CN110008602B - Road network selection method considering multi-feature coordination under large scale - Google Patents
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Abstract
The invention discloses a road network selection method considering multi-feature coordination under a large scale, which comprises the steps of constructing point, arc section and polygon topology for a road network and identifying meshes in the road network; meanwhile, generating a click connection by considering road semantic, geometric and topological characteristics, and identifying a tip arc section and a tip mesh; determining a mesh density threshold and a road stroke connection importance threshold; dividing the types of the roads according to the tip arc sections and the tip meshes; and the like. The advantages are that: by adopting the road network selection method, road network selection can be completed by taking road connectivity and integrity into consideration and coordinating road network characteristics and local density multi-characteristics in the road network selection process; when the large-scale road is selected, the spatial characteristics of the road network can be well maintained; when the large-scale road is selected, the connectivity and integrity of the road network can be better maintained, and the road network structure of the road is well summarized while the connectivity of the road network is considered.
Description
Technical Field
The invention relates to the technical field of geography cartography, in particular to a road network selection method considering multi-feature coordination under a large scale.
Background
The road network on the map is an objective construction of the communication and distribution condition of the road network in the real geographic world, and is a skeleton element of the map. In general, the road network has many grades, complex relationships and network shapes, and therefore, automatic integration of the road network has been a difficult problem. In the road network selection process, the selected emphasis point depends on the scale span, however, the existing research does not limit the comprehensive scale range applicable to the method, and for the automatic map synthesis of the urban large-scale (greater than 1:100000) road network, the construction of the road network is very fine, so that in the automatic comprehensive selection process, not only the connectivity and integrity of the road but also the overall network characteristics and density characteristics of the road network need to be considered.
The road network selection process comprises two aspects: how many and which are selected, when the scale changes, the spatial distribution characteristics of the selection result completely depend on the two factors. Wherein, the former is the quota selection problem, which can be generally solved by a square root model; the latter is a structuring and optimization selection problem, and is always a hot spot of research. In the existing research, a graph theory-based selection method lays a foundation for organizing road network data and considering road network topological constraint, however, the method is difficult to realize the structured selection of the road network. The existing road network selection method comprises the following steps: firstly, connecting road sections into strokes as selected objects by introducing a good continuity (good continuity) principle in Gestalt visual perception, and completing selection according to the importance of the strokes to ensure the connectivity of a road network; secondly, calculating the importance of stroke, and evaluating the importance of stroke by using a length index, wherein the evaluation index is too single; thirdly, considering the length and connectivity of the stroke and the average density of the arc-containing segment, and adding the connectivity, the centrality, the road grade and other semantic information of the stroke in the road network, wherein the second and third methods can effectively simulate the road visual length in manual selection, and consider the road target integrity while maintaining the road connectivity, namely the method can identify the primary and secondary roads, however, the selection aspect of the secondary road is relatively rough, so that the network characteristics of the road network and the local density characteristics of the road network are lost; fourthly, the density of meshes in the road data is used for reflecting the road density of a local area, a density threshold value is obtained, and the selection rate is determined.
Disclosure of Invention
The invention aims to provide a road network selection method considering multi-feature coordination under a large scale, so that the problems in the prior art are solved.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a road network selecting method considering multi-feature coordination under a large scale comprises the following steps,
s1, constructing point, arc section and polygon topology for the road network, and identifying meshes in the point, arc section and polygon topology; meanwhile, generating a click connection by considering road semantic, geometric and topological characteristics, and identifying a tip arc section and a tip mesh;
s2, determining a mesh density threshold and a road stroke connection importance threshold;
s3, dividing the types of the roads according to the tip arc sections and the tip meshes;
s4, judging whether each stroke connection in the divided road stroke types is a stroke connection containing a tip mesh, if not, executing a step S5; if yes, go to step S7.
S5, calculating the importance of the road stroke connection according to the type of the road stroke;
s6, judging whether the importance of the road stroke connection is smaller than a stroke connection importance threshold value or not, if so, deleting the road stroke connection, and if not, keeping the road stroke connection;
s7, collecting the strokes containing the peripheral meshes to obtain a peripheral mesh set;
s8, classifying the end mesh set according to the type of the road links contained in the meshes, processing the identified end meshes larger than the mesh density threshold value, stripping the end meshes with the highest density and the related road link set, and comparing the importance of the road links to obtain the road links with the lowest importance;
s9, deleting the road stroke with the minimum importance; judging whether a suspension arc section is generated or not, if not, deleting the road stroke, and combining topological polygons on the left side and the right side of the road stroke to generate a new mesh; if the suspension arc section is generated, deleting the tail road section of the road stroke in the tail mesh, combining topological polygons on the left side and the right side of the road section, and generating a new mesh;
s10, it is determined whether or not the currently processed peripheral mesh is the last peripheral mesh in the peripheral mesh set larger than the mesh density threshold, and if so, a new mesh is output, otherwise, the process returns to step S8.
Preferably, in the identification process of the tip arc segment in step S1, identifying an arc segment in the road stroke connection, where the number of intersections of all arc segments in the road stroke connection with the tip arc segment is less than 2, and the arc segment is called as the tip arc segment in the road stroke connection; and simultaneously, identifying the existence of the closed arc segments with the same head and tail nodes, wherein the closed arc segments also belong to the tip arc segments.
Preferably, in the identifying step of the peripheral mesh in step S1, the road mesh is identified based on the road network topological relation, and the mesh including the peripheral arc segment in the road link connection is referred to as the peripheral mesh.
Preferably, the mesh density threshold value is determined in step S2 based on the relationship between the mesh density and the number of meshes in the same rank.
Preferably, the mesh density is equal to the ratio of the total length of the road in the smallest area containing the mesh to the area of the mesh.
Preferably, the determination process of the road stroke connection importance threshold in step S2 is determined by the minimum distance visually distinguishable on the graph and the target scale.
Preferably, the method for dividing the road stroke type in step S3 includes the following steps,
s301, to-be-linked with road (S)i) The collection of other road strokes whose head ends meet is marked as StartV (S)i) (ii) a And road stroke (S)i) The other road strokes connected with the end points are integrated into EndV (S)i) (ii) a Road stroke(Si) The number of distal arc segments of (A) is Burrn (S)i) (ii) a Stroke eS with roadiHas a number of road meshes Net (L) associated with the end arc ofi)；
S302, dividing the road strokes into the following 4 types by judging the 4 parameters,
class I road stroke: net (L)i)＝0。
Class II road stroke: interreflection [ StartV (S)i),EndV(Si)]>0 and Burrn (S)i) 1 and Net (L)i)>0。
Class III road stroke: interreflection [ StartV (S)i),EndV(Si)]>0 and Burrn (S)i)>1 and Net (L)i)>0。
Class IV road stroke: interreflection [ StartV (S)i),EndV(Si)]0 and Net (L)i)>0。
Preferably, the order of classification in step S8 is first a mesh containing a similar road stroke, second a mesh containing a similar road stroke, and finally a mesh containing a similar road stroke.
The invention has the beneficial effects that: 1. road network selection can be finished by taking road connectivity and integrity into consideration and coordinating road network characteristics and local density multi-characteristics. 2. When the large-scale road is selected, the spatial characteristics of the road network can be well maintained. 3. When the large-scale road is selected, the connectivity and integrity of the road network can be better maintained, and the road network structure of the road is well summarized while the connectivity of the road network is considered.
Drawings
FIG. 1 is a flow chart of a road network selection method according to an embodiment of the present invention;
FIG. 2 is a schematic view of a distal arc segment and a distal mesh in an embodiment of the present invention;
FIG. 3 is a comparison of estimated secondary roadway mesh density distributions for mesh density thresholds in an embodiment of the present invention;
FIG. 4 is a comparison of estimated primary roadway mesh density distributions for mesh density thresholds in an embodiment of the present invention;
FIG. 5 is a schematic diagram of road stroke classification in the embodiment of the present invention;
FIG. 6 is a 1:5 ten thousand standard drawings in the embodiment of the present invention;
FIG. 7 is a road network selection result based on strokes in the embodiment of the present invention;
FIG. 8 is a net selection result based on meshes in an embodiment of the present invention;
fig. 9 shows a selection result of the routing method according to the present invention in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, the present invention provides a road network selection method considering multi-feature coordination under a large scale, which includes the following steps:
s1, constructing point, arc section and polygon topology for the road network, and identifying meshes in the point, arc section and polygon topology; meanwhile, generating a click connection by considering road semantic, geometric and topological characteristics, and identifying a tip arc section and a tip mesh;
s2, determining a mesh density threshold and a road stroke connection importance threshold;
s3, dividing the types of the roads according to the tip arc sections and the tip meshes;
s4, judging whether each stroke connection in the divided road stroke types is a stroke connection containing a tip mesh, if not, executing a step S5; if yes, go to step S7.
S5, calculating the importance of the road stroke connection according to the type of the road stroke;
s6, judging whether the importance of the road stroke connection is smaller than a stroke connection importance threshold value or not, if so, deleting the road stroke connection, and if not, keeping the road stroke connection;
s7, collecting the strokes containing the peripheral meshes to obtain a peripheral mesh set;
s8, classifying the end mesh set according to the type of the road links contained in the meshes, processing the identified end meshes larger than the mesh density threshold value, stripping the end meshes with the highest density and the related road link set, and comparing the importance of the road links to obtain the road links with the lowest importance;
s9, deleting the road stroke with the minimum importance; judging whether a suspension arc section is generated or not, if not, deleting the road stroke, and combining topological polygons on the left side and the right side of the road stroke to generate a new mesh; if the suspension arc section is generated, deleting the tail road section of the road stroke in the tail mesh, combining topological polygons on the left side and the right side of the road section, and generating a new mesh;
s10, it is determined whether or not the currently processed peripheral mesh is the last peripheral mesh in the peripheral mesh set larger than the mesh density threshold, and if so, a new mesh is output, otherwise, the process returns to step S8.
In this embodiment, the suspension arc segment is an arc with a portion connected and the remaining portion unconnected.
In the embodiment, by adopting the method, road connectivity, integrity, road network characteristics and local density multi-characteristics can be considered in the process of selecting the road network, and road network selection can be completed in a coordinated manner.
Example one
As shown in fig. 2, in the present embodiment, as explained with respect to step S1, the road network selecting method according to the present invention needs to generate a stroke connection while considering the road semantic, geometric and topological features, and perform tip feature recognition such as tip arc segment and tip mesh.
In this embodiment, stroke is derived from the principle of good continuity in the Gestalt cognitive principle, and the concept is generated from the idea of drawing a curve segment with one stroke. Road point, line and surface topology is constructed, and road stroke connection is formed according to information such as arc semantic, direction and length, for example, road stroke connection S in FIG. 21、S2、S3、S4、S5、S6. For the end arc segment, if a certain arc segment in the road stroke connection is connected with all arc segments in the road stroke connectionIf the number of the intersections is less than 2, the arc section is called as a tip arc section in the road stroke connection, and the tip arc section comprises S1Middle arc segment AB, DE, S2Arc segment FG, IJ, S in3Middle arc segment KL, NO, S4Middle arc segments BG, LP, S5Middle arc segment CH, MQ, S6Arc segments DI, IN. Meanwhile, for a closed arc segment with the same head and tail nodes, the closed arc segment also belongs to a tail arc segment.
In the present embodiment, the peripheral meshes are identified from the road network topology, such as meshes i, ii, iii, and iv, and the meshes including the peripheral arcs in the link of the road are referred to as peripheral meshes, such as meshes i, ii, and iv.
Example two
As shown in fig. 3 and 4, in the present embodiment, as explained with respect to step S2, in the road network selecting method of the present invention, two parameters, namely, a mesh density Threshold (TN) and a road stroke connection importance Threshold (TS), need to be calculated and determined, so as to assist in subsequently selecting a road stroke.
In this embodiment, whether the meshes in the road are selected needs to be determined by comparing the road mesh density with a mesh density Threshold (TN); the mesh density is the ratio of the total length of the road in the smallest area containing the mesh to the area of the mesh, and is given by the following formula:
D＝P/A
where D represents the cell density, P is the total length of the path at the cell boundary, and A is the area of the cell.
In the present embodiment, the cell density Threshold (TN) can be determined by a method based on statistical analysis in general, and the density threshold is determined by integrating the relationship between the cell density of the same rank and the number of cells before and after the analysis of the pattern. 1:1 ten thousand of source scale, 1:1 of target scale: in the case where roads are divided into two types, i.e., a main road and a sub road, the meshes are divided into meshes for the main road and meshes for the sub road. The curves in fig. 3 and 4 show the relationship between the density value and the number of cells whose density is the value, and show the comparison of the density distribution of the two types of cells at different scales. FIG. 3 shows that the density value of 0.012m/m is the dividing line of two sections, and the meshes with the density more than 0.012 need to be selected at a 1:5 ten thousand scale; as can be seen from the distribution curve of fig. 4, the mesh density distributions of the two main roads almost match, indicating that the main roads are hardly rejected at a scale of 1:5 ten thousand, and the mesh density Threshold (TN) of 0.012 at a scale of 1:5 ten thousand can be selected.
In the embodiment, whether the stroke in the road is selected or not is determined by comparing the importance of the road stroke with a road stroke connection importance Threshold (TS); the importance of the road stroke differs between the calculation method of the road stroke including the peripheral mesh and the calculation method of the road stroke not including the peripheral mesh. For a road stroke containing a tip mesh, the stroke importance is calculated according to the following formula:
I＝BC×L
wherein I is the importance of stroke; BC is stroke mediation centrality; l is the stroke length.
The importance of stroke is calculated from the following equation for a road stroke containing no tip mesh.
I＝(1+N)×L
Wherein I is the importance of stroke; n is the click connectivity; l is the stroke length.
The road stroke connection importance Threshold (TS) is determined by the minimum distance visually distinguishable on the graph and the target scale. In general, a drawing expert considers that the visually distinguishable distance on the drawing is 0.4mm, and the target Scale (1: Scale)target) Next, the road stroke connection importance Threshold (TS) is calculated according to the following equation:
Ts＝0.4×Scaletarget
EXAMPLE III
As shown in fig. 5, in the road network selection method in the present embodiment, the road link types need to be divided according to the linked road link set of the first end point and the end point of the link, the number of the end arc segments, and the number of the end meshes. Will follow the road (S)i) The collection of other road strokes whose head ends meet is marked as StartV (S)i) (ii) a And road stroke (S)i) The other road strokes connected with the end points are integrated into EndV (S)i) (ii) a Road stroke (S)i) The number of distal arc segments of (A) is Burrn (S)i) (ii) a And roadstroke(Si) Has a number of road meshes Net (L) associated with the end arc ofi)；
S302, dividing the road strokes into the following 4 types by judging the 4 parameters,
class I road stroke: net (L)i)＝0。
Class II road stroke: interreflection [ StartV (S)i),EndV(Si)]>0 and Burrn (S)i) 1 and Net (L)i)>0。
Class III road stroke: interreflection [ StartV (S)i),EndV(Si)]>0 and Burrn (S)i)>1 and Net (L)i)>0。
Class IV road stroke: interreflection [ StartV (S)i),EndV(Si)]0 and Net (L)i)>0. As shown in FIG. 5, the class I road stroke has S1、S2、S3、S4、S9、S11、S12、S13、S14、S15Class II road strokes have S8Class III road strokes have S5Class IV road strokes have S6、S7、S10。
Example four
As shown in fig. 6 to 9, in the present embodiment, three methods are used for road selection, where fig. 6 is 1:5 ten thousand standard map sheets, and fig. 7, 8, and 9 respectively use a road network selection method based on stroke, a road network selection method based on meshes, and the method of the present invention, and integrate from 1:1 ten thousand to 1:5 ten thousand road selection results. Comparing with the standard map sheet result of fig. 6, for the road in the rectangle a, based on the road network selection method result of stroke, the road section a at the end is retained in fig. 7, but the road section b playing a role of communication is lost, so that the road connectivity is damaged; as a result of the mesh-based road network selection method, the road section b is retained in fig. 8, but the road section a is lost, so that the integrity of the road is damaged; the method selected by the invention reserves the road sections a and b in the graph 9, thereby better maintaining the connectivity and integrity of the road network. In addition, for the road in the rectangle B, due to the influence of mesh aggregation, the road network selection method based on stroke cannot detect the complex structure at the position in fig. 7, so that the original structure is lost, and a suspension arc section appears; in the mesh-based road network selection method, although the connectivity of the road network at the position is considered in fig. 8, the structure is obviously changed; according to the selection method, the trunk road at the position is well extracted in the graph 9, and the road network structure at the position is well summarized while the connectivity of the road network is considered.
By adopting the technical scheme disclosed by the invention, the following beneficial effects are obtained:
the invention provides a road network selection method considering multi-feature coordination under a large scale, which is used for selecting a road network from a reverse library, and can finish road network selection considering road connectivity, integrity and the coordination of road network features and local density multi-features; when the large-scale road is selected, the spatial characteristics of the road network can be well maintained; when the large-scale road is selected, the connectivity and integrity of the road network can be better maintained, and the road network structure of the road is well summarized while the connectivity of the road network is considered.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and improvements can be made without departing from the principle of the present invention, and such modifications and improvements should also be considered within the scope of the present invention.
Claims (8)
1. A road network selection method considering multi-feature coordination under a large scale is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
s1, constructing point, arc section and polygon topology for the road network, and identifying meshes in the point, arc section and polygon topology; meanwhile, generating a click connection by considering road semantic, geometric and topological characteristics, and identifying a tip arc section and a tip mesh;
s2, determining a mesh density threshold and a road stroke connection importance threshold;
s3, dividing the types of the roads according to the tip arc sections and the tip meshes;
s4, judging whether each stroke connection in the divided road stroke types is a stroke connection containing a tip mesh, if not, executing a step S5; if yes, go to step S7;
s5, calculating the importance of the road stroke connection according to the type of the road stroke;
s6, judging whether the importance of the road stroke connection is smaller than a stroke connection importance threshold value or not, if so, deleting the road stroke connection, and if not, keeping the road stroke connection;
s7, collecting the strokes containing the peripheral meshes to obtain a peripheral mesh set;
s8, classifying the end mesh set according to the type of the road links contained in the meshes, processing the identified end meshes larger than the mesh density threshold value, stripping the end meshes with the highest density and the related road link set, and comparing the importance of the road links to obtain the road links with the lowest importance;
s9, deleting the road stroke with the minimum importance; judging whether a suspension arc section is generated or not, if not, deleting the road stroke, combining topological polygons on the left side and the right side of the road stroke to generate a new mesh, and executing S10; if the suspension arc segment is generated, deleting the tail segment of the road stroke in the tail mesh, merging topological polygons at the left side and the right side of the segment to generate a new mesh, and executing S10;
s10, judging whether the currently processed peripheral mesh is the last peripheral mesh which is larger than the mesh density threshold value in the peripheral mesh set, if so, outputting a new mesh, otherwise, returning to the step S8;
if the number of intersections of a certain arc section in the road stroke connection and all arc sections in the road stroke connection is less than 2, the arc section is called as a tip arc section in the road stroke connection; for a closed arc segment with the same head and tail nodes, the closed arc segment belongs to a tip arc segment;
the meshes containing the tail arc sections in the road link are tail meshes.
2. The road network selection method considering multi-feature coordination under large scale according to claim 1, characterized in that: the identification process of the tip arc segment in the step S1 is to identify an arc segment in the road stroke connection, where the number of intersections of all arc segments in the road stroke connection is less than 2, and the arc segment is called as the tip arc segment in the road stroke connection; and simultaneously, identifying the existence of the closed arc segments with the same head and tail nodes, wherein the closed arc segments also belong to the tip arc segments.
3. The road network selection method considering multi-feature coordination under large scale according to claim 1, characterized in that: in the process of recognizing the peripheral mesh in step S1, the road mesh is recognized based on the road network topological relation, and the mesh including the peripheral arc segment in the road link connection is referred to as the peripheral mesh.
4. The road network selection method considering multi-feature coordination under large scale according to claim 1, characterized in that: the mesh density threshold value in step S2 is determined based on the relationship between the mesh density and the number of meshes in the same rank.
5. The road network selection method considering multi-feature coordination under large scale according to claim 4, characterized in that: the mesh density is equal to the ratio of the total length of the road in the smallest area containing the mesh to the mesh area.
6. The road network selection method considering multi-feature coordination under large scale according to claim 1, characterized in that: the determination process of the road stroke connection importance threshold in step S2 is to determine the road stroke connection importance threshold by the minimum distance visually distinguishable on the graph and the target scale.
7. The road network selection method considering multi-feature coordination under large scale according to claim 1, characterized in that: the method of dividing the type of the road string in step S3 includes the following,
s301, to-be-linked with road (S)i) The collection of other road strokes whose head ends meet is marked as StartV (S)i) (ii) a And road stroke (S)i) The other road strokes connected with the end points are integrated into EndV (S)i) (ii) a Road stroke (S)i) The number of distal arc segments of (A) is Burrn (S)i) (ii) a Stroke eS with roadiHas a number of road meshes Net (L) associated with the end arc ofi)；
S302, dividing the road strokes into the following 4 types by judging the 4 parameters,
class I road stroke: net (L)i)＝0；
Class II road stroke: interreflection [ StartV (S)i),EndV(Si)]>0 and Burrn (S)i) 1 and Net (L)i)>0；
Class III road stroke: interreflection [ StartV (S)i),EndV(Si)]>0 and Burrn (S)i)>1 and Net (L)i)>0；
Class IV road stroke: interreflection [ StartV (S)i),EndV(Si)]0 and Net (L)i)>0。
8. The road network selection method considering multi-feature coordination under large scale according to claim 7, characterized in that: the classification sequence in step S8 is first a mesh containing class ii road strokes, second a mesh containing class iii road strokes, and finally a mesh containing class iv road strokes.
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| CN112035592B (en) * | 2020-09-07 | 2021-03-23 | 中国测绘科学研究院 | Road network isolated mesh elimination method based on stroke tip characteristics |
| CN112052549B (en) * | 2020-09-09 | 2021-03-05 | 中国测绘科学研究院 | Method for selecting roads in small mesh gathering area |
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