taking the skeleton line nodes as circle centers and taking the distances between the skeleton line nodes and the end point sub-polygon concave points as radii;
and acquiring intersection points of the circle and the end point sub-polygon, and forming the partition line list by taking the shortest partition line of the concave point of the end point sub-polygon and the intersection points.
5. The vector map slit segment identification method of claim 1, further comprising:
the preset weighting base height ratio is a shape significance index and is used for selecting an optimal segmentation line in the segmentation line list.
6. The vector map long and narrow arc segment identification method of claim 5, wherein the formula for calculating the weighted base-height ratio comprises:
wherein, PwThe weighted base height ratio P0 represents the base height ratio of the segmentation result, L is the length of the skeleton line in the area surrounded by the segmentation result, and W is the length of the segmentation line.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1684236A1 (en) * | 2005-01-25 | 2006-07-26 | Samsung Electronics Co., Ltd. | Method, apparatus and medium for three-dimensionally transforming two-dimensional flyover data in three-dimensional graphics environment and for three-dimensionally visualizing two-dimensional flyover data in three-dimensional graphics environment |
| CN102930561A (en) * | 2012-10-22 | 2013-02-13 | 南京大学 | Delaunay-triangulation-based grid map vectorizing method |
| CN109658509A (en) * | 2018-12-11 | 2019-04-19 | 中国测绘科学研究院 | A kind of long and narrow figure spot method of partition, device, computer equipment and storage medium |
| CN110176018A (en) * | 2019-04-18 | 2019-08-27 | 中国测绘科学研究院 | A kind of figure spot merging method keeping structuring atural object contour feature |
-
2020
- 2020-11-11 CN CN202011255234.2A patent/CN112199460B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1684236A1 (en) * | 2005-01-25 | 2006-07-26 | Samsung Electronics Co., Ltd. | Method, apparatus and medium for three-dimensionally transforming two-dimensional flyover data in three-dimensional graphics environment and for three-dimensionally visualizing two-dimensional flyover data in three-dimensional graphics environment |
| CN102930561A (en) * | 2012-10-22 | 2013-02-13 | 南京大学 | Delaunay-triangulation-based grid map vectorizing method |
| CN109658509A (en) * | 2018-12-11 | 2019-04-19 | 中国测绘科学研究院 | A kind of long and narrow figure spot method of partition, device, computer equipment and storage medium |
| CN110176018A (en) * | 2019-04-18 | 2019-08-27 | 中国测绘科学研究院 | A kind of figure spot merging method keeping structuring atural object contour feature |
Non-Patent Citations (1)
| Title |
|---|
| "土地利用图中狭长多边形降维综合一致性改正";江宝得;《测绘科学》;20141231;全文 * |
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