KR101510618B1 - Method and apparatus for separating street trees areas using airborne lidar data - Google Patents

Abstract

The present invention relates to a method of separating a roadside area using air lagoon data and an apparatus thereof, and air lagoon data has three-dimensional position information on various objects such as buildings, trees, and the like existing on the ground. In order to extract the street number of the urban area from the point cloud data, it is necessary to separate the top point of the individual street number and the street number area. Accordingly, the present invention has developed an improved method of extracting the top point of a roadside tree and a method of separating a new roadside area from the existing method, and by doing so, it is possible to automate the management of the roadside tree managed by the local government The present invention relates to a method of isolating a roadside area using data and a device therefor.

Description

TECHNICAL FIELD [0001] The present invention relates to a method and apparatus for separating a roadside area using airlaid data,

More particularly, the present invention relates to a method of extracting an apex point of an individual avenue for constructing three-dimensional information of an individual avenue using air ladder data, And a method and a device for separating the area of the area.

LIDAR (LIght Detection And Ranging) data is used in various fields along with object extraction such as buildings, trees, etc. because it has 3-D location information of all objects on the ground.

Among the main land objects in the city, the roadside tree is one of the useful urban facilities that provide the green space with the function of reducing carbon dioxide and preventing pollution pollution. Also, it is necessary to acquire efficient information and systematic management as an important linear green space that forms a green network in the city. In general, it is managed by the local government, and airplanes are used to systematically acquire information such as the position, Is effective.

Conventional techniques for extracting tree information related to estimation of urban roadside tree information include a method using a canopy height model (CHM) that facilitates tree extraction for forest areas and a method of normalizing the ground height value of the ground object A method using a normalized digital surface model (NDSM) that facilitates extraction is mainly used.

In addition, we used the K-Means Clustering algorithm to extract the tops of the trees to separate areas of individual trees, and then normalized the height of the ground based on the point data. Then, we set the horizontal distance from the top point And a method of selecting a point within a threshold value as an individual tree has been conducted.

However, in the conventional techniques for extracting tree information by using the Lada data, mainly the dense trees in the forest area and the urban area in which only the trees are present, and the effort, the width of the water line, the location The direct research and development on the estimation of numerical information is still insufficient.

In addition, as a roadside area separation technique using air lathes is required as described above, Korean Patent Laid-Open Publication No. 2011-0027654 (Mar. 16, 2011), as a prior art document, A system and a method for capturing an image of an image in detail are proposed. The prior art discloses a system and method relating to acquiring an image in relation to an object or a wide area, wherein in an exemplary embodiment, a first system, including a first image capture device, Characterized in that it is related to each other along the image axis via a second system comprising a plurality of image capturing devices as well as acquiring or capturing an overview image representing the image, There is a problem in that it is not possible to extract points or separate areas of individual trees.

As another prior art, Korean Patent Registration No. 1089361 (2011.11.28) proposed a digital digital map system capable of modifying the image of the image through detection of the topographic change. The above-mentioned prior art document shows the modification of the image of the image through the terrain change detection, in which the position information of the reference point capable of replacing the function of the standard viewpoint in the aerial photographing is displayed in the aerial photographing image, However, there is a problem in that it is not possible to extract the canal points or separate the area of the individual line numbers.

In addition, there have been a lot of studies on the 3D modeling of buildings using Lada data both locally and externally. However, there have been few researches on the area segmentation technique for constructing the three - dimensional information of individual trees in the trees.

Therefore, it is necessary to develop a technique for extracting the top point of a tree line using the Lada data and a method for separating the area of an individual tree line.

The present invention has been made in order to solve the above problems, and it is an object of the present invention to provide a method of extracting a top point of an individual tree line and a method of separating an area of a tree line to construct three- And to provide the above-mentioned objects. A simulation airline which can know numerical accuracy of the number of trees, the number of trees, the width of watercourses, etc. is generated and the top point of the individual trees is extracted. Using the intersection of the straight line connecting the top and ground points It is possible to separate the area of the individual lines from the Lada data distributed in the point cloud form and extract the height value of the apex corresponding to the height of the individual line more accurately, And to provide an area separation method using airlaid data in order to automate and manage the management of the roadside trees.

와 표준편차

를 계산하여 평균값

을 기준으로 표준편차가

이내에 있는 제1 꼭대기 후보점은 선택하고 그 외의 제1 꼭대기 후보점을 정제하여 제2 가로수 꼭대기 후보점을 추출하는 것을 더 포함하며, 상기 개별 가로수의 영역을 분리하는 수단은, 우측 수목꼭대기점을 좌측 지면점에 이어 하나의 직선을 생성하고, 좌측 수목꼭대기점을 우측 투영된 지면점에 이어 다른 하나의 직선을 생성하며, 두 직선이 교차하는 교차점을 생성하는 것을 더 포함하고, 상기 개별 가로수의 영역을 분리하는 수단은, 교차점을 좌측 수목꼭대기점과 좌측 지면점을 잇는 직선에 투영하여 거리

를 계산하고, 교차점을 우측 수목꼭대기점과 우측 투영된 지면점을 잇는 직선에 투영하여 거리

를 계산하는 것을 더 포함하며, 상기 개별 가로수의 영역을 분리하는 수단은, 거리

및

를 계산하여 2차원 및 3차원 공간상에 개별 가로수의 영역 분리선 및 분리면을 생성하는 것을 더 포함하고, 상기 가로수 수치 정보를 추정하는 수단은, 개별 가로수가 포함하는 라이다 점들 중 최대 높이값을 수고값으로 추정하고, 개별 가로수 점을 이용하여 생성된 최대 원의 지름을 수관폭으로 추정하는 것을 특징으로 한다.The apparatus for separating a roadside area using airlaid data according to an embodiment of the present invention includes means for generating a mock airline distributed in the form of a point cloud to systematically obtain position, A street top point refining means for extracting a street top candidate point from the generated simulated air field; Means for separating an area of an individual tree number using the refined tree top point as an estimated top point; And means for estimating the street-line number information by calculating an average value of the plane positions of the individual line-separated points of the area. In addition, the above-mentioned canyon top point refinement means may comprise: a first top candidate point extracting means; A recent point selection means; And a second top candidate point extracting means, wherein the first top candidate point extracting means extracts non-avequant information from the ground and non-ground information including the aberration in the simulated airlaid data, Further comprising extracting a point, wherein the latest point selection means further comprises selecting a predetermined number of at least one more recent point of interest at the first top point candidate extracted by removing the non-avenue information, The top candidate point extracting means measures the height difference between the recent lean point selected at the first top candidate point and the latest lean point,

And standard deviation

And the average value

The standard deviation

Further comprising selecting a first top candidate point within the second tree and refining the first top candidate point to extract a second tree top candidate point, wherein the means for separating the area of the individual tree line comprises: Further comprising creating one straight line following the left ground point, creating a further straight line following the left projected ground point to the right projected ground point, and creating an intersection point where the two straight lines intersect, The means for separating the area is a means for projecting the intersection point on a straight line connecting the left top end point and the left ground point,

And the intersection point is projected on a straight line connecting the point of the right side of the tree to the right projected ground point,

Wherein the means for separating the area of the individual line numbers comprises means

And

Wherein the means for estimating the line number information comprises means for calculating the maximum height value of the Lada points included in the individual line numbers, And estimating the diameter of the largest circle generated by using the individual line number points as the water pipe width.

The method of isolating a roadside area using air lagoon data according to another embodiment of the present invention includes generating a mock airline distributed in a point cloud form in order to systematically obtain the position, ; Extracting an avenue nearest point from the generated simulated airline; Separating an area of an individual line number using the refined line number as an estimated top point; And estimating the street-line number information by calculating an average value of the plane positions of the individual line-separated points of the area.

According to the present invention, in order to construct a tree-line information database from airline data, it is possible to separate the individual lineage areas from the Lada data distributed in the form of a point cloud, It is possible to extract the height value of the top point more accurately, and it is possible to automate the management of the roadside managements managed by the local government by using air lagoon data.

1 is a block diagram illustrating a method of separating a roadside area using airline data according to an embodiment of the present invention.
FIG. 2 is an exemplary diagram showing two-dimensional and three-dimensional simulated airlaid data according to an embodiment of the present invention; FIG.
FIG. 3 illustrates an example of selecting a nearest point from a top candidate point according to an exemplary embodiment of the present invention to show an average distance. FIG.
4 is a block diagram showing the blurring of an individual tree area separation in accordance with an embodiment of the present invention.
FIG. 5 is a diagram illustrating an example of a tree line area setting according to an embodiment of the present invention.
FIG. 6 is an exemplary diagram showing the division of an individual tree-line area in two-dimensional and three-dimensional space according to an embodiment of the present invention; FIG.
FIG. 7 is an exemplary diagram illustrating an estimation of the avenue number information according to an embodiment of the present invention; FIG.
8 is a flowchart illustrating a method of separating a roadside area using airlaid data according to an embodiment of the present invention.

Hereinafter, a method of isolating a roadside area using airlaid data according to the present invention and one embodiment of the apparatus will be described with reference to the accompanying drawings.

FIG. 1 is a block diagram illustrating a method of separating a roadside area using an airlaid data according to an embodiment of the present invention. Referring to FIG. 1, a simulation airlaid data generation 100, a roadside point refinement 200, 300) and a number-of-way information (400).

First, the simulation airline data (100) is the point data generated to verify the estimation of the avenue information.

In order to generate the simulated data, environment setting and preparation for the aircraft and the laser scanner should be performed first. In the present invention, manually generated software is manually created based on a large number of researches related to researcher's Lada data processing. In addition, unlike the simulation data generation method of the present invention, simulation data can be generated through various methods such as satellite images and remote sensing images, and there is no limitation on generation of simulation data.

In the present invention, a method of generating a simulation data is performed by setting an area of an individual line number assuming a line width, generating a line number in the area, and assigning a height value according to the line type to generate an individual line number.

For reference, the width of a water pipe means the maximum diameter of the green leaf part perpendicular to the horizontal plane of the tree, that is, the diameter of the water pipe, and the water pipe type indicates the shape of the water pipe.

In addition, the purpose of using the simulated data is to examine the applicability of the proposed top - point refinement and domain separation method to obtain the reference value for the result verification and to improve the previous research method in the experimental process.

The selection of the area of the avenue, the normalization of the height of the ground, and the process of removing the low-height non-aberration information are not performed in the experiment. In this case, the height of the ground is set to 0m on the assumption that the normalization is completed. In the actual urban area, various information such as road facilities and automobiles exist. However, The simulated data include the information of the streetlight and the power line, and do not include objects with low height on the ground.

Next, the lineage top point refinement 200 refers to eliminating non-lineage information such as a streetlight, a telephone pole, and the like having a value similar to the maximum height of the avenue in the process of estimating the top point. In order to refine the estimated top point, in the present invention, the top point and the next point of the Lada point constituting the actual tree are characterized by a relatively small height difference.

Since the non-tree-line information may be misjudged as the apex of the avenue, it should be removed in the process of refining the apex of the avenue, and the input data is the point data from which the non-avenue information is removed.

The tree-top point refinement consists of refining the Lada point data and estimating the tree-top point.

Refinement of the Lida point data is the process of separating the ground and the non-ground and removing the information of the avenue including the building, the street lamp, and the automobile.

We use a variety of algorithms including progressive morphology algorithms to extract the information of a non-plane including the aberration from the grid data. At this time, it is possible to remove each object based on the height of the object including the street light, the power line, and the low object including the vehicle (for example, 10m or more for a high object and 2m or less for a low object) Since the height is not fixed, it can be arbitrarily set variously.

Estimation of the avenue top point uses the local maximum value filtering method applying circular filter similar to the shape of the tree to estimate the top point candidate point.

For reference, the local maximum means a summation that satisfies a value close to the value of a given domain, and the local maximum can be represented by filtering only the nearest value.

In the actual data processing, a normalized digital surface model (NDSM) generated by interpolating the non-ground surface data from which the non-tree objects such as buildings are removed is used.

For reference, the normalized numerical surface model may be relatively different depending on the height of the artifacts such as buildings, trees, etc. depending on the height and the height of the terrain. Therefore, the height value of the ground point is subtracted from the height value of the non- Is constantly normalized regardless of the change of the terrain.

Also, though not shown in the drawing, in experiments using simulation data, refined non-surface point data are interpolated using Nearest Neighbor Interpolation at the resolution of the point-averaged distance.

For reference, recent lean interpolation is a spatial approach based on the distance between the point symbols to assign the raw pixels closest to the position generated by the output pixels to the output pixels. That is, it can be interpolated by filling the empty pixels with neighboring nearest neighbors.

In this way, it is possible to extract the top of the first avenue through refinement of the Lada point and estimation of the top of the avenue.

The individual lineage area separation 300 then refers to separating the areas for the calculation of numerical information of the individual line numbers after the top point extraction.

In the present invention, an intersection point capable of generating a top point and a ground point between adjacent avenues is used for area separation.

In addition, the tree-level numerical information estimation (400) refers to estimating a tree-level numerical information item including a position, a length, and a width of a row of trees, and calculating an average value of planar positions of the points of the separated individual trees.

Hereinafter, a method of separating a roadside area using the airline data described above will be described in more detail.

FIG. 2 is a diagram illustrating an example of two-dimensional and three-dimensional simulated airlaid data according to an exemplary embodiment of the present invention.

As shown in FIG. 2, the airlaid simulation data includes a line number 110, a line number center 120, a ground 130, a street light 140, and a power line 150, , Triangles, squares, circles, shapes, patterns and colors. It is possible to set colors according to each area including each object of the grid data, and to display different colors by using the same color or pattern (letter, shape, pattern).

The street tree 140 and the power line 150 are connected to the tree line objects in the area 3 and 4 of the simulation data generated for analyzing the influence of the information of the street lamp 140 and the power line 150 coexisting with the tree- 150) exists at similar locations and heights.

The number of trees in the 1st to 4th areas is arranged to have an irregular planting interval. The areas 1 and 2 have a similar height and the area of the number of the trees is overlapped. At this time, labor is the height of the trees.

The number of trees in the 5th to 9th areas is gradually increased in order to analyze the extraction effect according to the proximity of the avenue.

In particular, in the simulated data, the width of the aqueduct of the avenue is the diameter value of the large circle in Fig. 2, irregular water pipe width is applied in the case of the first to fourth area, .

Also, in the simulated RDA data of FIG. 2, the items of the numerical reference value for the individual line number are composed of the number of the line number, the number of Lada points included in the individual line number, the position of the center point, the height and the water pipe width, It is used to verify the roadside numerical information estimate.

FIG. 3 is an exemplary diagram illustrating an average distance by selecting a nearest point at a top candidate point according to an exemplary embodiment of the present invention.

The number of trees in the urban area is basically located on the sidewalk, and the number of trees is planted considering the spacing of planting.

In order to refine the estimated top point, the top point of the avenue is estimated from the estimated top point 210 to the nearest point 210 on the assumption that the height difference from the nearby points is not large, And calculates the mean value and the standard deviation of the height difference.

The estimated apex candidate point is the first apex candidate point 210 extracted from the refinement of the aquaduct.

The average distance between the line-of-sight estimation points in a certain area is calculated based on the first aquaduct candidate point 210 as shown in FIG. This can be calculated as shown in Equation (1).

(1)

(One)

For each of the selected top candidate points 210, three points existing in the recent lean are selected on the basis of the horizontal distance as shown in FIG. 3, and the average difference is calculated by measuring the height difference between the three recent lean points.

In this data processing, arbitrary three points to five points are arbitrarily selected as the nearest line-of-sight estimation point or the shortest distance point as an object of the average distance calculation based on the first line number candidate point 210, Can be calculated.

에 대한 평균값

와 표준편차

를 계산하여 일정한 범위에 들어오는 가로수 꼭대기 후보점을 선택한다.The average distance between the line-of-sight estimation points calculated on the basis of each first avequil point candidate point 210

Average value for

And standard deviation

And selects a candidate for a tree-top candidate that falls within a certain range.

을 기준으로 표준편차가

이내에 있는 가로수 꼭대기 후보점을 선택하고,

이내에 포함되지 않는 값을 오류점으로 선택하여 가로수 꼭대기점을 정제함으로써 제2 가로수 꼭대기 후보점이 추출된다.At this time, the average value of the average distance between each of the avequest candidate points and the roadside estimation points

The standard deviation

≪ RTI ID = 0.0 > a < / RTI >

And the second aquaduct candidate point is extracted by refining the avequee top point by selecting a value that is not included within the second aquatic point candidate as an error point.

FIG. 4 is a block diagram showing the flow of the individual tree area separation in accordance with an embodiment of the present invention. Referring to FIG. 4, after the tree aquaplanes of FIG. 3 are refined, the individual tree aquifers It is shown on a two-dimensional or three-dimensional space to separate the avenue area.

The method of separating a roadside area includes a rectangle generation, a coordinate determination of a ground point, and an intersection calculation means. The calculation of the numerical information about the number of avenues is performed by using the apexes estimated.

In this case, the input data are refined top points and non - ground point data, and the proposed area separation method precedes the area selection process in which a line number exists using linear information such as extracted top point and road.

First, creating a rectangle containing the entire region refers to creating a rectangular region containing refined non-surface points, which is input data.

In this case, the area of the avenue is created on the two-dimensional space using the minimum and maximum values of X and Y of the line-of-sight estimation points.

As shown in the figure, the triangle represents the second aquaduct candidate point 211 as the apex of the avenues, the rectangle of the solid line represents the area of the generated rectangle, and the many points represent the adjacent avenues 110.

Next, the determination of the coordinates of the ground points refers to the calculation of the coordinates of the ground points 310 vertically lowered at each of the estimated top points 211. In this case, the triangle represents the top point 211 of each line, the circle connected vertically at the top point represents the ground point 310, and each top point and ground point can be expressed in three-dimensional space by various methods including solid lines. have.

Next, the calculation of the intersection point is performed by using the ridge top point 211 and the ground point 310.

Four points of coordinates of the left and right tree top points 213 and 215 and the left and right ground points 311 and 313 are used as input data and an arbitrary plane using four points to generate an intersection point 320, On the three-dimensional space.

Although not shown in FIG. 4, since any four points on the three-dimensional space can not form a plane, a plane is constructed using the top point of the tree and the point point data of one point, and the plane Projection method is used.

In addition, the intersection point 320 where the straight line generated by the left top and right projected ground points and the straight line generated by the right top and left ground points intersect each other is determined.

At this time, the rectangle formed in the center between the top and bottom ground points of the trees shows the intersection point, and the solid line shows the connecting line between the top point and the ground point of each tree.

FIG. 5 is a diagram illustrating an example of a tree-lined area setting according to an exemplary embodiment of the present invention.

First, when four data points of left and right tree tops LT (213), RT (215), left and right ground points LB (311) and RB (313) enter as input data, Construct a plane equation and project the remaining ground point data onto this plane. Assuming that a plane is constructed by using the left and right tree top points LT 213 and RT 215 and the left and right ground points LB 311 and RB 313 in FIG. 5, the right ground RB 313 point is projected on a plane RB '315 is generated.

The left tree top point LT 213 and the right transparent ground point RB 'in the left and right tree top points LT 213 and RT 215 and the left and right ground points LB 311 and RB' (315) as a straight line. In a similar manner, the right tree top point RT 215 and the left ground point LB 311 are created as one straight line and the intersection IP 320 where the two straight lines intersect is determined.

(321)을 계산할 수 있다. 또한 유사한 방법으로 교차점(320)을 우측 수목꼭대기점 RT(215)와 우측 투영된 지면점 RB'(315)를 잇는 직선에 투영하여 거리

(323)를 계산할 수 있다.At this time, the intersection IP 320 is projected on a straight line connecting the left tree top point LT (213) and the left ground point LB (311)

(321). In a similar manner, the intersection 320 is projected on a straight line connecting the right top end point RT 215 and the right projected ground point RB '315,

(323).

In this way, a kind of area can be set using the distance of the intersection projected to the top of the tree, and the lidar point data included in the top of each tree can be extracted.

FIG. 6 is a diagram illustrating the division of individual tree-line areas in a two-dimensional and three-dimensional space according to an embodiment of the present invention. In the two-dimensional space, four individual tree- Respectively.

Creation of the domain separation plane refers to creating two-dimensional and three-dimensional domain separation planes of some of the top points.

As shown in the drawing, it is possible to generate the area dividing line 340 orthogonal to the two-dimensional X and Y through the outline and the intersection point generated in the generation of the rectangle including the entire area, and the area dividing surface 330 in the three- You can create and distinguish individual regions.

In this case, the straight line passing through the intersection point in the two-dimensional space is the X-axis and the Y-axis, and the X-axis and Y-axis are orthogonal to each other and the generated surface including the intersection point on the three- But not as many as nine as the number of top points.

FIG. 7 is a diagram illustrating an estimation of the line number information according to an exemplary embodiment of the present invention. Referring to FIG. 7, after dividing an area of an individual line number, Set the diameter of the largest circle to the pipe width. At this time, the center point of the tree line can be calculated as the plane position average value (X, Y) of the individual line number points, and the calculated center point results of the individual line number can be expressed in the two-dimensional form.

In addition, to calculate the width of the individual line of trees, the maximum circle including the individual line is created and the diameter of the circle is calculated as the width of the pipe.

The concept of road-line numerical information estimation, which is calculated using individual line-of-sight points, is set up by including the position of the line number, the length of the line, and the width of the water line.

으로 계산하여 결정된다.The position information estimation is performed based on the average value of the plane positions of the individual line-

.

The height is estimated as the maximum height of the Lada points included in the individual line, and the width of the pipe is estimated as the diameter of the largest circle generated using the individual line number points.

와 표준편차

를 계산하며, 평균값

를 기준으로 표준편차

이내에 있는 제1 꼭대기 후보점을 선택하고, 표준편차

를 벗어나는 제1 꼭대기 후보점은 제거하여 제2 꼭대기 후보점을 추출(S300)한다.FIG. 8 is a flowchart illustrating a method of separating a roadside area using airlaid data according to an exemplary embodiment of the present invention, and generates simulated airlaid data using various methods including an aircraft and a laser scanner (S100). The generated simulated airlair data can be represented by two-dimensional and three-dimensional data. In the simulated airlaid data, the information including the ground information and the non-ground information is filtered through the filtering for separating the information of the non- (S200). ≪ / RTI > At this time, the first top candidate point is extracted by removing the non-line-of-sight information based on the height of a high object such as a street light, a power line, etc. and a low object such as a car. And selects a predetermined number of at least one latest receding point at the extracted first apex candidate point. At this time, the average difference is calculated by measuring the height difference between the selected last nearest points, and using the latest nearest points calculated on the basis of each first top candidate point,

And standard deviation

, And the average value

Standard deviation

≪ / RTI > and the standard deviation < RTI ID = 0.0 >

And the second top candidate point is extracted (S300) by removing the first top candidate point.

Based on the extracted second top candidate point, the area of the adjacent line number is divided using the straight line and the intersection point connecting the top of the tree and the ground, and the individual line number area is separated (S400) in order to calculate the numerical information of the individual line number. Also, the center point of the individual line number is estimated (S500) by using the positional average value of the separated individual line number points, the numerical information about the individual line number is obtained (S600) It is possible to more accurately extract the height value of the apex corresponding to the height of the individual line number and to automate the management of the road number managed by the local government using the airline Lda data.

The present invention proposes an algorithm for refining the tops of the trees and separating the individual tree areas using the aerial image data, and the following results can be obtained.

In the first urban area, the tops of the trees can be influenced by artificial facilities including electric wires and telephone poles. However, it is possible to refine the estimated tops of the trees by assuming that the planting spacing is constant and the difference in height between adjacent trees is not large. . To do this, we use the mean and standard deviation of the difference in height from the points in the current lean at the estimated aquaduct candidate points.

Second, we construct the equations of the plan by using the top and ground points of each avenue for the division of the individual line number area, and the intersection point is determined after creating a straight line connecting the top point and the ground point data in the diagonal direction. The determined intersection points are projected to the top point and the ground point of the vertical line, respectively, to set the individual line number area.

Thirdly, the number of trees and the number of trees are obtained as a result of applying the proposed methodology through experiment using the simulated airlaid data. In a total of 9 trees, the number of trees, the number of points, the location, and the width of the waterline may be different from the reference value. However, the information of the non-tree trees including streetlights, Because they are in close proximity. Also, it can occur even when there is a small number of trees in a row and the number of adjacent trees in a row is the same.

Fourth, in order to apply the proposed segmentation method to the real Lada data, it may have a limitation that the region selection process in which the line number exists is preceded.

Therefore, in the present invention, the individual line number is separated from the airline Lda data to construct the database of the line number information, so that it is possible to separate the area of the individual lineage from the Lada data distributed in the form of the point cloud, The height of the top point of the road can be extracted more accurately and it is possible to automate the management of the roadside manages managed by the municipality using the airline data.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. .

323 :

330 : 영역 분리면 340 : 영역 분리선
400 : 가로수 수치 정보 추정 100: Simulated airline generation 110: tree line
120: street center 130: ground
140: streetlight 150: power line
200: roadside point refinement 210, 211: first and second aqueduct points
213: Top tree point on the left LT 215: Top tree point on the right side RT
300: Individual line number area separation 310: ground point
311: left ground point LB 313: right ground point RB
315: projected right ground point RB '320: intersection point
321:

323:

330: area separation surface 340: area separation line
400: roadside numerical information estimation

Claims (18)

Means for generating simulated airline data distributed in the form of a point cloud in order to systematically acquire the position,
A street top point refining means for extracting a street top candidate point from the generated simulated air ladder data;
Means for separating an area of an individual tree number using the refined tree top point as an estimated top point; And
Means for estimating a street-level numerical information by calculating an average value of planar positions of the individual line-separated points of the area,
The means for separating the areas of the individual trees may comprise:
Two-dimensional rectangular area generating means including at least one of the estimated top point and non-ground point data;
A coordinate point determining means for determining coordinate point coordinates of a ground point representing a ground point vertically lowered at each of the estimated top points on a three-dimensional space; And
Means for calculating a coordinate intersection point of the left and right tree top points and left and right ground points,
Wherein the coordinate point determining means
A plane is configured by using one of left and right tree top points and left and right ground points, and the other ground point of the left and right ground points is projected on the plane to generate a projected ground point to determine coordinates of the ground point,
Wherein the coordinate intersection point calculation means comprises:
The top point of the right tree is formed as a straight line following the left projected ground point or the left ground point,
Wherein the intersection point where the two straight lines intersect is generated by creating another straight line following the top projected land point or the right ground point on the left side of the tree. The method according to claim 1,
The above-mentioned canyon top point refining means comprises:
A first top candidate point extracting means;
A recent point selection means; And
And a second top candidate point extracting means for extracting the second top candidate point from the image data. The method of claim 2,
The first apex candidate point extracting means extracts,
The first top candidate point is extracted by removing the non-avenues information from the ground and non-ground information including the avenue number in the simulated air lagoon data. The method of claim 2,
The nearest point-to-
And a predetermined number of at least one latest nearest point is selected from the extracted first top candidate points by removing the non-line-of-the-row information. The method of claim 2,
The second apex candidate point extracting means extracts,
The height difference between the most recent lint point selected at the first top candidate point and the recent lint point is measured and the average value of the measured height difference

And standard deviation

And the average value

The standard deviation

Wherein the first top candidate point is selected and the other top candidate points are refined to extract the second top candidate point. delete The method according to claim 1,
The means for separating the areas of the individual trees may comprise:
The intersection point is projected on a straight line connecting the left top end point and the left ground point,

Lt; / RTI >
The intersection point is projected on a straight line connecting the top point of the right tree and the ground point on the right side,

≪ / RTI >
Wherein the area dividing line and the dividing plane of individual line numbers are generated on the two-dimensional and three-dimensional space. delete The method according to claim 1,
Wherein the means for estimating the street-
Estimating the maximum height value among the lidar points included in the individual line numbers and estimating the diameter of the largest circle generated using the individual line number points as the water pipe width. Device. A step of generating mock airline data distributed in the form of a point cloud in order to systematically acquire the position,
Extracting a tree-top candidate point from the generated simulated airline data;
Separating an area of an individual line number using the refined line number as an estimated top point; And
And estimating the roadside number information by calculating an average value of the planar positions of the individual lineage points separated by the area,
The step of separating the area of the individual line number comprises:
A two-dimensional rectangular area generating step including at least one of the estimated top point and non-ground point data;
A coordinate point of a ground point representing a ground point vertically lowered at each of the estimated top points on a three-dimensional space; And
A coordinate intersection calculation step of a left and right tree top point and left and right ground points,
Wherein the step of determining the coordinates of the ground point comprises:
A plane is configured by using one of left and right tree top points and left and right ground points, and the other ground point of the left and right ground points is projected on the plane to generate a projected ground point to determine coordinates of the ground point,
The coordinate intersection calculating step may include:
The top point of the right tree is formed as a straight line following the left projected ground point or the left ground point,
Wherein the point of intersection of the two straight lines is generated by creating another straight line following the top point of the left tree at the right projected ground point or the right ground point. The method of claim 10,
The step of purifying the above-
A first top candidate point extraction step;
A recent point selection step; And
And a second top candidate point extraction step. The method of claim 11,
The first top candidate point extraction step may include:
Wherein the first top candidate point is extracted by removing non-aberration information from the ground and non-ground information including the aberration in the simulated airlaid data. The method of claim 11,
In the last point selection step,
And removing the non-line-tree information to select a predetermined number of at least one latest nearest point from the extracted first apex candidate point. The method of claim 11,
The second top candidate point extraction step may include:
The height difference between the most recent lean point selected at the first top candidate point and the recent lean point is measured, and the average value

And standard deviation

And the average value

The standard deviation

Selecting the first top candidate point within the first top candidate point and refining the first top candidate point to extract the second top candidate point. delete The method of claim 10,
The step of separating the area of the individual line number comprises:
The intersection point is projected on a straight line connecting the left top end point and the left ground point,

Lt; / RTI >
The intersection point is projected on a straight line connecting the top point of the right tree and the ground point on the right side,

≪ / RTI >
Wherein the area dividing line and the dividing plane of the individual line number are generated on the two-dimensional and three-dimensional space. delete The method of claim 10,
The method of claim 1,
Estimating the maximum height value among the lidar points included in the individual line numbers and estimating the diameter of the largest circle generated using the individual line number points as the water pipe width. Way.

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