WO2022061994A1 - 基于全局特征的数字地图线要素综合方法、装置和介质 - Google Patents

基于全局特征的数字地图线要素综合方法、装置和介质 Download PDF

Info

Publication number
WO2022061994A1
WO2022061994A1 PCT/CN2020/121520 CN2020121520W WO2022061994A1 WO 2022061994 A1 WO2022061994 A1 WO 2022061994A1 CN 2020121520 W CN2020121520 W CN 2020121520W WO 2022061994 A1 WO2022061994 A1 WO 2022061994A1
Authority
WO
WIPO (PCT)
Prior art keywords
line
digital map
map
index
elements
Prior art date
Application number
PCT/CN2020/121520
Other languages
English (en)
French (fr)
Inventor
汪愿愿
胡林舒
梁钢
陈振德
张丰
Original Assignee
浙江大学
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 浙江大学 filed Critical 浙江大学
Publication of WO2022061994A1 publication Critical patent/WO2022061994A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G06T17/05—Geographic models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume

Definitions

  • the invention relates to the field of cartography, in particular to a method for automatic synthesis of elements in the field of digital map cartography.
  • the main purpose of automatic synthesis is to remove as many redundant elements as possible on the basis of retaining the main information of the map.
  • the so-called global features of the data are the overall information of the map. Considering the global features of the data during automatic synthesis can effectively preserve the main information of the map.
  • the evaluation index should be related to the degree of retention of map information and the degree of deletion of redundant elements.
  • the automatic synthesis algorithm based on the global characteristics of the data is difficult to realize. A big reason is that the result of automatic synthesis is difficult to express quantitatively.
  • the present invention provides a method for automatic synthesis of digital map line elements based on the global characteristics of data, which can effectively solve the above-mentioned problems.
  • the present invention provides a method for synthesizing digital map line elements based on global features, which includes the following steps:
  • S2 Sort the line elements in the digital map according to the geometric feature importance index from low to high, and delete some line elements with the lowest geometric feature importance index from the digital map according to the set ratio;
  • the map similarity S is the similarity between the one-dimensional vector formed by all grid values of the simplified digital map and the one-dimensional vector formed by all grid values of the original digital map;
  • the element deletion rate Rp is the ratio of the number of line elements deleted in the simplified digital map relative to the original digital map to the total number of line elements in the original digital map;
  • the present invention can further provide one or more of the following preferred modes, and the technical features of each preferred mode can be combined correspondingly on the premise that there is no conflict with each other.
  • the geometric feature importance index IFI l of each line element l in the digital map is calculated as follows:
  • IFI l k 1 L l +k 2 M l +k 3 N l
  • L l is the length index of the line element l, and the calculation formula is as follows:
  • x is the pixel length of the line element l in the digital map
  • k 4 is the weight coefficient
  • d l is the Euclidean distance length of the line element l
  • p z is the resolution of the digital map
  • M l is the surrounding density index of the line element l, and the calculation formula is as follows:
  • M l a 1 tan -1 (b 1 ⁇ l +c 1 )+d 1
  • a 1 , b 1 , c 1 , and d 1 are weight coefficients, ⁇ l is the peripheral density of the line element l, and its calculation formula is:
  • P represents the number of other line elements located in the buffer of line element 1 and does not intersect with line element 1
  • n is the number of other line elements except line element 1 in the buffer of line element 1;
  • N l is the connectivity index of the line element l, and the calculation formula is as follows:
  • N l a 2 tan -1 (b 2 C l +c 2 )+d 2
  • a 2 , b 2 , c 2 , and d 2 are weight coefficients
  • C l is the connection degree of line element l
  • its calculation formula is:
  • I represents the number of other line features located in the buffer of line feature l and intersecting with line feature l.
  • the method for calculating the map similarity S is as follows:
  • S301 rasterize the deleted simplified digital map m and the original digital map o respectively, and convert the vector map into a raster map;
  • S302 Perform grid assignments on each grid map respectively, and the value of each grid is the number of line elements passing through the pixel in the grid map;
  • the calculation method of the element deletion rate Rp is as follows:
  • N r represents the number of line elements in the simplified digital map m that are truncated relative to the original digital map o
  • N o represents the total number of line elements in the original digital map o.
  • the specific steps of deleting some line elements with the lowest geometric feature importance index and not serving as connecting line elements from the digital map according to a set proportion are as follows:
  • S201 Construct two endpoint feature sets for each line feature in the digital map, record the line feature intersecting with the first endpoint of the line feature in the first set, and record the second endpoint with the line feature in the second set Line features whose endpoints intersect; construct a third set initialized to an empty set to record connecting line features;
  • S202 For the current line element with the lowest geometric feature importance index in the digital map, determine whether it satisfies the deletion condition, if so, delete the line element and execute S203, if not, keep the line element and regard it as a connection
  • the line element is recorded in the third set;
  • the deletion condition is that at least one of the first set and the second set corresponding to the line element is an empty set;
  • S205 Repeat S202-S204 continuously until the number of line elements deleted in this round reaches the number of line elements corresponding to the set ratio.
  • the set ratio is 0.5% to 2% of the total number of line elements in the original digital map.
  • the weight coefficients k 1 , k 2 , and k 3 are respectively 0.4, 0.3, and 0.3;
  • the weight coefficient k 4 takes the value of 0.2;
  • the weight coefficients a 1 , b 1 , c 1 , and d 1 are respectively 1/ ⁇ , -0.2, 6, and 0.55;
  • the weight coefficients a 2 , b 2 , c 2 , and d 2 are 1/ ⁇ , 8, -6.3, and 0.45, respectively.
  • the present invention provides a digital map line element synthesis device based on data global features, which includes a memory and a processor;
  • the memory for storing computer programs
  • the processor is configured to implement the global feature-based digital map line element synthesis method according to any one of claims 1 to 8 when executing the computer program.
  • the present invention provides a computer-readable storage medium, where a computer program is stored on the storage medium, and when the computer program is executed by a processor, the computer program according to any one of claims 1 to 8 is implemented.
  • the present invention has the following beneficial effects: 1.
  • the length index, the peripheral density index and the connectivity index are comprehensively considered from the three aspects of the geometric characteristics of the line elements, and the long length, the low peripheral density and the connection degree are retained. important line elements of high degree.
  • the map similarity is proposed to measure the similarity between the truncated map and the original map. 3.
  • the deletion method effectively solves the discontinuity problem of line elements after automatic synthesis, and based on the local update method on both sides of the line elements, it avoids the need to make a global judgment on the remaining elements after each deletion, and does not greatly reduce the deletion of line elements. effectiveness. 4. It is difficult to preset accurate map similarity for different scenarios. Therefore, the optimization idea of constraint equation is adopted, and the cartographic comprehensive effect index of the map is proposed to quantitatively evaluate the automatic comprehensive results of the map. When the comprehensive effect index reaches the best, the cartographic comprehensive Automatic stop, avoiding the preset operation of the final similarity of the map.
  • Fig. 1 is the flow chart of the digital map line element synthesis method based on global feature
  • Figure 2 is a graph of length index
  • Figure 3 is a graph of the surrounding density index
  • Figure 4 is a line element distribution diagram
  • Figure 5 is a graph of connectivity index
  • FIG. 6 is a schematic diagram of an actual iterative process of the method of the present invention.
  • Figure 7 shows the result of line element deletion under different degrees of similarity
  • Figure 8 is a comparison diagram of the automatic comprehensive effect of line elements
  • Figure 9 shows the comparative experimental results under different automatic synthesis algorithms.
  • a method for synthesizing line elements of a digital map based on global features includes the following steps:
  • S1 Calculate the geometric feature importance index IFI for each line element in the digital map.
  • the IFI is obtained by the weighted summation of the length index L, the surrounding density index M and the connectivity index N of the line element.
  • the geometric feature importance index of each line element l in the digital map is denoted as IFI l , and its calculation method is as follows:
  • IFI l k 1 L l +k 2 M l +k 3 N l
  • the weight of the length index should be the largest, and the peripheral density index and the connectivity index can be the same but smaller than the length index.
  • the values of the weight coefficients k 1 , k 2 , and k 3 are determined to be 0.4, 0.3, and 0.3, respectively, through optimization.
  • L l is the length index of the line element l.
  • the length index of the line element is the normalized value of the line length, but since the length of the line element to be deleted is mostly in the short and medium range, the present invention obtains the normalization of the line length by translating and scaling the inverse proportional function. function. This function can ensure that the line features have a larger rate of change when the line element is short, and the rate of change decreases as the line length increases. Meanwhile, in order to unify with other indices, the present invention uses the pixel length as the length unit of the line element.
  • the formula for calculating Ll is as follows:
  • x is the pixel length of the line element l in the digital map
  • k4 is the weight coefficient
  • dl is the Euclidean distance length of the line element l
  • pz is the resolution of the digital map.
  • the parameter value of k 4 needs to be set based on experience and can be used to control the shape of the function. In this embodiment, k 4 is set to be 0.2, and the image of the L l function is shown in FIG. 2 . .
  • M1 is the surrounding density index of the line element 1 , and can be calculated from the surrounding density ⁇ 1 of the line element 1 .
  • the calculation method of ⁇ l is: first set the buffer of line element l, then traverse other elements, obtain the total amount of elements in the buffer, and finally divide by the length of the line element.
  • the connectivity index when calculating the surrounding density of line features, those features that intersect with the original feature will be excluded. Its calculation formula is:
  • P represents the number of other line elements located in the buffer of line element 1 and does not intersect with line element 1
  • n is the number of other line elements except line element 1 in the buffer of line element 1.
  • the peripheral density ⁇ l also needs to be normalized, and at the same time reflects the variation in different density intervals, and the normalized result is called the peripheral density index M l .
  • the line length attribute is the most essential feature of line features, and its calculation does not involve other features.
  • the surrounding density attributes of adjacent elements will affect each other, resulting in a regional and uniform variation in the distribution of surrounding density values. Therefore, the perimeter density normalization function for line features differs from the length normalization function.
  • Two thresholds ⁇ 1 and ⁇ 2 are set here, where ⁇ 1 is smaller than ⁇ 2 .
  • ⁇ 1 is smaller than ⁇ 2 .
  • the surrounding density index is close to 1 and the change trend is relatively gentle.
  • the surrounding density index is greater than ⁇ 2 , it can be considered that the elements in this range are difficult to distinguish, the surrounding density index is close to 0 and the change trend is relatively gentle.
  • the surrounding density index of the elements in this range can be set to have a close proportional relationship with the surrounding density.
  • the present invention uses the arc tangent function to normalize the surrounding density ⁇ l to describe the above three situations, and the calculation formula of M l is as follows:
  • M l a 1 tan -1 (b 1 ⁇ l +c 1 )+d 1
  • a 1 , b 1 , c 1 , and d 1 are all weight coefficients, which are used to adjust the approximate positions of ⁇ 1 and ⁇ 2 and need to be set based on experience.
  • a 1 , b 1 , c 1 , and d 1 are set to 1/ ⁇ , -0.2, 6, and 0.55, respectively.
  • ⁇ 1 is approximately at 20, and ⁇ 2 is at 40.
  • the normalization function M 1 The image is shown in Figure 3.
  • N l is the connection degree index of the line element l, and the connection degree describes the contribution degree of the line element to the surrounding elements.
  • the lengths of the three line elements are the same, the line element 2 has a smaller peripheral density than the line element 1, and the line element 3 has a greater degree of connection than the line element 2. Therefore, in terms of importance, the line Feature 3 > Line Feature 2 > Line Feature 1.
  • the calculation method of the degree of connectivity is similar to that of the surrounding density, and it is only necessary to convert the non-connected features into connected features in the process of obtaining the surrounding features.
  • the connection degree C l of line element l is calculated as:
  • I represents the number of other line features located in the buffer of line feature l and intersecting with line feature l.
  • the degree of connectivity can also be normalized by the arc tangent function.
  • the calculation formula of N l is as follows:
  • N l a 2 tan -1 (b 2 C l +c 2 )+d 2
  • a 2 , b 2 , c 2 , and d 2 are weight coefficients.
  • the weighting coefficients a 2 , b 2 , c 2 , and d 2 are 1/ ⁇ , 8, -6.3, and 0.45, respectively.
  • ⁇ 1 is approximately at 0.6
  • ⁇ 2 is at 1.1.
  • the normalization function The Nl image is shown in Figure 5.
  • S2 Sort the line elements in the digital map according to the geometric feature importance index from low to high, and delete some line elements with the lowest geometric feature importance index from the digital map according to the set ratio.
  • the set ratio w% is the ratio of the number of line elements deleted in each round to the total number of line elements C t in the original digital map. The specific value can be adjusted according to the actual situation. Generally, w% is set at 0.5% to 2%. After the line elements are sorted, the line elements with the lowest geometric feature importance index are ranked first, so each iteration needs to delete the top w% ⁇ C t line elements.
  • the "set ratio" is equivalent to the step size of each round in the subsequent iteration process.
  • the "set ratio" is 1% of the total number of line elements in the original digital map, and this value can balance the mapping time and the final mapping effect.
  • Each iteration of the map's synthesis process needs to remove 1% of the total number of line features in the original digital map.
  • S3 After executing S2, calculate the map similarity S and the element deletion rate Rp between the simplified digital map (denoted as m) and the original digital map (denoted as o) deleted by S2.
  • map similarity S is the similarity between the one-dimensional vector formed by all the grid values of the simplified digital map and the one-dimensional vector formed by all the grid values of the original digital map.
  • the calculation method of the map similarity S is as follows:
  • S301 Perform gridization on the truncated simplified digital map m and the original digital map o respectively, and convert the vector map into a grid map.
  • the grid values in the grid map can be initialized in advance.
  • the element deletion rate Rp in the present invention is the ratio of the number of line elements deleted in the simplified digital map relative to the original digital map to the total number of line elements in the original digital map. As the iterative deletion process proceeds, the Rp value will continue to increase.
  • the calculation method of the element deletion rate Rp is as follows:
  • N r represents the number of line elements in the simplified digital map m that are truncated relative to the original digital map o
  • N o represents the total number of line elements in the original digital map o.
  • S4 Calculate the comprehensive cartographic effect index of the simplified digital map after S2 deletion based on the map similarity S and the element deletion rate Rp obtained in S3.
  • the map similarity S is used to express the degree of retention of map information
  • the additionally defined element deletion rate Rp is used to reflect the number of redundant elements deleted, and the two constitute the comprehensive cartographic effect index.
  • the factor deletion rate Rp is used as an independent variable.
  • a complete automatic synthesis process should be: at the beginning, the element deletion rate is low and the map similarity is high. The effect index benefits from high pruning rates.
  • the similarity of the map is reduced.
  • the weight of the similarity of the map should be increased to avoid the similarity of the map being too low. Therefore, the formula for calculating the comprehensive cartographic effect index finally set in the present invention is as follows:
  • the constants a and b are used to modify the initial weight value to control the importance of S and Rp.
  • Rp ranges from 0 to 1, and the quadratic weight value can make the weight change process smooth.
  • the above-mentioned map synthesis process of S1 to S5 is the process of obtaining the maximum value of F based on the calculation formula of the comprehensive cartographic effect index F, where Rp is an independent variable and F is a dependent variable. Since the map similarity S will also change with the change of Rp, it is difficult to directly obtain the maximum value MaxF of the comprehensive effect index. Therefore, the iterative method is adopted for estimation.
  • the actual iterative process can be as shown in Figure 6. Cyclic process.
  • the minimum similarity threshold is predetermined, which is the termination judgment condition of element deletion, and its value will affect the final result.
  • Figure 7 shows the result of the iterative deletion of elements in a map in a case. With the continuous deletion of line elements, the map similarity is reduced from 1.0 to 0.8. It can be found from the figure that when the similarity is 0.9, most of the line elements with dense distribution are deleted, which basically guarantees the important line elements of the map. However, when the similarity is 0.8, while the minor line elements are deleted, more important line elements are also deleted, and the whole result looks very broken. Therefore, it is very important to select the appropriate map similarity. Different maps need to determine the best minimum similarity threshold value according to their own characteristics.
  • S201 Construct two endpoint feature sets for each line feature in the digital map, record the line feature intersecting with the first endpoint of the line feature in the first set, and record the second endpoint with the line feature in the second set Line features whose endpoints meet.
  • a third set initialized to the empty set is constructed to record the connecting line features. For storage convenience, only the unique IDs of the corresponding line features can be stored in the three sets.
  • S202 For the current line element with the lowest geometric feature importance index in the digital map, determine whether it satisfies the deletion condition, if so, delete the line element and execute S203, if not, keep the line element and regard it as a connection
  • the line element is recorded in the third set;
  • the deletion condition is that at least one of the first set and the second set corresponding to the line element is an empty set;
  • S205 Repeat S202-S204 continuously until the number of line elements deleted in this round reaches the number of line elements corresponding to the aforementioned set ratio. For example, if 1%C t line elements need to be deleted in this iteration, the cycle of S202 to S204 needs to be repeated continuously until 1% C t line elements are deleted, and then the map similarity S and element deletion in step S3 can be performed. Decrease rate Rp calculation.
  • the deletion method effectively solves the discontinuity problem of line elements after automatic synthesis, and based on the local update method on both sides of the line elements, it avoids the need to make a global judgment on the remaining elements after each deletion, and does not greatly reduce the deletion of line elements. effectiveness.
  • steps S1 to S5 are the same as the aforementioned steps in the specific implementation manner, ie, steps S1 to S5, and step S2 is specifically implemented by using S201 to S205, which will not be repeated here. Part of the implementation process and implementation results are shown below:
  • the line element data selects road data within the scope of OSM China
  • the coordinate system is EPSG: 4326, and contains a total of 3,284,928 elements.
  • vector tiles with lower levels, more elements, and more important areas are selected for line elements.
  • the experiment is carried out by using the automatic synthesis method of map line elements proposed by the method of the present invention, and 7/213/85 tiles are selected as the experimental area. This area is the northern part of Zhejiang.
  • the traffic system is developed but the traffic density distribution is uneven, and the center of the tiles is the most dense. , as shown in Figure 8(a).
  • the automatic synthesis of the present invention is performed on the vector tile, and the same vector tile is randomly deleted with the same element deletion rate in order to form a comparison.
  • the results are shown in Figure 8(b) and Figure 8(c) respectively. Show. It can be found from FIG.
  • the result of the method for synthesizing digital map line elements based on the global feature of the present invention has a high similarity with the original data, basically retains the main information of the original map, and automatically reduces the elements by 56%. quantity.
  • the data using the random synthesis method is quite different from the original data, the similarity of the map is low, and the comprehensive results show the characteristics of fragmentation. Therefore, the automatic deletion of line elements from small to large according to the IFI of the present invention can better preserve the original information of the map.
  • the results of automatic synthesis are partially enlarged, and it can be found that most of the deleted elements belong to fine and dense roads, and most of these roads belong to the street level in the real world.
  • the method of the present invention is compared with the commonly used comprehensive algorithm for directly pruning according to the length threshold in vector tiles (this algorithm is adopted by Geoserver), and the data is selected as 6/106/42 tiles.
  • the method of the present invention is more excellent in element performance than the comprehensive method of element deletion based on thresholds.
  • a digital map line element synthesis device based on data global features can also be provided, which includes a memory and a processor;
  • the memory for storing computer programs
  • the processor when executing the computer program, implements the aforementioned method for synthesizing line elements of a digital map based on global features.
  • a computer-readable storage medium can also be provided, and a computer program is stored on the storage medium, and when the computer program is executed by a processor, the aforementioned digital map line elements based on global features are implemented. Comprehensive approach.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Databases & Information Systems (AREA)
  • Geometry (AREA)
  • Software Systems (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Remote Sensing (AREA)
  • Computer Graphics (AREA)
  • Image Analysis (AREA)

Abstract

一种基于全局特征的数字地图线要素综合方法、装置和介质。所述方法包括：首先计算线要素的几何特征重要度指数，评价线要素在地图表达时的重要程度，并按照该指数从低到高进行要素删减；然后，计算地图相似度，量化每次要素删减操作对地图信息留存的改变量；再后，计算基于要素删减率和地图相似度的制图综合效果指数，并迭代进行自动综合过程；最终达到制图综合效果指数最大值时，自行结束自动综合过程。所述方法在数字地图制图综合领域具有重要的实际应用价值。

Description

基于全局特征的数字地图线要素综合方法、装置和介质 技术领域
本发明涉及地图制图领域，尤其涉及一种数字地图制图领域中要素自动综合方法。
背景技术
自动综合的主要目的是在保留地图主要信息的基础上，尽可能多地删除冗余要素。所谓数据的全局特征即地图的整体信息，自动综合时考虑数据的全局特征可以有效保留地图主要信息。为了实现基于数据全局特征的自动化操作，需要建立基于地图整体信息的自动综合结果量化指标。从自动综合的目的出发，该评价指标应当与地图信息的保留程度和冗余要素的删减程度有关。基于数据全局特征的自动综合算法较难实现，一个很大的原因就是自动综合的结果难以量化表达。
因此如何基于数据全局特征实现地图中要素的自动化删减，是数字地图综合过程中的主要技术难点。
发明内容
为了克服上述现有技术的不足，本发明提供一种基于数据全局特征的数字地图线要素自动综合的方法，可有效解决上述问题。
本发明具体采用的技术方案如下：
第一方面，本发明提供了一种基于全局特征的数字地图线要素综合方法，其包括以下步骤：
S1：对数字地图中的每个线要素计算其几何特征重要度指数，几何特征重要度指数由该线要素的长度指数、周边密度指数和连接度指数加权求和得到；
S2：按照几何特征重要度指数从低到高对数字地图中的线要素进行排序，并从数字地图中按照设定比例删减几何特征重要度指数最低的部分线要素；
S3：计算S2删减后的简化数字地图和原始数字地图之间的地图相似度S以及要素删减率Rp；
所述地图相似度S为简化数字地图的所有栅格值所构成的一维向量与原始 数字地图的所有栅格值所构成的一维向量之间的相似度；
所述要素删减率Rp为简化数字地图中相对于原始数字地图被删减的线要素数量占原始数字地图中线要素总数量的比例；
S4：基于S3中得到的地图相似度S和要素删减率Rp计算S2删减后的简化数字地图的制图综合效果指数F＝(a×Rp 2+b)S+(1-a×Rp 2-b)Rp，其中a和b分别为常数系数且a>0；
S5：不断迭代步骤S2～S4，直到所述地图相似度S低于最低相似度阈值时停止迭代，并以迭代过程中取得的最大制图综合效果指数对应的简化数字地图作为最终输出结果，完成数字地图线要素的综合。
基于上述第一方面的技术方案，本发明还可以进一步提供以下一种或多种优选方式，且各个优选方式的技术特征在没有相互冲突的前提下，均可进行相应组合。
作为优选，数字地图中的每个线要素l的几何特征重要度指数IFI l计算方法如下：
IFI l＝k 1L l+k 2M l+k 3N l
式中：k 1、k 2、k 3均为权重系数，且k 1+k 2+k 3＝1；
L l为线要素l的长度指数，计算公式如下：
Figure PCTCN2020121520-appb-000001
x＝d l/p z
式中：x为线要素l在数字地图中的像元长度，k 4为权重系数，d l为线要素l的欧几里得距离长度，p z为数字地图的分辨率大小；
M l为线要素l的周边密度指数，计算公式如下：
M l＝a 1tan -1(b 1ρ l+c 1)+d 1
式中：a 1、b 1、c 1、d 1均为权重系数，ρ l为线要素l的周边密度，其计算公式为：
式中，P代表位于线要素l的缓冲区中且与线要素l不相交的其他线要素数量，n为线要素l的缓冲区中除线要素l之外的其他线要素的数量；
N l为线要素l的连接度指数，计算公式如下：
N l＝a 2tan -1(b 2C l+c 2)+d 2
式中：a 2、b 2、c 2、d 2均为权重系数，C l为线要素l的连接度，其计算公式为：
C l＝I/d l
式中：I代表位于线要素l的缓冲区中且与线要素l相交的其他线要素数量。
作为优选，所述的地图相似度S的计算方法如下：
S301：对删减后的简化数字地图m与原始数字地图o分别进行栅格化，将矢量地图转换为栅格地图；
S302：分别对每张栅格地图进行栅格赋值，每个栅格的值为所在栅格地图中经过该像元的线元素数量；
S303：分别将两张栅格地图的栅格矩阵转换为一维向量，然后计算两个一维向量之间的余弦相似度，作为简化数字地图m与原始数字地图o之间的地图相似度S。
作为优选，所述的要素删减率Rp计算方法如下：
Rp＝N r/N o
式中：N r代表简化数字地图m中相对于原始数字地图o被删减的线要素数量，N o代表原始数字地图o中线要素总数量。
作为优选，所述的S2中，从数字地图中按照设定比例删减几何特征重要度指数最低且不作为连接线要素的部分线要素的具体步骤顺次如下：
S201：针对数字地图中的每个线要素构建两个端点要素集合，以第一集合记录与该线要素的第一个端点相交的线要素，以第二集合记录与该线要素的第二个端点相交的线要素；构建初始化为空集的第三集合，用于记录连接线要素；
S202：针对数字地图中当前的几何特征重要度指数最低的线要素，判断其是否满足删除条件，若满足则删除该线要素并执行S203，若不满足则保留该线要素并将其视为连接线要素记入第三集合中；所述删除条件为线要素对应的第一集合和第二集合中至少有一个是空集；
S203：每删除一个线要素，则更新与该线要素任一端点相交的所有线要素的两个端点要素集合，然后执行S204；
S204：检查第三要素集合中是否存在重新满足所述删除条件的线要素，若存在则删除该线要素并重新执行S203，若不存在则不进行要素删除；
S205：不断重复S202～S204，直至本轮删除的线要素数量达到所述设定比例对应的线要素数量。
作为优选，所述常数系数a＝1，常数系数b＝0。
作为优选，所述设定比例为原始数字地图中线要素总数量的0.5％～2％。
作为优选，所述几何特征重要度指数IFI l计算过程中，权重系数k 1、k 2、k 3取值分别为0.4、0.3、0.3；
所述长度指数计算过程中，权重系数k 4取值为0.2；
所述周边密度指数计算过程中，权重系数a 1、b 1、c 1、d 1分别为1/π、-0.2、6、0.55；
所述连接度指数计算过程中，权重系数a 2、b 2、c 2、d 2分别为1/π、8、-6.3、0.45。
第二方面，本发明提供了一种基于数据全局特征的数字地图线要素综合装置，其包括存储器和处理器；
所述存储器，用于存储计算机程序；
所述处理器，用于当执行所述计算机程序时，实现如权利要求1～8任一项所述的基于全局特征的数字地图线要素综合方法。
第三方面，本发明提供了一种计算机可读存储介质，所述存储介质上存储有计算机程序，当所述计算机程序被处理器执行时，实现如权利要求1～8任一项所述的基于全局特征的数字地图线要素综合方法。
相比于传统的要素自动综合方法，本发明具有如下有益效果：1、从线要素几何特征的三方面综合考虑长度指数、周边密度指数和连接度指数，保留了长度长、周边密度小，连接度高的重要线要素。2、借鉴余弦相似度，提出地图相似度用于衡量删减后的地图与原始地图的相似程度。3、为避免出现线长度过短被误判删除的情况，提出控制线要素删除顺序进行解决。该删除方法有效解决了自动综合后线要素的不连续问题，并且基于线要素两侧的局部更新方法，避免了每次删除后需要对其余要素进行的全局判断，没有大幅度降低线要素的删除效率。4、针对不同场景预设准确的地图相似度较为困难，因此采用约束方程的优化思想，提出地图的制图综合效果指数对地图自动综合结果进行量化评价，当综合效果指数达到最佳时，制图综合自动停止，避免了对地图最终相似度的预设操作。
附图说明
图1是基于全局特征的数字地图线要素综合方法的流程图；
图2为长度指数图；
图3为周边密度指数图；
图4为线要素分布图；
图5为连接度指数图；
图6为本发明方法的一种实际迭代过程示意图；
图7为不同相似度下的线要素删减结果；
图8为线要素自动综合效果对比图；
图9为不同自动综合算法下的对比实验结果。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行详细说明。为了使公众对本发明有更好的了解，在下文对本发明的细节描述中，详尽描述了一些特定的细节部分。对本领域技术人员来说没有这些细节部分的描述也可以完全理解本发明。
如图1所示，在本发明的一个较佳实施例中，提供了一种基于全局特征的数字地图线要素综合方法，其包括以下步骤：
S1：对数字地图中的每个线要素计算其几何特征重要度指数IFI，IFI由该线要素的长度指数L、周边密度指数M和连接度指数N加权求和得到。
在本实施例中，将数字地图中的每个线要素l的几何特征重要度指数记为IFI l，其计算方法如下：
IFI l＝k 1L l+k 2M l+k 3N l
式中：k 1、k 2、k 3均为权重系数，且k 1+k 2+k 3＝1。一般长度指数的权重应当最大，周边密度指数和连接度指数可以相同但小于长度指数。本实施例中，通过优化确定权重系数k 1、k 2、k 3取值分别为0.4、0.3、0.3。
上式中，L l为线要素l的长度指数。线要素的长度指数是线长度的归一化值，但由于需要被删减的线要素的长度大多位于中短范围内，因此本发明通过对反比例函数进行平移、缩放得到线长度的归一化函数。该函数能保证在线要素较短时具有较大的变化率，并且该变化率随着线长的增加不断降低。同时，为了和其他指数的统一化，本发明使用像元长度作为线要素的长度单位。L l计算公式如下：
Figure PCTCN2020121520-appb-000002
x＝d l/p z
式中：x为线要素l在数字地图中的像元长度，k 4为权重系数，d l为线要素l的欧几里得距离长度，p z为数字地图的分辨率大小。k 4的参数值需基于经验设置，可用于控制函数形状。本实施例中设置k 4为0.2，其L l函数图像如图2所示。。
另外，M l为线要素l的周边密度指数，可由线要素l的周边密度ρ l计算得到。ρ l的计算方法是：先设定线要素l的缓冲区，然后遍历其他要素，获取位于缓冲区内的要素总量，最后除以线要素的长度。同时，为与连接度指数计算不冲突，计算线要素的周边密度时，将排除那些与原要素相交的要素。其计算公式为：
ρ l＝P/d l
式中，P代表位于线要素l的缓冲区中且与线要素l不相交的其他线要素数量，n为线要素l的缓冲区中除线要素l之外的其他线要素的数量。
和线长度一样，周边密度ρ l也需要进行归一化操作，同时体现出不同密度区间内的变化差异，归一化结果称为周边密度指数M l。线长度属性属于线要素最本质的特征，其计算不涉及到其他要素。而相邻要素的周边密度属性会相互影响，致使周边密度值分布呈现出区域性与均匀变化特性。因此，线要素的周边密度归一化函数与长度归一化函数有所不同。
这里设置两个阈值λ 1和λ 2，其中λ 1小于λ 2。当周边密度小于λ 1时，可以认为该范围内要素较为显著，周边密度指数接近于1并且变化趋势较为平缓。当周边密度大于λ 2时，可以认为该范围内的要素难以被分辨，周边密度指数接近于0并且变化趋势较为平缓。当周边密度处于λ 1和λ 2时，可以设定该范围内要素的周边密度指数同周边密度拥有接近于正比的关系。本发明使用反正切函数对周边密度ρ l进行归一化操作，以描述上述三种情况，M l计算公式如下：
M l＝a 1tan -1(b 1ρ l+c 1)+d 1
式中：a 1、b 1、c 1、d 1均为权重系数，用于调整λ 1和λ 2的大致位置，需要基于经验进行设置。本实施例中，a 1、b 1、c 1、d 1分别设置为1/π、-0.2、6、0.55，此时λ 1大致位于20，λ 2位于40，其归一化函数M l图像如图3所示。
另外，N l为线要素l的连接度指数，连接度描述了线要素对周边要素的贡献程度，连接度越大，该要素越重要。如图4所示，3个线要素长度一致，线要素2相比线要素1具有更小的周边密度，线要素3相比线要素2具有更大的连接度，所以在重要程度上，线要素3>线要素2>线要素1。连接度的计算方式和周边密度较为相似，仅需要在求取周边要素过程中将非连接的要素转变为连接的要素。线要素l的连接度C l计算公式为：
C l＝I/d l
式中：I代表位于线要素l的缓冲区中且与线要素l相交的其他线要素数量。
连接度也可以采用反正切函数进行归一化，N l计算公式如下：
N l＝a 2tan -1(b 2C l+c 2)+d 2
式中：a 2、b 2、c 2、d 2均为权重系数。本实施例中，权重系数a 2、b 2、c 2、d 2分别为1/π、8、-6.3、0.45，，此时λ 1大致位于0.6，λ 2位于1.1，其归一化函数N l图像如图5所示。
S2：按照几何特征重要度指数从低到高对数字地图中的线要素进行排序，并从数字地图中按照设定比例删减几何特征重要度指数最低的部分线要素。
需要注意的是，在本步骤删减几何特征重要度指数最低的部分线要素时，是以一定的比例进行删除的。该设定比例w％即每一轮删除的线要素数量占原始数字地图中线要素总量C t的比例，具体取值可根据实际情况进行调整，一般设定w％为0.5％～2％。当线要素排序完毕后，排在最前面的为几何特征重要度指数最低的线要素，因此每一轮迭代需删除排在最前面的w％×C t个线要素。该“设定比例”相当于后续迭代过程中的每一轮的步长，每一轮的删除要素比例越小，后续迭代过程中最终得到的最大制图综合效果指数越接近真实值，最终制图效果越好，但整个方法所需的制图时间也越长。本实施例中，经过大量的试验，确定“设定比例”为原始数字地图中线要素总数量的1％，该取值可以平衡制图时间与最终制图效果。地图的综合过程中的每一轮迭代，都需要删除原始数字地图中线要素总数量的1％。
S3：当执行完S2后，计算S2删减后的简化数字地图(记为m)和原始数字地图(记为o)之间的地图相似度S以及要素删减率Rp。
其中，地图相似度S为简化数字地图的所有栅格值所构成的一维向量与原始 数字地图的所有栅格值所构成的一维向量之间的相似度。
本实施例中，地图相似度S的计算方法如下：
S301：对删减后的简化数字地图m与原始数字地图o分别进行栅格化，将矢量地图转换为栅格地图，栅格地图中的栅格值可以预先初始化。
S302：然后分别对每张栅格地图进行栅格赋值，每个栅格的值为所在栅格地图中经过该像元的线元素数量。在实际赋值过程中，首先对可在将矢量地图栅格化后，以屏幕像元大小作为栅格像元大小，每当一条线要素经过一个像元时，该像元计数值加1，遍历所有线要素后，即可完成栅格赋值。
S303：分别将简化数字地图m与原始数字地图o的栅格矩阵分别转换为一维向量，然后计算两个一维向量之间的余弦相似度，作为简化数字地图m与原始数字地图o之间的地图相似度S。需注意，简化数字地图m与原始数字地图o的栅格矩阵转换为一维向量的过程中，需要按照相同的转换方法进行，本实施例中按照从左到右、从上到下的光栅扫描顺序进行转换。
另外，本发明中的要素删减率Rp为简化数字地图中相对于原始数字地图被删减的线要素数量占原始数字地图中线要素总数量的比例，随着迭代删减过程的进行，Rp值会不断增大。
本实施例中，要素删减率Rp计算方法如下：
Rp＝N r/N o
式中：N r代表简化数字地图m中相对于原始数字地图o被删减的线要素数量，N o代表原始数字地图o中线要素总数量。
S4：基于S3中得到的地图相似度S和要素删减率Rp计算S2删减后的简化数字地图的制图综合效果指数。
在本发明中，地图相似度S用于表达地图信息的保留程度，而额外定义的要素删减率Rp则用来体现冗余要素的删除数量，两者构成制图综合效果指数。随着要素的删减，地图相似度不断减小，要素删减率不断增加，当二者达到某一平衡时，制图综合效果指数将达到最佳值。这里把要素删减率Rp作为自变量，一个完整的自动综合过程应当是：一开始，要素删减率较低，地图相似度较高，此时应当赋予要素删减率高权重，使制图综合效果指数受益于高删减率。删减一定要素数量后，地图相似度降低，此时应当提高地图相似度的权重，避免地图相似度过低。因此，本发明中最终设定的制图综合效果指数计算公式如下：
F＝(a×Rp 2+b)S+(1-a×Rp 2-b)Rp
其中a和b分别为常数系数且a>0。
在该公式中，F、Rp、S均是随着迭代次数变化而变化的值，因此对于第i轮迭代而言，制图综合效果指数F(i)可以表示为下式：
F(i)＝(a×Rp(i) 2+b)S(i)+(1-a×Rp(i) 2-b)Rp(i)
其中i为当前迭代的轮数，常数a和b用于修改初始权重值，以控制S与Rp的重要性，本实施例最终确定常数系数a＝1，常数系数b＝0。Rp的范围为0到1，二次化的权重值可以使权重变化过程变得平滑。
S5：不断迭代步骤S2～S4，直到所述地图相似度S低于最低相似度阈值时停止迭代，并以迭代过程中取得的最大制图综合效果指数对应的简化数字地图作为最终输出结果，完成数字地图线要素的综合。
在实际实现的过程中，上述S1～S5的地图综合过程即基于制图综合效果指数F的计算公式求取F的最大值的过程，其中Rp为自变量，F为因变量。由于随着Rp的变化，地图相似度S也会发生变化，所以较难直接获取到综合效果指数的最大值MaxF，因此采取迭代法进行估计，其实际的迭代过程可采用如图6所示的循环流程。
另外，本发明中，最低相似度阈值预先确定，为要素删减的终止判断条件，其取值会影响最终的结果。如图7所示为一个案例中的地图在进行要素迭代删减过程中的结果，随着线要素的不断删减，地图相似度从1.0降低到了0.8。从图中可以发现当相似度为0.9时，被删除的大多是分布密集的线要素，基本保证了地图的重要线要素。但是当相似度为0.8时，在删除次要线要素的同时也删除了较多的重要线要素，整个结果看起来十分地破碎。所以选定合适的地图相似度显得十分重要，不同的地图需要根据其自身特点确定最佳的最低相似度阈值的取值。
需要注意的是，从图7的结果中还可以看出，当相似度为0.9时，图中圆圈标识处出现了连接线的缺失。这些线都是由于过短被误判而删除，但是从地图的制图效果角度上考虑应当保留，因此在本发明中可以在迭代步骤S2的过程中通过控制线要素删除的顺序来解决这个问题。该解决方法的主要思路为：建立一个集合用于插入未满足删除条件的线要素(可能的连接线)，每当原始数据中有线要素删除时，对该集合进行更新，确认是否有新的满足条件的线要素可以删除，该集合起到了保留连接线的作用。下面描述该解决方法在本实施例中的一种具体 实现方式：
在每一轮迭代所述S2过程中，从数字地图中按照设定比例删减几何特征重要度指数最低部分线要素时，需保留两端连接有其他线要素的连接线要素，具体步骤顺次如下：
S201：针对数字地图中的每个线要素构建两个端点要素集合，以第一集合记录与该线要素的第一个端点相交的线要素，以第二集合记录与该线要素的第二个端点相交的线要素。同时构建初始化为空集的第三集合，用于记录连接线要素。为了存储方便，三个集合中可仅存储相应线要素的唯一ID。
S202：针对数字地图中当前的几何特征重要度指数最低的线要素，判断其是否满足删除条件，若满足则删除该线要素并执行S203，若不满足则保留该线要素并将其视为连接线要素记入第三集合中；所述删除条件为线要素对应的第一集合和第二集合中至少有一个是空集；
S203：每删除一个线要素，则更新与该线要素任一端点相交的所有线要素的两个端点要素集合，然后执行S204；
S204：检查第三要素集合中是否存在重新满足所述删除条件的线要素，若存在则删除该线要素并重新执行S203，若不存在则不进行要素删除；
S205：不断重复S202～S204，直至本轮删除的线要素数量达到前述设定比例对应的线要素数量。例如，本轮迭代需要删除1％C t个线要素，那么需要不断重复S202～S204的循环，直至删除完1％C t个线要素后，即可进行S3步骤的地图相似度S以及要素删减率Rp计算。
该删除方法有效解决了自动综合后线要素的不连续问题，并且基于线要素两侧的局部更新方法，避免了每次删除后需要对其余要素进行的全局判断，没有大幅度降低线要素的删除效率。
为了进一步说明本发明的技术效果，下面基于一个具体案例来展示上述基于全局特征的数字地图线要素综合方法在具体地图上的制图效果。
实施例
本实施例步骤与具体实施方式前述步骤相同，即步骤S1～S5，且S2步骤具体采用S201～S205实现，在此不再进行赘述。下面就部分实施过程和实施结果进行展示：
本实施例线要素数据选择OSM中国范围内的道路数据，坐标系为 EPSG:4326，共包含3284928个要素，选取其中层级较低、要素数量较多且区域范围较为重要的矢量瓦片进行线要素自动综合实验。本实施例主要包含两个实验：
(1)本发明方法的有效性验证。
利用本发明方法提出的地图线要素自动综合方法进行实验，选用7/213/85瓦片作为实验区域，该区域为浙江北部，交通体系发达但是交通密度分布不均匀并以瓦片中心位置最为密集，如图8(a)所示。对该矢量瓦片进行本发明的自动综合，同时为了形成对比以相同的要素删减率对同样的矢量瓦片进行随机删除要素，其结果分别如图8(b)和图8(c)所示。从图8中可以发现采取本发明的基于全局特征的数字地图线要素综合方法的结果与原始数据具有较高的相似度，基本保留了原始地图的主要信息，同时自动综合减少了56％的要素数量。而采取随机综合方法的数据与原始数据存在较大的差异，地图相似度较低，综合结果表现出碎片化特征。所以，按照本发明的IFI从小到大对线要素进行自动化删除能够较好地保留地图原始信息。另外对自动综合后的结果进行局部放大，从中可以发现，大部分被删除要素都属于细而密的道路，这些道路在真实世界中大多属于街道级别。
(2)本发明方法与其他算法的对比。
将本发明方法与矢量瓦片中常用的按长度阈值直接进行删减的综合算法(该算法被Geoserver所采用)进行比较，数据选择6/106/42瓦片。
结果如图9所示，其中(a)为原始瓦片数据，(b)为基于本发明的自动综合算法下的要素结果，(c)为采用1像素作为长度阈值进行删减的要素结果。其中，(b)综合结果无论是数据分布密集或是零散的区域都与原始瓦片数据具有较高的相似度。而对于(c)来说，在圆圈内中的要素密集区域，表现的结果不够理想，整体结果相比原始数据集呈现出破碎的现象。这是因为该算法仅仅从要素自身特征出发进行要素删减，而未考虑全局特征。大量的连接要素由于未达到1像元长度而被删除，这些连接要素的缺失导致了综合结果与原始数据集产生了较大的差异，且这种差异随着要素密集程度的增加与缩放层级的减小将更为明显。
因此在要素层级较低或要素规模较大时，选择本发明方法相比基于阈值进行要素删减的综合方法在要素表现上更加优秀。
另外，在其他实施例中，还可以提供一种基于数据全局特征的数字地图线要 素综合装置，其包括存储器和处理器；
所述存储器，用于存储计算机程序；
所述处理器，用于当执行所述计算机程序时，实现前述的基于全局特征的数字地图线要素综合方法。
另外，在其他实施例中，还可以提供一种计算机可读存储介质，该存储介质上存储有计算机程序，当所述计算机程序被处理器执行时，实现前述的基于全局特征的数字地图线要素综合方法。
以上所述的实施例只是本发明的部分较佳的方案，然其并非用以限制本发明。有关技术领域的普通技术人员，在不脱离本发明的精神和范围的情况下，还可以做出各种变化和变型。因此凡采取等同替换或等效变换的方式所获得的技术方案，均落在本发明的保护范围内。

Claims (10)

  1. 一种基于全局特征的数字地图线要素综合方法，其特征在于，包括以下步骤：
    S1：对数字地图中的每个线要素计算其几何特征重要度指数，几何特征重要度指数由该线要素的长度指数、周边密度指数和连接度指数加权求和得到；
    S2：按照几何特征重要度指数从低到高对数字地图中的线要素进行排序，并从数字地图中按照设定比例删减几何特征重要度指数最低的部分线要素；
    S3：计算S2删减后的简化数字地图和原始数字地图之间的地图相似度S以及要素删减率Rp；
    所述地图相似度S为简化数字地图的所有栅格值所构成的一维向量与原始数字地图的所有栅格值所构成的一维向量之间的相似度；
    所述要素删减率Rp为简化数字地图中相对于原始数字地图被删减的线要素数量占原始数字地图中线要素总数量的比例；
    S4：基于S3中得到的地图相似度S和要素删减率Rp计算S2删减后的简化数字地图的制图综合效果指数F＝(a×Rp 2+b)S+(1-a×Rp 2-b)Rp，其中a和b分别为常数系数且a>0；
    S5：不断迭代步骤S2～S4，直到所述地图相似度S低于最低相似度阈值时停止迭代，并以迭代过程中取得的最大制图综合效果指数对应的简化数字地图作为最终输出结果，完成数字地图线要素的综合。
  2. 如权利要求1所述的基于全局特征的数字地图线要素综合方法，其特征在于，数字地图中的每个线要素l的几何特征重要度指数IFI l计算方法如下：
    IFI l＝k 1L l+k 2M l+k 3N l
    式中：k 1、k 2、k 3均为权重系数，且k 1+k 2+k 3＝1；
    L l为线要素l的长度指数，计算公式如下：
    Figure PCTCN2020121520-appb-100001
    x＝d l/p z
    式中：x为线要素l在数字地图中的像元长度，k 4为权重系数，d l为线要素l的欧几里得距离长度，p z为数字地图的分辨率大小；
    M l为线要素l的周边密度指数，计算公式如下：
    M l＝a 1tan -1(b 1ρ l+c 1)+d 1
    式中：a 1、b 1、c 1、d 1均为权重系数，ρ l为线要素l的周边密度，其计算公式为：
    ρ l＝P/d l
    式中，P代表位于线要素l的缓冲区中且与线要素l不相交的其他线要素数量，n为线要素l的缓冲区中除线要素l之外的其他线要素的数量；
    N l为线要素l的连接度指数，计算公式如下：
    N l＝a 2tan -1(b 2C l+c 2)+d 2
    式中：a 2、b 2、c 2、d 2均为权重系数，C l为线要素l的连接度，其计算公式为：
    C l＝I/d l
    式中：I代表位于线要素l的缓冲区中且与线要素l相交的其他线要素数量。
  3. 如权利要求1所述的基于全局特征的数字地图线要素综合方法，其特征在于，所述的地图相似度S的计算方法如下：
    S301：对删减后的简化数字地图m与原始数字地图o分别进行栅格化，将矢量地图转换为栅格地图；
    S302：分别对每张栅格地图进行栅格赋值，每个栅格的值为所在栅格地图中经过该像元的线元素数量；
    S303：分别将两张栅格地图的栅格矩阵转换为一维向量，然后计算两个一维向量之间的余弦相似度，作为简化数字地图m与原始数字地图o之间的地图相似度S。
  4. 根据权利要求1所述的基于全局特征的数字地图线要素综合方法，其特征在于，所述的要素删减率Rp计算方法如下：
    Rp＝N r/N o
    式中：N r代表简化数字地图m中相对于原始数字地图o被删减的线要素数量，N o代表原始数字地图o中线要素总数量。
  5. 根据权利要求1所述的基于全局特征的数字地图线要素综合方法，其特征在于，在每一轮迭代所述S2过程中，从数字地图中按照设定比例删减几何特 征重要度指数最低部分线要素时，需保留两端连接有其他线要素的连接线要素，具体步骤顺次如下：
    S201：针对数字地图中的每个线要素构建两个端点要素集合，以第一集合记录与该线要素的第一个端点相交的线要素，以第二集合记录与该线要素的第二个端点相交的线要素；构建初始化为空集的第三集合，用于记录连接线要素；
    S202：针对数字地图中当前的几何特征重要度指数最低的线要素，判断其是否满足删除条件，若满足则删除该线要素并执行S203，若不满足则保留该线要素并将其视为连接线要素记入第三集合中；所述删除条件为线要素对应的第一集合和第二集合中至少有一个是空集；
    S203：每删除一个线要素，则更新与该线要素任一端点相交的所有线要素的两个端点要素集合，然后执行S204；
    S204：检查第三要素集合中是否存在重新满足所述删除条件的线要素，若存在则删除该线要素并重新执行S203，若不存在则不进行要素删除；
    S205：不断重复S202～S204，直至本轮删除的线要素数量达到所述设定比例对应的线要素数量。
  6. 根据权利要求1所述的基于全局特征的数字地图线要素综合方法，其特征在于，所述常数系数a＝1，常数系数b＝0。
  7. 根据权利要求1所述的基于全局特征的数字地图线要素综合方法，其特征在于，所述设定比例为原始数字地图中线要素总数量的0.5％～2％。
  8. 根据权利要求1所述的基于全局特征的数字地图线要素综合方法，其特征在于，所述几何特征重要度指数IFI l计算过程中，权重系数k 1、k 2、k 3取值分别为0.4、0.3、0.3；
    所述长度指数计算过程中，权重系数k 4取值为0.2；
    所述周边密度指数计算过程中，权重系数a 1、b 1、c 1、d 1分别为1/π、-0.2、6、0.55；
    所述连接度指数计算过程中，权重系数a 2、b 2、c 2、d 2分别为1/π、8、-6.3、0.45。
  9. 一种基于数据全局特征的数字地图线要素综合装置，其特征在于，包括存储器和处理器；
    所述存储器，用于存储计算机程序；
    所述处理器，用于当执行所述计算机程序时，实现如权利要求1～8任一项所述的基于全局特征的数字地图线要素综合方法。
  10. 一种计算机可读存储介质，其特征在于，所述存储介质上存储有计算机程序，当所述计算机程序被处理器执行时，实现如权利要求1～8任一项所述的基于全局特征的数字地图线要素综合方法。
PCT/CN2020/121520 2020-09-25 2020-10-16 基于全局特征的数字地图线要素综合方法、装置和介质 WO2022061994A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202011026520.1 2020-09-25
CN202011026520.1A CN112258645B (zh) 2020-09-25 2020-09-25 基于全局特征的数字地图线要素综合方法、装置和介质

Publications (1)

Publication Number Publication Date
WO2022061994A1 true WO2022061994A1 (zh) 2022-03-31

Family

ID=74234238

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/121520 WO2022061994A1 (zh) 2020-09-25 2020-10-16 基于全局特征的数字地图线要素综合方法、装置和介质

Country Status (2)

Country Link
CN (1) CN112258645B (zh)
WO (1) WO2022061994A1 (zh)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115719492A (zh) * 2022-11-29 2023-02-28 中国测绘科学研究院 一种面状要素宽窄特征识别方法、装置、设备及可读存储介质
CN116049206A (zh) * 2023-01-09 2023-05-02 广东省城乡规划设计研究院有限责任公司 一种空间规划业务库的更新方法
CN116630357A (zh) * 2023-05-24 2023-08-22 中国自然资源航空物探遥感中心 栅格地图目标线提取方法、系统、存储介质及计算设备
CN116977480A (zh) * 2023-09-21 2023-10-31 湖北大学 一种尺度相关的异质性线要素自动分段方法及系统

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020103599A1 (en) * 2001-01-30 2002-08-01 Kabushiki Kaisha Toshiba Route guidance generation apparatus and method
CN102663958A (zh) * 2012-03-23 2012-09-12 北京师范大学 一种顾及拓扑关系的大规模矢量地图快速综合的方法
CN104142962A (zh) * 2013-05-10 2014-11-12 北京四维图新科技股份有限公司 一种对电子地图的线要素进行处理的方法
CN104978763A (zh) * 2015-05-13 2015-10-14 中国矿业大学(北京) 一种基于三维Douglas-Peucker算法的河网要素与DEM的同步综合地图仿真方法
CN109145171A (zh) * 2018-07-23 2019-01-04 广州市城市规划勘测设计研究院 一种多尺度地图数据更新方法
CN110189618A (zh) * 2019-05-28 2019-08-30 南京大学 一种顾及密度差异的河渠线状水系要素自动制图综合方法

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4150744B2 (ja) * 1996-10-15 2008-09-17 松下電器産業株式会社 交通情報表示装置
CN101901489B (zh) * 2010-07-20 2011-12-28 南京大学 一种面向混合型复杂目标的距离图制图方法
CN108491482B (zh) * 2018-03-12 2022-02-01 武汉科技大学 一种顾及邻近度关系的地质图动态综合方法及系统

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020103599A1 (en) * 2001-01-30 2002-08-01 Kabushiki Kaisha Toshiba Route guidance generation apparatus and method
CN102663958A (zh) * 2012-03-23 2012-09-12 北京师范大学 一种顾及拓扑关系的大规模矢量地图快速综合的方法
CN104142962A (zh) * 2013-05-10 2014-11-12 北京四维图新科技股份有限公司 一种对电子地图的线要素进行处理的方法
CN104978763A (zh) * 2015-05-13 2015-10-14 中国矿业大学(北京) 一种基于三维Douglas-Peucker算法的河网要素与DEM的同步综合地图仿真方法
CN109145171A (zh) * 2018-07-23 2019-01-04 广州市城市规划勘测设计研究院 一种多尺度地图数据更新方法
CN110189618A (zh) * 2019-05-28 2019-08-30 南京大学 一种顾及密度差异的河渠线状水系要素自动制图综合方法

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115719492A (zh) * 2022-11-29 2023-02-28 中国测绘科学研究院 一种面状要素宽窄特征识别方法、装置、设备及可读存储介质
CN115719492B (zh) * 2022-11-29 2023-08-11 中国测绘科学研究院 一种面状要素宽窄特征识别方法、装置、设备及可读存储介质
CN116049206A (zh) * 2023-01-09 2023-05-02 广东省城乡规划设计研究院有限责任公司 一种空间规划业务库的更新方法
CN116049206B (zh) * 2023-01-09 2023-10-03 广东省城乡规划设计研究院有限责任公司 一种空间规划业务库的更新方法
CN116630357A (zh) * 2023-05-24 2023-08-22 中国自然资源航空物探遥感中心 栅格地图目标线提取方法、系统、存储介质及计算设备
CN116630357B (zh) * 2023-05-24 2024-04-26 中国自然资源航空物探遥感中心 栅格地图目标线提取方法、系统、存储介质及计算设备
CN116977480A (zh) * 2023-09-21 2023-10-31 湖北大学 一种尺度相关的异质性线要素自动分段方法及系统
CN116977480B (zh) * 2023-09-21 2023-12-12 湖北大学 一种尺度相关的异质性线要素自动分段方法及系统

Also Published As

Publication number Publication date
CN112258645B (zh) 2023-08-22
CN112258645A (zh) 2021-01-22

Similar Documents

Publication Publication Date Title
WO2022061994A1 (zh) 基于全局特征的数字地图线要素综合方法、装置和介质
CN104899849B (zh) 一种多目标交互式图像分割方法和装置
US6426747B1 (en) Optimization of mesh locality for transparent vertex caching
Scheiblauer et al. Out-of-core selection and editing of huge point clouds
Chen et al. A scale-adaptive DEM for multi-scale terrain analysis
JP7177768B2 (ja) ルート計画計算デバイスを改良するためのデバイス及び方法
US9877031B2 (en) System and method for multi-resolution raster data processing
JP2005332395A5 (zh)
CN113628314B (zh) 一种虚幻引擎中摄影测量模型的可视化方法、装置和设备
US20220198748A1 (en) Method for visualizing large-scale point cloud based on normal
WO2021062736A1 (zh) 划分方法、编码器、解码器以及计算机存储介质
CN108920765A (zh) 一种基于建筑物三维线段模型的假设平面拟合方法
CN116071519B (zh) 基于调和映射生成网格模型的图像处理方法和装置
CN110910462B (zh) 一种基于特征计算的点云轻量化方法及存储介质
US11988523B2 (en) Polygon overlap assignment using medial axis
CN116030180B (zh) 辐照度缓存光照计算方法及装置、存储介质、计算机设备
CN111061729B (zh) 顾及粒度的矢量瓦片组织方法
CN109035405B (zh) 一种基于预测-校正模型的网格简化方法
CN115937460A (zh) 基于最优传输的保特征表面重建方法
Platings et al. Compression of Large‐Scale Terrain Data for Real‐Time Visualization Using a Tiled Quad Tree
CN113495935A (zh) 一种基于不规则三角化网格剖分的地形切片方法和系统
Wolff Heat map segmentation
CN114565157B (zh) 基于地理映射的城市结构多重分形特征识别方法及装置
CN116416387B (zh) Osgb三维模型快速顶层重建方法
CN113835638B (zh) 一种存储系统中垃圾回收目的块的确定方法、装置及设备

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20954840

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20954840

Country of ref document: EP

Kind code of ref document: A1