CN106599044A - Recognition and processing method for road network target information - Google Patents

Recognition and processing method for road network target information Download PDF

Info

Publication number
CN106599044A
CN106599044A CN201610985705.2A CN201610985705A CN106599044A CN 106599044 A CN106599044 A CN 106599044A CN 201610985705 A CN201610985705 A CN 201610985705A CN 106599044 A CN106599044 A CN 106599044A
Authority
CN
China
Prior art keywords
point
segmental arc
line segment
subpoint
road network
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201610985705.2A
Other languages
Chinese (zh)
Inventor
李成名
吴伟
武鹏达
殷勇
郭沛沛
王伟
赵占杰
程瑶
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Chinese Academy of Surveying and Mapping
Original Assignee
Chinese Academy of Surveying and Mapping
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Chinese Academy of Surveying and Mapping filed Critical Chinese Academy of Surveying and Mapping
Priority to CN201610985705.2A priority Critical patent/CN106599044A/en
Publication of CN106599044A publication Critical patent/CN106599044A/en
Pending legal-status Critical Current

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29Geographical information databases

Landscapes

  • Engineering & Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Remote Sensing (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Processing Or Creating Images (AREA)

Abstract

本发明实施例公开了一种道路网目标信息的识别和处理方法,所述处理方法包括:A、查找空间数据道路网的弧段上的删除点;B、将步骤A中查找到的删除点删除;C、查找空间数据道路网的弧段上的投影点和拟合点;D、将查找到的投影点和拟合点聚类到各个指定处理单元;E、将所述处理单元中的投影点和拟合点分别进行投影处理和拟合处理。由上,本发明实施例有利于修补并准确反映库体道路网实体间的空间关系,形成逻辑上更完善的拓扑结构来满足自动制图综合的需求。

The embodiment of the present invention discloses a method for identifying and processing road network target information. The processing method includes: A. searching for deletion points on the arc segment of the spatial data road network; B. searching for the deletion points found in step A Delete; C, find the projection points and fitting points on the arc segment of the spatial data road network; D, cluster the found projection points and fitting points into each specified processing unit; E, group the Projection points and fitting points are respectively subjected to projection processing and fitting processing. From the above, the embodiment of the present invention is beneficial to repair and accurately reflect the spatial relationship between entities in the road network of the reservoir body, and form a logically more complete topology structure to meet the requirements of automatic cartography synthesis.

Description

一种道路网目标信息的识别和处理方法A method for identifying and processing road network target information

技术领域technical field

本发明涉及地理信息系统制图综合领域,尤其涉及一种为满足计算机自动制图综合的道路网目标信息的识别和处理方法。The invention relates to the field of geographic information system cartography synthesis, in particular to a method for identifying and processing road network target information for automatic computer cartography synthesis.

背景技术Background technique

地图是人们理解世界和改造世界的工具,它的出现极大地改善人们对客观存在的事物以及其周边环境的认识程度。地理信息系统是建立在数字化的地图基础之上的。随着经济的不断地发展,人们对不同比例尺下地理信息空间数据的丰富程度要求越来越高,因此数据库中往往需要尽可能丰富地存储各个比例尺的地理信息空间数据,如:国家基础地理信息系统。在已有基础地理数据库基础之上,如何由大比例尺地图数据库快速而保真地派生出任一小比例尺地图数据库,已成为现代地图学的一个研究热点。The map is a tool for people to understand and transform the world. Its appearance has greatly improved people's understanding of objectively existing things and their surrounding environment. GIS is based on digitized maps. With the continuous development of the economy, people have higher and higher requirements for the richness of geographic information spatial data at different scales, so the database often needs to store geographic information spatial data of various scales as richly as possible, such as: national basic geographic information system. On the basis of the existing basic geographic database, how to quickly and faithfully derive any small-scale map database from the large-scale map database has become a research hotspot in modern cartography.

道路网是地图表达上最基本的地理要素,是构成国家空间信息基础设施框架数据重要的一部分,是地图上用来分割其他人工地理要素的空间分割线,其重要程度和使用频率都高于其他一般地图要素。目前,任何形式的地图的表达都依赖于道路网,道路网是数据库中最重要的一类线状要素,自动制图综合的好坏很大程度上取决于道路网自动综合,因此研究道路网自动综合是十分必要和关键的。The road network is the most basic geographical element in map expression, an important part of the national spatial information infrastructure framework data, and a spatial division line used to divide other artificial geographical elements on the map. General map elements. At present, the expression of any form of map depends on the road network, which is the most important type of linear elements in the database. The quality of automatic cartographic synthesis depends largely on the automatic synthesis of road networks. Synthesis is very necessary and critical.

发明内容Contents of the invention

有鉴于此,本发明的主要目的在于针对现有城市大比例尺地图数据库道路网数据存在的空间关系错误问题,提出一种道路网目标信息的识别和处理方法,通过对组成道路网拓扑关系的特征目标信息进行识别和处理,以修补并准确反映数据库中道路网实体间的空间关系,形成更完善的拓扑结构从而满足自动制图综合的需求。In view of this, the main purpose of the present invention is to propose a method for identifying and processing road network target information, aiming at the problem of spatial relationship errors in the road network data of the existing urban large-scale map database, by analyzing the characteristics of the road network topological relationship Target information is identified and processed to repair and accurately reflect the spatial relationship between road network entities in the database, and form a more complete topology to meet the needs of automatic cartography synthesis.

本发明实施例提供一种道路网目标信息的识别方法,包括以下步骤:An embodiment of the present invention provides a method for identifying road network target information, including the following steps:

A、将空间数据道路网的弧段建立弧段R树;A. Establish an arc segment R tree for the arc segment of the spatial data road network;

B、将查找到的所述弧段树中的预相交的各个成对弧段建立数组;B. Establishing an array for each paired arc segment in the pre-intersected arc segment tree found;

C、对于所述数组中的任一成对弧段及任一成对弧段的第一弧段上的任一组成节点,执行以下步骤:C, for any pair of arcs in the array and any component node on the first arc of any pair of arcs, perform the following steps:

c1、判断成对弧段中的第一弧段上的一组成节点是否在该成对弧段中的第二弧段上;c1, judging whether a component node on the first arc segment in the paired arc segment is on the second arc segment in the paired arc segment;

c2、若否,则将该第二弧段上的每两个相邻的点组成的线段建立线段R树;c2, if not, then the line segment that every two adjacent points on this second arc segment is formed line segment R tree is established;

c3、搜索得到在当前组成节点的外包框向外的指定第一阈值范围内的线段;c3. Search to obtain the line segment within the specified first threshold range outside the outer box of the current component node;

c4、将该第一弧段上的该组成节点作为圆心点,以指定第二阈值为半径作缓冲圆;c4, taking the component node on the first arc as the center point, and making a buffer circle with the specified second threshold as the radius;

c5、判断所述缓冲圆与所述搜索得到的线段是否相交;若是,则记录相交线段、圆心点、该第一弧段和该第二弧段;c5. Determine whether the buffer circle intersects the line segment obtained by the search; if so, record the intersecting line segment, the center point, the first arc segment and the second arc segment;

c6、根据该圆心点与该相交线段的两端点的距离与指定第三阈值的关系,识别该圆心点的类型。c6. Identify the type of the center point according to the relationship between the distance between the center point and the two ends of the intersecting line segment and the specified third threshold.

由上,通过上述方法实现了对道路网目标信息的自动识别。From the above, the automatic identification of road network target information is realized through the above method.

优选地,所述步骤c6包括:Preferably, said step c6 includes:

当判断该圆心点与该相交线段的两端点中的一点的距离小于该第三阈值,且该两端点中的该点位于该第一弧段上,且当该圆心点与该两端点中的任一点拟合后的拟合点与该圆心点在第一弧段的下一个点的距离小于该第三阈值时,则该圆心点为删除点。When it is judged that the distance between the center point and one of the two ends of the intersecting line segment is less than the third threshold, and the point of the two ends is located on the first arc segment, and when the distance between the center point and the two ends When the distance between the fitting point after fitting of any point and the next point of the center point in the first arc segment is less than the third threshold, the center point is a deletion point.

由上,实现了对道路网中删除点的识别。From the above, the recognition of the deleted points in the road network is realized.

优选地,所述步骤c6包括:Preferably, said step c6 includes:

当判断该圆心点与该相交线段的两端点的距离都大于该第三阈值时,则该圆心点为投影点。When it is judged that the distances between the center point and the two ends of the intersecting line segment are greater than the third threshold, the center point is a projected point.

由上,实现了对道路网中投影点的识别。From the above, the recognition of projected points in the road network is realized.

优选地,所述步骤c6包括:Preferably, said step c6 includes:

当判断该圆心点与该相交线段的两端点中的一点的距离小于该第三阈值,且与该相交线段的两端点中的另一点的距离大于该第三阈值时;或者or

当该圆心点与该相交线段的两端点的距离都小于该第三阈值时;When the distances between the center point and the two ends of the intersecting line segment are less than the third threshold;

则该圆心点为拟合点。Then the center point is the fitting point.

由上,实现了对道路网中拟合点的识别。From the above, the identification of fitting points in the road network is realized.

基于上述道路网目标信息的识别方法,本发明实施例还提供了一种道路网目标信息的处理方法,包括以下步骤:Based on the above identification method of road network target information, an embodiment of the present invention also provides a processing method of road network target information, including the following steps:

D、查找空间数据道路网的弧段上的删除点;D. Find the deletion point on the arc segment of the spatial data road network;

E、将步骤A中查找到的删除点删除;E, the deletion point found in step A is deleted;

F、查找空间数据道路网的弧段上的投影点和拟合点;F. Find projection points and fitting points on the arc segment of the spatial data road network;

G、将查找到的投影点和拟合点聚类;G. Clustering the found projection points and fitting points;

H、将聚类后的投影点和拟合点分别进行投影处理和拟合处理。H. Perform projection processing and fitting processing on the clustered projection points and fitting points respectively.

由上,通过上述方法实现了对道路网目标信息的处理。From the above, the processing of the road network target information is realized through the above method.

优选地,所述步骤G包括:Preferably, said step G includes:

将查找到的任一投影点或拟合点为圆心,以指定第四阈值为半径画缓冲圆;Use any found projection point or fitting point as the center of the circle, and draw a buffer circle with the specified fourth threshold as the radius;

将该第四阈值范围内的投影点和拟合点聚类到指定处理单元。The projection points and fitting points within the fourth threshold range are clustered into designated processing units.

由上,实现了对投影点和拟合点的聚类。From the above, the clustering of projection points and fitting points is realized.

优选地,所述步骤H包括:Preferably, said step H includes:

H1、依次判断各个处理单元中是否有投影点;H1, sequentially determine whether there are projection points in each processing unit;

H2、当判断有投影点时,进一步判断投影点的个数是否为1;H2, when judging that there are projection points, further judge whether the number of projection points is 1;

H3、当判断投影点的个数为1时,将该投影点进行投影处理;H3. When it is judged that the number of projection points is 1, perform projection processing on the projection point;

H4、当判断投影点的个数不为1时,进一步判断投影弧段的线段是否为同一条;H4, when judging that the number of projected points is not 1, further judge whether the line segments of projected arcs are the same;

H5、当判断投影弧段的线段为同一条时,将各个投影点做拟合处理后获取拟合点,再将该拟合点到该投影弧段做投影处理;H5. When it is judged that the line segments of the projection arc are the same, each projection point is fitted to obtain the fitting point, and then the fitting point is projected to the projection arc;

H6、当判断投影弧段的线段不为同一条时,将各个投影点做拟合处理后获取拟合点,再将该拟合点到查找到的一条距离该拟合点最近的投影线段做投影处理;H6. When it is judged that the line segments of the projected arcs are not the same, each projected point is subjected to fitting processing to obtain the fitted point, and then the fitted point is found to be a projected line segment closest to the fitted point. projection processing;

H7、当步骤H1中判断处理单元中没有投影点时,进一步判断是否有拟合点;当判断有拟合点时,进行拟合处理。H7. When it is judged in step H1 that there is no projection point in the processing unit, further judge whether there is a fitting point; when it is judged that there is a fitting point, perform fitting processing.

由上,实现了对聚类后的投影点和拟合点的投影处理和拟合处理。From the above, the projection processing and fitting processing of the clustered projection points and fitting points are realized.

综上所述,本申请通过对目标信息的识别和处理,即,通过对删除点、投影点、拟合点的识别和处理,有利于修补并准确反映数据库中道路网实体间的空间关系,形成更完善的拓扑结构从而满足自动制图综合的需求。To sum up, this application helps to repair and accurately reflect the spatial relationship between road network entities in the database through the identification and processing of target information, that is, through the identification and processing of deleted points, projected points, and fitted points. Form a more complete topology to meet the needs of automatic cartographic synthesis.

附图说明Description of drawings

为了更清楚地说明本发明实施例或现有技术中的技术方案,下面将对实施例或现有技术描述中所需要使用的附图作一简单地介绍,显而易见地,下面描述中的附图是本发明的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动性的前提下,还可以根据这些附图获得其他的附图。In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description These are some embodiments of the present invention. For those skilled in the art, other drawings can also be obtained according to these drawings without any creative effort.

图1为本发明库体道路网数据中存在的缝隙和碎片类型图;Fig. 1 is the gap and fragment type figure that exist in the road network data of reservoir body of the present invention;

图2为本发明道路网目标信息分类图;Fig. 2 is a classification diagram of road network target information of the present invention;

图3为本发明道路网目标信息识别方法步骤图;Fig. 3 is a step diagram of the road network target information identification method of the present invention;

图4为本发明道路网目标信息处理方法步骤图;Fig. 4 is a step diagram of the method for processing road network target information of the present invention;

图5为本发明道路网目标信息聚类处理方法步骤图;Fig. 5 is a step diagram of the method for clustering and processing road network target information of the present invention;

图6为本发明实验测试结果对比图。Fig. 6 is a comparison chart of the experimental test results of the present invention.

具体实施方式detailed description

为使本发明实施例的目的、技术方案和优点更加清楚,下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。In order to make the purpose, 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 in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

完成自动制图综合这一任务,关键在于空间数据的一致性。拓扑关系是空间数据一致性的重要内容,本发明以现阶段城市大比例尺地图数据库为研究对象,发现库体数据的拓扑关系普遍存在一些问题,如不同层之间的面状目标虽然在视觉上满足制图需求,但存在事实上的缝隙和碎片,但是现有技术中无法对其进行自动识别及修复,难以满足顾及要素空间约束关系时的自动制图综合需求。To accomplish the task of automatic cartographic generalization, the key lies in the consistency of spatial data. Topological relationship is an important content of spatial data consistency. This invention takes the current urban large-scale map database as the research object, and finds that there are some common problems in the topological relationship of database data. For example, although the planar objects between different layers are visually It meets the needs of cartography, but there are actual gaps and fragments, but they cannot be automatically identified and repaired in the existing technology, and it is difficult to meet the needs of automatic cartography synthesis when considering the spatial constraints of elements.

为克服现有技术中的缺陷,本申请实施例针对城市大比例尺数据库中道路网实体的拓扑关系存在着缝隙和碎片等空间关系不一致的现象,首次提出了一种对道路相交、相离、交织目标分类识别并进行相应处理的方法,该方法实现了对空间关系的自动化修复,有效的保证了道路网空间数据的拓扑完整性。In order to overcome the deficiencies in the prior art, the embodiment of the present application aims at the phenomenon that there are gaps and fragments in the topological relationship of the road network entities in the urban large-scale database. A method for object classification and identification and corresponding processing, which realizes the automatic restoration of spatial relationships and effectively ensures the topological integrity of road network spatial data.

本发明中所述城市大比例尺数据库中的空间实体的拓扑关系存在缝隙和碎片,如图1所示,缝隙和碎片大致分为三种类型,相交、相离、交织。其中:There are gaps and fragments in the topological relationship of the spatial entity in the urban large-scale database described in the present invention. As shown in FIG. 1, the gaps and fragments are roughly divided into three types, intersecting, separating and interweaving. in:

说明1、拓扑属性。当地图的比例尺发生变化时,地图的大小和形状会相应的发生变化,地图图形的某些性质也要相应的发生变化,例如地图图形的长度、面积、角度和相互之间的相对距离。但是某些图形性质则不会发生变化,例如地图图形的邻接性、包含性、相交性和要素的几何类型(如点、线、面类型)等性质。这些在图形连续变化中保持不变的性质称为拓扑属性。Description 1. Topological attributes. When the scale of the map changes, the size and shape of the map will change accordingly, and some properties of the map graphics will also change accordingly, such as the length, area, angle and relative distance between map graphics. However, certain graphic properties will not change, such as the adjacency, inclusion, intersection, and geometric types of elements (such as point, line, and surface types) of map graphics. These properties that remain unchanged in the continuous change of the graph are called topological properties.

说明2、拓扑元素。地理空间中的实体多种多样,其形状也千变万化,但是一般在地图上用三种要素表示,即点要素、线要素和面要素。在二维平面中,它们分别可以对应三种基本的图形元素,即结点、弧段和面域,这三种图形元素称为拓扑元素。Description 2. Topological elements. There are many kinds of entities in geographic space, and their shapes are also varied, but they are generally represented by three types of features on the map, namely, point features, line features and area features. In a two-dimensional plane, they can correspond to three basic graphic elements, namely nodes, arcs and regions, and these three graphic elements are called topological elements.

说明2.1结点包括孤立的点、弧段的端点、弧段的连接点、多边形的内点和多边形的边界点等。Note 2.1 Nodes include isolated points, endpoints of arcs, connection points of arcs, interior points of polygons, boundary points of polygons, etc.

说明2.2弧段是指两个结点之间的有序线段,弧段的两个结点可以是不同的结点,也可以是相同的结点。Note 2.2 An arc segment refers to an ordered line segment between two nodes, and the two nodes of an arc segment can be different nodes or the same node.

说明2.3面域是指由多条闭合弧段链围成的多边形区域,可以用多边形图形来表示。Note 2.3 Area refers to a polygonal area surrounded by multiple closed arc chains, which can be represented by polygonal graphics.

如图2所示,为空间实体目标信息分类示意图。图中,识别的目标信息分为三大类,该三类目标信息,具体为:第一类目标信息为删除点、第二类目标信息为投影点、第三类目标信息为拟合点,其中:As shown in Figure 2, it is a schematic diagram of the classification of spatial entity target information. In the figure, the identified target information is divided into three categories. The three types of target information are specifically: the first type of target information is the deletion point, the second type of target information is the projection point, and the third type of target information is the fitting point. in:

说明1、删除点。如图2(a)所示,弧段A与弧段B相交于点O,弧段A上一点P到弧段B的距离小于阈值,且点P、点O之间的距离小于阈值,则称点P为删除点。Description 1. Delete point. As shown in Figure 2(a), arc A and arc B intersect at point O, the distance from point P on arc A to arc B is less than the threshold, and the distance between point P and point O is less than the threshold, then The point P is called the deletion point.

说明2、投影点。如图2(b)所示,弧段A、B不相交,弧段A上的一点P到弧段B的距离小于阈值,但P与弧段B的点O和点O'的距离大于阈值,则称点P为投影点。Description 2. Projection point. As shown in Figure 2(b), arcs A and B do not intersect, the distance between point P on arc A and arc B is less than the threshold, but the distance between P and point O and point O' of arc B is greater than the threshold , then the point P is called the projection point.

说明3、拟合点。如图2(c)所示,弧段A、B不相交,弧段A上的一点P到弧段B上的点O之间的距离小于阈值,则称点P和点O为拟合点。Description 3. Fitting points. As shown in Figure 2(c), arcs A and B do not intersect, and the distance between point P on arc A and point O on arc B is less than the threshold, then point P and point O are called fitting points .

说明4、基础处理模型。基础处理模型分为三种,第一种,删除点是直接删除点P,如图2(a)中的点P;第二种,投影点是做点P投影到弧段的投影点P',在弧段上插入投影点P',点P移到投影点P',如图2(b)中的点P;第三种,拟合点有两种处理方式,所有拟合点做拟合处理,拟合出一个新点P'或者选择权重最大的一个点P',然后将所有点移到P',如图2(c)中的点P和点O。Description 4. Basic processing model. The basic processing model is divided into three types. The first one is to delete the point P directly, such as the point P in Figure 2(a); the second one is to project the point P to the projection point P' of the arc segment. , insert the projection point P' on the arc, and move the point P to the projection point P', as shown in Figure 2(b). Fitting process, fit a new point P' or select a point P' with the largest weight, and then move all points to P', such as point P and point O in Figure 2(c).

(1)投影点计算公式(1) Projection point calculation formula

首先,求直线系数k:设直线的起点和终点分别为A(x1,y1)、B(x2,y2),直线外一点为C(x0,y0),垂足为D;并设k=|AD|/|AB|。则又因所以,带入坐标,即得,First, calculate the coefficient k of the straight line: Let the starting point and end point of the straight line be A(x 1 , y 1 ), B(x 2 , y 2 ) respectively, a point outside the straight line be C(x 0 , y 0 ), and the vertical foot be D ; and let k=|AD|/|AB|. but And because of so, so Bring in the coordinates, that is,

然后求投影点D坐标(x,y):Then find the projection point D coordinates (x, y):

X=X1+k*(X2*X1) (2)X=X 1 +k*(X 2 *X 1 ) (2)

y=y1+k*(y2*y1) (3)y=y 1 +k*(y 2 *y 1 ) (3)

(2)拟合点计算公式(2) Fitting point calculation formula

其中,(xi,yi)(i=0,1,2,…,n)为所有拟合原点坐标,n为拟合原点个数。Wherein, ( xi , y i ) (i=0, 1, 2, ..., n) are coordinates of all fitting origins, and n is the number of fitting origins.

如图3所示,为本发明道路网目标信息识别方法步骤图,为了实现快速的查找到目标信息,建立弧段arc最小外接矩形框的R树,找到可能是目标信息的成对弧段,然后在对成对弧段上的点进行详细的判断,提高算法的效率。其中,最小外接矩形是指以二维坐标表示的若干二维形状(例如点、直线、多边形)的最大范围,即以给定的二维形状各顶点中的最大横坐标、最小横坐标、最大纵坐标、最小纵坐标定下边界的矩形。道路网目标识别具体步骤如下:As shown in Figure 3, it is a step diagram of the road network target information identification method of the present invention. In order to quickly find the target information, the R tree of the arc minimum circumscribed rectangular frame is established, and the paired arc segments that may be the target information are found. Then make a detailed judgment on the points on the paired arcs to improve the efficiency of the algorithm. Among them, the minimum circumscribed rectangle refers to the maximum range of several two-dimensional shapes (such as points, straight lines, polygons) represented by two-dimensional coordinates, that is, the maximum abscissa, minimum abscissa, maximum The ordinate, minimum ordinate defines the bounding rectangle. The specific steps of road network target recognition are as follows:

S301、建立弧段树。具体的,建立空间数据所有的弧段arc最小外接矩形框的弧段R树。S301. Establish an arc segment tree. Specifically, an arc segment R-tree of the smallest circumscribed rectangular frame of the arc segment arc of all the spatial data is established.

S302、弧段R树中的所有的弧段arc中的可能相交的弧段进行配对,得到各个成对弧段,具体为:对所有弧段arc中的每一个弧段arc的外包框向外扩一个阈值的范围,在所建立的弧段R树中进行搜索,与搜索得到可能与之相交的弧段进行配对,将得到的各个成对弧段放入到弧段可能相交的数组中。S302. The arc segments that may intersect in all the arc segments arc in the arc segment R tree are paired to obtain each paired arc segment, specifically: the outer box of each arc segment arc in all arc segments arc is outward Expand the range of a threshold, search in the created arc segment R tree, pair with the searched arc segments that may intersect with it, and put each paired arc segment into the array that the arc segments may intersect.

S303、从数组中选择一成对的弧段arcA和arcB。S303. Select a pair of arc segments arcA and arcB from the array.

S304、选择该成对弧段上的弧段arcA上的一P点。S304. Select a point P on the arcA of the pair of arcs.

S305、判断当前P点是否在arcB上;若是,则执行S306;若否,则执行S312。S305. Determine whether the current point P is on arcB; if yes, execute S306; if not, execute S312.

S306、若当前节点P在arcB上,则表明arcA与arcB相交,将弧段arcA和arcB识别为相交的结果,对该点的处理结束;S306. If the current node P is on arcB, it indicates that arcA intersects arcB, and the arcA and arcB are identified as the intersecting result, and the processing of the point ends;

S307、判断当前弧段arcA上的是否有未经过S305判断处理的P点;若是,执行S308;若否,执行S309。S307 , judging whether there is a P point on the current arc segment arcA that has not been judged and processed in S305 ; if yes, go to S308 ; if not, go to S309 .

S308,选择当前弧段arcA上未经处理的一P点,并返回执行S305;S308, select an unprocessed point P on the current arc segment arcA, and return to execute S305;

S309,判断各个成对弧段是否有未经过S303的选择处理的成对弧段;若是,则执行S310;若否,则执行311。S309 , judging whether each paired arc segment has a paired arc segment that has not undergone the selection process in S303 ; if yes, execute S310 ; if not, execute 311 .

S310,选择未经过S304选择的一成对弧段,并返回执行S304。S310. Select a pair of arc segments not selected in S304, and return to execute S304.

S311,处理结束。S311, the processing ends.

S312、当步骤S305中判断当前P点不在arcB上时,进行弧段识别。将当前成对弧段的弧段arcB上每两个相邻的点组成的线段segment的外包框建立R树。S312. When it is judged in step S305 that the current point P is not on arcB, perform arc recognition. Build an R tree for the outer box of the line segment formed by every two adjacent points on the arcB of the current paired arc segment.

S313、将当前弧段arcA上点P的外包框向外扩一个阈值的范围搜索,判断是否能够在该阈值范围内的搜索到线段segment。若是,则执行S314;若否,则返回执行S307。S313 , expand the outer frame of the point P on the current arcA to a threshold range to search, and judge whether the line segment can be searched within the threshold range. If yes, execute S314; if not, return to execute S307.

S314,将该当前P点为圆心点,以指定第一阈值为半径作缓冲圆;S314, taking the current point P as the center point, and using the specified first threshold as the radius to make a buffer circle;

S315,判断该缓冲圆与S313搜索到的线段segment是否相交;若否,则执行S316;若是,则执行S317。S315, judging whether the buffer circle intersects the line segment searched in S313; if not, execute S316; if yes, execute S317.

S316,不做记录处理。S316, do not perform record processing.

S317,记录相交线段、圆心点P、当前弧段arcA和当前弧段arcB;S317, record the intersecting line segment, the center point P, the current arcA and the current arcB;

S318,进一步的识别分辨出该圆心点P是属于三类目标信息类型中的哪一类。S318, further identifying and distinguishing which type of the three types of target information the center point P belongs to.

具体的,通过点P与线段AB的端点A和B的距离PA、PB与阈值DistanceEpsilon之间的关系来识别和分辨该点P是属于三类目标信息类型中的哪一类:Specifically, through the relationship between the distances PA, PB and the threshold DistanceEpsilon between the point P and the endpoints A and B of the line segment AB to identify and distinguish which of the three types of target information the point P belongs to:

1、当PA小于DistanceEpsilon且点A在arcA上且P和A的拟合点与点P在arcA的下一个点(延AP方向)的距离小于DistanceEpsilon,则点P为删除点;1. When PA is less than DistanceEpsilon and point A is on arcA and the distance between the fitting point of P and A and the next point of point P in arcA (in the direction of AP) is less than DistanceEpsilon, then point P is the deletion point;

当PB小于DistanceEpsilon且点B在arcA上且P和B的拟合点与点P在arcA的下一个点(延BP方向)的距离小于DistanceEpsilon,则点P为删除点;When PB is less than DistanceEpsilon and point B is on arcA and the distance between the fitting point of P and B and the next point of point P in arcA (in the direction of BP) is less than DistanceEpsilon, then point P is the deletion point;

2、当PA大于DistanceEpsilon且PB大于DistanceEpsilon,则点P为投影点;2. When PA is greater than DistanceEpsilon and PB is greater than DistanceEpsilon, point P is the projection point;

3、当PA小于等于DistanceEpsilon且PB大于DistanceEpsilon,则点P为拟合点;3. When PA is less than or equal to DistanceEpsilon and PB is greater than DistanceEpsilon, then point P is the fitting point;

当PA大于DistanceEpsilon且PB小于等于DistanceEpsilon,则点P为拟合点;When PA is greater than DistanceEpsilon and PB is less than or equal to DistanceEpsilon, point P is the fitting point;

当PA小于DistanceEpsilon且PB小于DistanceEpsilon,则点P为拟合点。When PA is less than DistanceEpsilon and PB is less than DistanceEpsilon, point P is the fitting point.

返回执行S307。直至确定所有的成对弧段的所有的点P都已完成识别处理。Return to execute S307. The recognition process is completed until all points P of all paired arc segments are determined.

如图4所示,基于上述道路网目标信息识别方法,本发明还提供了一种道路网目标信息处理方法,具体步骤如下:As shown in Figure 4, based on the above road network target information identification method, the present invention also provides a road network target information processing method, the specific steps are as follows:

步骤401,查找删除点。对空间数据目标信息识别,所有弧段查找上述的删除点,并记录下来;Step 401, find the deletion point. For the identification of spatial data target information, all arcs are searched for the above-mentioned deletion points and recorded;

步骤402,处理删除点。对删除点采用的处理方式是直接删除弧段上的删除点;重复401,直至查找不到第一类点,即,删除点;In step 402, delete points are processed. The processing method for the deleted point is to directly delete the deleted point on the arc segment; repeat 401 until the first type of point cannot be found, that is, the deleted point;

步骤403,查找投影点和拟合点。对空间数据目标信息识别,所有弧段查找上述的投影点和拟合点,有则记录下来;如果没有则算法结束,处理完成;Step 403, find projection points and fitting points. For the identification of spatial data target information, search for the above-mentioned projection points and fitting points for all arc segments, and record them if there are; if not, the algorithm ends and the processing is completed;

步骤404,投影点和拟合点聚类。把阈值范围内的投影点和拟合点放入一个处理单元。Step 404, clustering the projection points and fitting points. Put the projected and fitted points within the threshold range into one processing unit.

步骤405,分类判断。判断所有的处理单元里面是否有投影点,若有投影点,则执行步骤406;如果没有投影点,则执行步骤411.Step 405, classification judgment. Judging whether there are projection points in all processing units, if there are projection points, then execute step 406; if there is no projection point, then execute step 411.

步骤406,判断处理单元中投影点的个数,当判断投影点的个数为1时,执行步骤407,做投影处理;当判断投影点的个数不为1时,执行步骤408。Step 406, judging the number of projection points in the processing unit, when it is judged that the number of projection points is 1, execute step 407, and perform projection processing; when it is judged that the number of projection points is not 1, execute step 408.

步骤407,做投影处理。Step 407, perform projection processing.

步骤408,判断投影弧段的线段是否为同一条;当判断投影弧段的线段为同一条时,则执行步骤409;当判断投影弧段的线段不为同一条时,则执行步骤410。Step 408, judging whether the line segments of the projected arcs are the same; when judging that the line segments of the projected arcs are the same, go to step 409; when judging that the line segments of the projected arcs are not the same, go to step 410.

步骤409,将多个投影点做拟合处理后,再做投影处理。In step 409, the projection processing is performed after fitting the multiple projection points.

具体为,将多个投影点拟合出新点P',并将该新点P'到该线段做投影处理,且投影线段中间插入点P”,更新所有的处理单元关于插入点弧段的索引信息;并返回执行步骤403。Specifically, multiple projection points are fitted to a new point P', and the new point P' is projected onto the line segment, and a point P" is inserted in the middle of the projected line segment, and all processing units are updated about the arc segment of the insertion point Index information; and return to step 403.

步骤410,将多个投影点做拟合处理后,找到一条距离拟合点最近的投影线段做投影处理,具体为,将多个投影点拟合出新点P',并查找与该新点P'距离最近的线段,并将该新点P'到该距离最近的线段做投影处理,且投影线段中间插入点P”,更新所有的处理单元关于插入点弧段的索引信息;并返回执行步骤403。Step 410, after performing fitting processing on multiple projection points, find a projection line segment closest to the fitting point for projection processing, specifically, fit multiple projection points to a new point P', and search for the new point P' P' is the closest line segment, and project the new point P' to the closest line segment, and insert the point P" in the middle of the projected line segment, update the index information of all processing units on the insertion point arc segment; and return to execute Step 403.

步骤411、判断各个处理单元中是否有拟合点。若是,则执行步骤413。若无,则返回执行步骤403。Step 411, judging whether there are fitting points in each processing unit. If yes, execute step 413 . If not, return to step 403.

步骤412,将处理单元中所有的拟合点做拟合处理,拟合出新点P”,或者选择权重最大的一个点P”,然后将所有点移到P”,并返回执行步骤403。Step 412, perform fitting processing on all the fitting points in the processing unit to fit a new point P", or select a point P" with the largest weight, then move all points to P", and return to step 403.

如图5所示,对于上述步骤404,投影点和拟合点聚类。本发明的实施例还提供了一种目标信息聚类算法,根据目标信息的空间关系,将目标信息聚类为各个处理单元,具体步骤如下(由于删除点是不需要聚类的,所以目标信息的聚类只考虑投影点和拟合点):As shown in FIG. 5 , for the above step 404 , the projection points and fitting points are clustered. The embodiment of the present invention also provides a target information clustering algorithm. According to the spatial relationship of the target information, the target information is clustered into each processing unit. The specific steps are as follows (because the deletion point does not need to be clustered, the target information clustering only considers projected and fitted points):

步骤501、选择S301-318中已识别分辨出的一点P(该实施例中点P是指投影点和拟合点)。Step 501. Select a point P that has been identified in S301-318 (the point P in this embodiment refers to the projection point and the fitting point).

步骤502、新建一包含当前点P的处理单元。Step 502, create a new processing unit including the current point P.

步骤503、将该当前点P的外包框向外扩一个阈值范围,将该范围内的投影点和/或拟合点放入R树。Step 503 , expand the outer bounding box of the current point P by a threshold range, and put the projected points and/or fitted points within the range into the R tree.

步骤504、逐个判断S301-318中已识别分辨出其他点P是否在该R树中,若否,则执行步骤505;若是,则执行步骤506。Step 504, judge one by one whether other points P identified in S301-318 are in the R-tree, if not, execute step 505; if yes, execute step 506.

步骤505、选择一不在该R树中的点P;返回执行步骤502。Step 505 , select a point P not in the R-tree; return to step 502 .

步骤506、将步骤504中判断在R树中的点P放入步骤502中新建的处理单元中。Step 506, put the point P in the R-tree determined in step 504 into the newly created processing unit in step 502.

步骤507、判断S301-318中已识别分辨出的点P是否都被放入步骤502中新建的处理单元中。若是,执行步骤508;若否,返回执行步骤504。Step 507, judging whether the identified points P in S301-318 are all put into the newly created processing unit in step 502. If yes, go to step 508 ; if not, go back to step 504 .

步骤508,聚类处理完成。Step 508, the clustering process is completed.

如图6所示,为本发明实验测试结果对比图,本算法在成都650平方公里的城市道路网数据上做了大量的测试,给定的实验阈值为0.1米,得到的处理结果如下图:As shown in Figure 6, it is a comparison chart of experimental test results of the present invention. This algorithm has done a large number of tests on the urban road network data of 650 square kilometers in Chengdu. The given experimental threshold is 0.1 meters. The processing results obtained are as follows:

图6(a)和图6(b)宽度较大的线代表处理前道路的拓扑弧段,宽度较小的线代表处理后道路的拓扑弧段;The lines with larger width in Fig. 6(a) and Fig. 6(b) represent the topological arc of the road before processing, and the lines with smaller width represent the topological arc of the road after processing;

图6(c)和图6(d)是道路网路口和路面的交界处,图6(c)是原数据,图6(d)是处理后的数据。Figure 6(c) and Figure 6(d) are the junction of the road network intersection and the road surface, Figure 6(c) is the original data, and Figure 6(d) is the processed data.

图6(e)和图6(f)是道路网路口,图6(e)是原数据的拓扑,图6(f)是处理后的删除冗余点后的拓扑。Figure 6(e) and Figure 6(f) are road network intersections, Figure 6(e) is the topology of the original data, and Figure 6(f) is the processed topology after deleting redundant points.

实验测试的环境为单台PC机,Windows版本为Windows XP,系统类型为32位操作系统,CPU为Intel Core2 Quad Q8400,主频为2.66GHz,内存(RAM)为3.25GB,硬盘总大小为60GB(固态),试验数据选取成都市中心城区1:500基础数据。下面以650平方公里道路网数据为例,在效率方面:拓扑构建,拓扑预处理和拓扑重构总用时179秒,其中拓扑预处理用时123秒;在准确性方面:识别的目标信息13049个,识别的准确率为100%,见表1。The experimental test environment is a single PC, the Windows version is Windows XP, the system type is 32-bit operating system, the CPU is Intel Core2 Quad Q8400, the main frequency is 2.66GHz, the memory (RAM) is 3.25GB, and the total size of the hard disk is 60GB (solid state), the test data selects the basic data of 1:500 in the downtown area of Chengdu. Taking the road network data of 650 square kilometers as an example, in terms of efficiency: the total time for topology construction, topology preprocessing and topology reconstruction is 179 seconds, of which the topology preprocessing takes 123 seconds; in terms of accuracy: 13049 target information are identified, The recognition accuracy rate is 100%, see Table 1.

表1道路面拓扑预处理结果比较表Table 1 Comparison table of road surface topology preprocessing results

实验结果表明,使用拓扑关系来表达和修复空间数据空间关系十分有效,基于识别和处理模型、运用拓扑关系来处理空间数据的方法,从准确率上,满足了制图自动综合对数据一致性的需求;从效率上,满足实际生产实践的要求。The experimental results show that it is very effective to use topological relations to express and repair the spatial relations of spatial data. Based on the identification and processing model, the method of using topological relations to process spatial data satisfies the data consistency requirements of automatic cartography synthesis in terms of accuracy. ; In terms of efficiency, it meets the requirements of actual production practice.

综上所述,本发明的优点是实现了对现有城市大比例尺地图数据库道路网实体空间关系存在缝隙和碎片的自动识别及修复,满足了顾及要素空间约束关系时的道路网自动制图综合需求。克服了现有的道路网综合方法多依据道路几何特征、语义特征、属性特征进行道路化简,对道路网拓扑特征,尤其错误目标尚没有形成全面、系统、一致的处理方式,进而影响道路网综合中对于路网结构的整体把握,使道路网综合方法不能高效的应用于多尺度的道路制图表达的缺陷。In summary, the present invention has the advantage of realizing the automatic identification and repair of gaps and fragments existing in the existing urban large-scale map database road network entity spatial relationship, and meeting the comprehensive needs of road network automatic mapping when taking into account the spatial constraint relationship of elements . It overcomes that the existing road network synthesis methods are mostly based on road geometric features, semantic features, and attribute features for road simplification, and there is no comprehensive, systematic, and consistent processing method for road network topology features, especially wrong targets, which will affect the road network. The overall grasp of the road network structure in the synthesis makes the road network synthesis method unable to efficiently apply to the defects of multi-scale road cartography.

以上所述仅为本发明的较佳实施例而已,并不用以限制本发明,凡在本发明的精神和原则之内,所作的任何修改、等同替换、改进等,均应包含在本发明的保护范围之内。The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of the present invention. within the scope of protection.

Claims (7)

1. a kind of recognition methodss of road network target information, it is characterised in that comprise the following steps:
A, the segmental arc of spatial data road network is set up into segmental arc R tree;
B, each the pre- intersecting paired segmental arc in the segmental arc tree for finding is set up into array;
C, for the array in arbitrary paired segmental arc and arbitrary paired segmental arc the first segmental arc on arbitrary composition node, hold Row following steps:
Whether the composition node in c1, the first segmental arc for judging in paired segmental arc is in the second segmental arc in the paired segmental arc;
C2, if it is not, then by each two in second segmental arc it is adjacent point composition line segment set up line segment R trees;
C3, search obtain the line segment in the range of the outside specified first threshold of the outsourcing frame of current composition node;
C4, using the composition node in first segmental arc as centre point, to specify Second Threshold to make buffering circle as radius;
C5, judge that the buffering circle searches for whether the line segment that obtains intersects with described;If so, then record intersecting line segment, centre point, First segmental arc and second segmental arc;
C6, intersected with this according to the centre point line segment two-end-point distance and specify the 3rd threshold value relation, recognize the center of circle The type of point.
2. method according to claim 1, it is characterised in that step c6 includes:
The distance of any in the two-end-point that the centre point intersects line segment with this is judged is less than the 3rd threshold value, and the two-end-point In the point be located in first segmental arc, and the match point after any point in the centre point and the two-end-point is fitted and the circle When the distance of the next point of the first segmental arc is less than three threshold values, then the centre point is deletion point to the heart o'clock.
3. method according to claim 1, it is characterised in that step c6 includes:
When judging that the centre point intersects the distance of the two-end-point of line segment with this and is both greater than three threshold values, then the centre point is to throw Shadow point.
4. method according to claim 1, it is characterised in that step c6 includes:
The distance of any in the two-end-point that the centre point intersects line segment with this is judged is less than the 3rd threshold value, and intersects with this When the distance of another point in the two-end-point of line segment is more than three threshold values;Or
When the distance that the centre point intersects the two-end-point of line segment with this is both less than three threshold values;
Then the centre point is match point.
5. a kind of processing method of the road network target information of the recognition methodss based on described in any one of claim 1-4, it is special Levy and be, comprise the following steps:
D, the deletion point searched in the segmental arc of spatial data road network;
E, by the deletion point deletion found in step A;
F, the subpoint and match point searched in the segmental arc of spatial data road network;
G, the subpoint for finding and match point are clustered;
H, the subpoint and match point after cluster is carried out into respectively projection process and process of fitting treatment.
6. method according to claim 5, it is characterised in that step G includes:
It is the center of circle by the arbitrary subpoint for finding or match point, to specify the 4th threshold value to draw buffering circle as radius;
By the subpoint in the 4th threshold range and match point cluster to designated treatment unit.
7. method according to claim 6, it is characterised in that step H includes:
H1, judge whether there is subpoint in each processing unit successively;
H2, when judge have subpoint when, whether the number for determining whether subpoint is 1;
H3, when the number for judging subpoint is for 1, the subpoint is carried out into projection process;
H4, when judging the number of subpoint not for 1, whether the line segment for determining whether to project segmental arc is same;
H5, when judge project segmental arc line segment for same when, each subpoint is done and obtain after process of fitting treatment match point, then general The match point to the projection segmental arc does projection process;
H6, when judge project segmental arc line segment not for same when, each subpoint is done and obtain after process of fitting treatment match point, then The match point is done into projection process to for finding apart from the nearest Projection Line Segment of the match point;
H7, when there is no subpoint during processing unit is judged in step H1, further determined whether match point;When judgement has plan During chalaza, process is fitted.
CN201610985705.2A 2016-11-09 2016-11-09 Recognition and processing method for road network target information Pending CN106599044A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201610985705.2A CN106599044A (en) 2016-11-09 2016-11-09 Recognition and processing method for road network target information

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201610985705.2A CN106599044A (en) 2016-11-09 2016-11-09 Recognition and processing method for road network target information

Publications (1)

Publication Number Publication Date
CN106599044A true CN106599044A (en) 2017-04-26

Family

ID=58589908

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201610985705.2A Pending CN106599044A (en) 2016-11-09 2016-11-09 Recognition and processing method for road network target information

Country Status (1)

Country Link
CN (1) CN106599044A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108629036A (en) * 2018-05-10 2018-10-09 中国人民解放军战略支援部队信息工程大学 A kind of road Generalization Method and device
CN109491984A (en) * 2018-10-09 2019-03-19 湖北省农村信用社联合社网络信息中心 Hash packet data library fragment poll method for sorting

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103150309A (en) * 2011-12-07 2013-06-12 清华大学 Method and system for searching POI (Point of Interest) points of awareness map in space direction
CN103390355A (en) * 2013-07-30 2013-11-13 中国民用航空总局第二研究所 Method for detecting taxiway conflict on basis of A-SMGCS (Advanced Surface Movement Guidance and Control System)

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103150309A (en) * 2011-12-07 2013-06-12 清华大学 Method and system for searching POI (Point of Interest) points of awareness map in space direction
CN103390355A (en) * 2013-07-30 2013-11-13 中国民用航空总局第二研究所 Method for detecting taxiway conflict on basis of A-SMGCS (Advanced Surface Movement Guidance and Control System)

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
吴伟: "面向地图综合的城市道路数据多特征自动提联及等级智能识别模型", 《中国优秀硕士学位论文全文数据库基础科学辑》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108629036A (en) * 2018-05-10 2018-10-09 中国人民解放军战略支援部队信息工程大学 A kind of road Generalization Method and device
CN109491984A (en) * 2018-10-09 2019-03-19 湖北省农村信用社联合社网络信息中心 Hash packet data library fragment poll method for sorting
CN109491984B (en) * 2018-10-09 2020-12-15 湖北省农村信用社联合社网络信息中心 Hash packet data base fragment polling sorting method

Similar Documents

Publication Publication Date Title
CN105701204B (en) The extracting method and display methods of electronic map interest point based on road network
CN107025685B (en) Airborne building roof point cloud modeling method under topology perception
CN110135351A (en) Boundary recognition method and equipment of built-up area based on urban building space data
CN113724279B (en) System, method, equipment and storage medium for automatically dividing traffic cells into road networks
CN107885790A (en) A kind of path space network multiple-factor automatic update method
CN108961403A (en) A method of major trunk roads are extracted with open source street map
CN101924647A (en) A Local Topology Reconstruction Method for Incremental Update of Navigation Road Network
KR101394976B1 (en) Generating methodology of multi-scale model for the attached cadastral map
Yang et al. A map‐algebra‐based method for automatic change detection and spatial data updating across multiple scales
CN109034234A (en) A kind of identification of adjacent area feature and automatic processing method
Nguyen et al. A multi-perspective approach to interpreting spatio-semantic changes of large 3D city models in CityGML using a graph database
CN103473420B (en) The automatic positioning method of statistical graph in a kind of statistical maps
CN107818338A (en) A kind of method and system of building group pattern-recognition towards Map Generalization
CN116258857A (en) Outdoor tree-oriented laser point cloud segmentation and extraction method
CN106599044A (en) Recognition and processing method for road network target information
Yang Identify building patterns
CN102254093B (en) Connected domain statistical correlation algorithm based on Thiessen polygon
CN107610229A (en) The three-dimensional building thing model self-repairing method corroded based on heuristic envelope
CN107993242A (en) Based on airborne LiDAR point cloud shortage of data zone boundary extracting method
Guo et al. Combined matching approach of road networks under different scales considering constraints of cartographic generalization
CN117609524A (en) Visual analysis method, device and equipment based on three-dimensional R-tree spatial index
Liu et al. Road density analysis based on skeleton partitioning for road generalization
CN116612209A (en) A Polygon Aggregation Method for Buildings
CN116071455A (en) A Road Network Stroke Generation Method Based on Same-Scale Similarity Relationship
CN115578245A (en) A Contour Extraction and Fusion Method of Contours of Residential Buildings

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication

Application publication date: 20170426

RJ01 Rejection of invention patent application after publication