JP7485957B2 - Land search method and program - Google Patents

Description

The present invention relates to a land search method and program.

In recent years, in order to increase the reusability of map information, different map data showing the same area may contain different information, and it may be necessary to identify plots of land that represent the same piece of land by matching map data.

Related technologies include, for example, a technology that acquires the location coordinates and attitude information of a terminal based on cadastral map data in which location coordinates and land addresses are associated, and acquires the land address of the location coordinates of a point. Another technology has been proposed that investigates the location of the land address to be searched and the status of land addresses in the vicinity based on a composite map in which a cadastral map is overlaid on a city block map. A further technology has been proposed that calculates a coefficient and offset to match two reference points on a residential address map and two reference points on a land address map, and converts the other to either the residential address map or the land address map.

A technology has also been proposed that specifies two or more matching feature points on a residential map and a cadastral map, and then embeds the cadastral map into the residential map based on the specified feature points. Furthermore, a technology has been proposed for adjusting forest planning information, including forest parcels shown on a forest planning map and forest locations, which are the land number information of the forest parcels, based on land parcel information and cadastral location information shown on a cadastral map, a cadastral map, or a land number map.

JP 2020-123078 A JP 2017-211787 A JP 2002-139993 A JP 2008-032810 A JP 2019-144887 A

However, positioning standards can differ depending on the map data, in which case the coordinates assigned to the same piece of land can differ depending on the map data, making matching based on coordinates difficult.

In one aspect, the present invention aims to provide a land search method and program that enables matching of land on different maps.

In order to solve the above problem, a land search method is provided. In the land search method, a computer calculates a first representative point of a first land included in a first map and a plurality of second representative points in a plurality of land pieces excluding the first land from the first map, calculates a first vector group connecting the first representative point and the second representative point, calculates a third representative point of a second land included in a second map having a measurement standard different from that of the first map and a plurality of fourth representative points in a plurality of land pieces excluding the second land from the second map, calculates a second vector group connecting the third representative point and the fourth representative point, calculates a similarity between the first vector group and the second vector group, and searches the second map for a second land piece having a second vector group similar to the first vector group based on the similarity, and associates the searched second land piece with the first land.

In addition, to solve the above problem, a program is provided that causes a computer to execute control similar to the above land search method.

In one aspect, it makes it possible to match land on maps with different positioning standards.

FIG. 1 is a diagram illustrating an example of an information processing apparatus according to a first embodiment. FIG. 13 illustrates an example of functional blocks of an information processing apparatus according to a second embodiment; FIG. 2 illustrates an example of a hardware configuration of an information processing device. FIG. 13 is a diagram illustrating an example of calculation of a center of gravity of a polygon. FIG. 2 is a diagram showing an example of land in real estate registry data. FIG. 13 is a diagram showing an example of a center of gravity of land in real estate registry data. FIG. 13 is a diagram showing an example of a vector connecting center points of land in real estate registry data. FIG. 2 is a diagram showing an example of land in farmland management data. FIG. 2 is a diagram showing an example of a center of gravity of land in farmland management data. FIG. 13 is a diagram showing an example of a vector connecting center points of land in farmland management data. FIG. 13 is a diagram showing an example of farmland association based on a group of similar vectors. 13 is a flowchart showing an example of an operation for determining a similarity between vector groups; 13 is a diagram showing an example of extraction of a first similar vector candidate having an angle difference within an angle threshold value; FIG. FIG. 13 is a diagram illustrating an example of extraction of a second similar vector candidate having a distance difference within a distance threshold. FIG. 13 is a diagram showing an example of voting for farmland having a similar vector. FIG. 13 is a diagram showing an example of the number of votes for farmland in farmland management data. 10 is a flowchart illustrating an example of an operation of the information processing device. 10 is a flowchart illustrating an example of an operation of the information processing device. 13 is a flowchart illustrating an example of an operation of data pre-processing. FIG. 1 is a diagram showing an example of items of real estate registry data and farmland management data before data pre-processing is performed. FIG. 13 is a diagram showing an example of items of real estate registry data and farmland management data after data pre-processing has been performed. FIG. 13 is a diagram showing an example of items of real estate registry data and farmland management data after the center of gravity has been calculated. FIG. 13 is a diagram showing an example of items of real estate registry data and farmland management data after vectors are calculated. FIG. 13 is a diagram illustrating an example of a data structure of real estate register data before data pre-processing is performed. FIG. 13 is a diagram illustrating an example of the data structure of real estate register data after the center of gravity is calculated. FIG. 13 is a diagram illustrating an example of the data structure of real estate register data after vectors are calculated. FIG. 13 is a diagram showing an example of an overlapping display of real estate registry data and farmland management data. FIG. 13 is a diagram showing an example of a display of farmland management data to which a lot number has been assigned.

The present embodiment will be described below with reference to the drawings.
[First embodiment]
1 is a diagram for explaining an example of an information processing device according to a first embodiment. The information processing device 1 includes a control unit 1a and a storage unit 1b. The control unit 1a searches a first map and a second map having different positioning standards for a second piece of land corresponding to a first piece of land included in the first map from the second map. Note that the "piece of land" corresponds to a partitioned area shown on the map.

In this case, the control unit 1a calculates a first representative point of a first land included in the first map, and multiple second representative points of multiple land pieces excluding the first land from the first map. For example, a center of gravity point can be used as the representative point. In addition, the control unit 1a calculates a first group of vectors connecting the first representative point and the second representative point.

In the same manner, the control unit 1a calculates a third representative point of a second land included in a second map having a different measurement standard from that of the first map, and multiple fourth representative points of multiple land excluding the second land from the second map, and also calculates a second group of vectors connecting the third representative point and the fourth representative point.

The control unit 1a then calculates the similarity between the first and second vector groups, and searches for a second piece of land having a second vector group similar to the first vector group on a second map that has a positioning standard different from that of the first map based on the similarity, and associates the searched second piece of land with the first piece of land. The memory unit 1b stores data on the first and second maps.

The operation will be described using the example shown in Fig. 1. The map data M1 and M2 are map data with different positioning standards.
[Step S1] The control unit 1a acquires map data M1 and M2. The map data M1 includes land areas R11, ..., R14, and the map data M2 includes land areas R21, ..., R24 and land areas R31, ..., R34.

[Step S2] The control unit 1a calculates representative points G11, ..., G14 of each of the land areas R11, ..., R14 included in the map data M1.
In addition, the control unit 1a calculates representative points G21, ..., G24 of the land areas R21, ..., R24, respectively, and representative points G31, ..., G34 of the land areas R31, ..., R34, respectively, included in the map data M2.

[Step S3] The control unit 1a calculates a vector group Vg10 including vectors V11, V12, and V13 that connect the representative point G11 of the land R11 to the representative points G12, ..., G14. Note that a vector group refers to multiple vectors that share a single representative point.

In addition, the control unit 1a calculates a vector group Vg20 including vectors V21, V22, and V23 that connect the representative point G21 of the land R21 to the representative points G22, . . . , G24.
Furthermore, the control unit 1a calculates a vector group Vg30 including vectors V31, V32, and V33 that connect the representative point G31 of the land R31 to the representative points G32, . . . , G34.

[Step S4] The control unit 1a performs a similarity determination of the vector groups, and detects a vector group similar to the vector group Vg10 from among the vector groups Vg20 and Vg30. In this example, it is assumed that the vector group Vg20 is determined to be similar to the vector group Vg10. Note that the state in which the two vector groups are considered to be similar and the similarity determination will be described later with reference to Figures 11 to 14.

[Step S5] If the control unit 1a determines that the vector group Vg20 is similar to the vector group Vg10, it searches the map data M2 for land that has the vector group Vg20. Since the land that has the vector group Vg20 on the map data M2 is land R21, the control unit 1a associates (matches) the searched land R21 with the land R11 included in the map data M1. Note that a land that has a vector group is a land that has the starting point of each vector included in the vector group as its representative point.

In this way, the information processing device 1 obtains a group of vectors connecting a representative point of a first piece of land on a first map with representative points of multiple other pieces of land on the first map. The information processing device 1 then searches for a second piece of land having a vector group similar to the obtained vector group from a second map that has a different positioning standard from the first map, and associates the searched second piece of land with the first piece of land. This makes it possible to match pieces of land on maps that have different positioning standards.

[Second embodiment]
Next, the information processing device according to the second embodiment will be described in detail. Here, the Ministry of Agriculture, Forestry and Fisheries manages the location and shape of farmland using farmland management data. In recent years, there has been an increasing need to link farmland management data with other data, such as paddy field management data.

In other data such as paddy field management data, each piece of land is assigned a land number, so it is possible to link other data based on the land number, but agricultural land management data does not have land numbers. For this reason, when linking agricultural land management data with other data, it is necessary to assign a land number to the agricultural land management data.

When assigning a land number to each piece of land in the agricultural land management data, it is possible to use, for example, real estate registry data managed by the Ministry of Justice. Real estate registry data is data that manages the location, shape, and land number of each piece of land.

In the second embodiment, farmland similar to farmland shown in the real estate registry data is searched for in the farmland management data, and the searched farmland is associated with the farmland shown in the real estate registry data. Note that the real estate registry data and the farmland management data are map data with different positioning standards. In the following explanation, the representative point will be described as the center of gravity.

<Function block>
2 is a diagram showing an example of functional blocks of an information processing device according to the second embodiment. The information processing device 10 includes a control unit 11 and a storage unit 12. The control unit 11 realizes the function of the control unit 1a in FIG. 1, and the storage unit 12 realizes the function of the storage unit 1b in FIG. 1.

The control unit 11 includes a map data input processing unit 11a, a data preprocessing unit 11b, a center of gravity calculation unit 11c, a vector group calculation unit 11d, a similarity determination unit 11e, a farmland identification unit 11f, a land number assignment unit 11g, and a display processing unit 11h.

The map data input processing unit 11a performs input processing of map data based on real estate registry data and farmland management data. The data preprocessing unit 11b extracts data required for performing farmland searches from the real estate registry data and farmland management data (specific examples of data preprocessing are described later in Figures 19 to 21).

The center of gravity calculation unit 11c calculates the center of gravity of land (including farmland, residential land, etc.) included in the real estate registry data and farmland management data.
The vector group calculation unit 11d calculates a group of vectors starting from the center of gravity of a farmland included in the real estate registry data and heading toward a plurality of center of gravity of a plurality of other plots of land.

In addition, the vector group calculation unit 11d calculates a group of vectors that start from the center of gravity of the farmland included in the farmland management data and point to multiple center of gravity of multiple other plots of land.

The similarity determination unit 11e performs a similarity determination between a first group of vectors calculated from the real estate registry data and a second group of vectors calculated from the farmland management data (the details of the similarity determination will be described later with reference to Figures 12 to 14).

The farmland identification unit 11f identifies farmland having a second vector group similar to the first vector group from the farmland management data. The land number assignment unit 11g assigns the land number of the farmland having the first vector group calculated from the real estate registry data to the identified farmland.

The display processing unit 11h displays farmland management data including farmland with land numbers. In addition, when a first vector group calculated from the real estate registry data and a second vector group calculated from the farmland management data are similar, the display processing unit 11h generates and displays an image in which the farmland having the first vector group and the farmland having the second vector group are superimposed (examples of the image display will be described later in Figures 27 and 28).

<Hardware>
3 is a diagram showing an example of a hardware configuration of an information processing device 10. The information processing device 10 is entirely controlled by a processor (computer) 100. The processor 100 has the function of a control unit 11.

The processor 100 is connected to a memory 101 , an input/output interface 102 , and a network interface 104 via a bus 103 .
The processor 100 may be a multiprocessor. The processor 100 may be, for example, a central processing unit (CPU), a field programmable gate array (FPGA), a micro processing unit (MPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), or a programmable logic device (PLD). The processor 100 may also be a combination of two or more elements of a CPU, an FPGA, an MPU, a DSP, an ASIC, or a PLD.

The memory 101 has the functions of the storage unit 12 and is used as the main storage device of the information processing device 10. The memory 101 temporarily stores at least a portion of the OS (Operating System) programs and application programs to be executed by the processor 100. The memory 101 also stores various data required for processing by the processor 100.

The memory 101 is also used as an auxiliary storage device for the information processing device 10, and stores the OS program, application programs, and various data. As an auxiliary storage device, the memory 101 may include a semiconductor storage device such as a flash memory or SSD (Solid State Drive), or a magnetic recording medium such as a HDD (Hard Disk Drive).

The peripheral devices connected to the bus 103 include an input/output interface 102 and a network interface 104. The input/output interface 102 has the function of the display processing unit 11h. The input/output interface 102 can also connect to information input devices such as a keyboard and a mouse, and transmits signals sent from the information input devices to the processor 100.

In addition, the input/output interface 102 also functions as a communication interface for connecting peripheral devices. For example, the input/output interface 102 can connect an optical drive device that uses laser light or the like to read data recorded on an optical disc. Optical discs include Blu-ray Disc (registered trademark), CD-ROM (Compact Disc Read Only Memory), and CD-R (Recordable)/RW (Rewritable).

The input/output interface 102 can also connect to a memory device or a memory reader/writer. A memory device is a recording medium equipped with a communication function with the input/output interface 102. A memory reader/writer is a device that writes data to a memory card or reads data from a memory card. A memory card is a card-type recording medium.

The network interface 104 connects to a network and performs network interface control. The network interface 104 may be, for example, a network interface card (NIC) or a wireless local area network (LAN) card. Data received by the network interface 104 is output to the memory 101 or the processor 100.

The above hardware configuration can realize the processing functions of the information processing device 10. For example, the information processing device 10 can perform the processing of the present invention by each of the processors 100 executing a predetermined program.

The information processing device 10 realizes the processing functions of the present invention, for example, by executing a program recorded on a computer-readable recording medium. The program describing the processing content to be executed by the information processing device 10 can be recorded on various recording media.

For example, a program to be executed by the information processing device 10 can be stored in an auxiliary storage device. The processor 100 loads at least a portion of the program in the auxiliary storage device into the main storage device and executes the program.

The program can also be recorded on a portable recording medium such as an optical disk, a memory device, or a memory card. The program stored on the portable recording medium can be installed in an auxiliary storage device under the control of the processor 100, for example, and then made executable. The processor 100 can also read and execute the program directly from the portable recording medium.

<Calculating the center of gravity>
4 is a diagram showing an example of calculation of the center of gravity of a polygon. When calculating the center of gravity of a polygon (hereinafter, referred to as a polygon) that represents the shape of land, the polygon is divided into multiple triangles, the areas of the triangles are multiplied by the x-coordinates of the centers of gravity of the triangles, and the multiplied value is divided by the sum of the areas of the triangles (the area of the polygon), to find the x-coordinate of the center of gravity of the polygon.

Similarly, the y coordinate of the polygon's center of gravity is found by multiplying the area of a triangle and the y coordinate of the triangle's center of gravity, and then dividing the product by the sum of the triangle's areas. Note that the center of gravity (Xg, Yg) of a triangle is (Xg, Yg) = ((X1 + X2 + X3)/3, (Y1 + Y2 + Y3)/3), where the vertices of the triangle are (X1, Y1), (X2, Y2), and (X3, Y3).

In the example of Figure 4, polygon P is divided into five triangles t1, ..., t5 based on an arbitrarily set origin. Here, the areas of triangles t1, ..., t5 are s1, ..., s5, the sum of the triangle areas (area of polygon P) is sa (=s1 + ... +s5), and the centers of gravity of triangles t1, ..., t5 are (x1, y1), ..., (x5, y5).

At this time, the coordinates of the center of gravity (xg, yg) of the polygon P are calculated by the following expressions (1a) and (1b).
xg = (s1 x x1 + s2 x x2 + ... + s5 x x5) / sa
...(1a)
yg = (s1 x y1 + s2 x y2 + ... + s5 x y5) / sa
...(1b)
<Land centroids and vectors from real estate registry data>
5 is a diagram showing an example of land in real estate registry data. The control unit 11 acquires map data for an area ar1 specified by a user from the real estate registry data. In the figure, polygons surrounded by thick solid lines indicate farmland, and polygons surrounded by thin solid lines indicate land such as residential land. Lands r11, ..., r18 are located around farmland r10.

Figure 6 is a diagram showing an example of the center of gravity of land in real estate registry data. The control unit 11 determines the center of gravity of multiple land parcels included in area ar1. The circles in the figure indicate the center of gravity of each polygon. The control unit 11 calculates the center of gravity g10 of farmland r10, and calculates the center of gravity g11, ..., g18 of each of the parcels r11, ..., r18.

Figure 7 is a diagram showing an example of vectors connecting the center points of land in real estate registry data. The control unit 11 calculates a vector starting from the center point of gravity of farmland and heading toward the center point of land such as residential land. In this example, the control unit 11 calculates a vector v11 starting from the center point of gravity g10 of farmland r10 and heading from the center point of gravity g10 to the center point of gravity g11 of land r11, and calculates a vector v12 from the center point of gravity g10 of farmland r10 to the center point of gravity g12 of land r12.

Similarly, the control unit 11 calculates a vector v13 extending from the center of gravity g10 of farmland r10 to the center of gravity g13 of land r13, and calculates a vector v14 extending from the center of gravity g10 of farmland r10 to the center of gravity g14 of land r14.

The control unit 11 also calculates a vector v15 from the center of gravity g10 of the farmland r10 toward the center of gravity g15 of the land r15, and calculates a vector v16 from the center of gravity g10 of the farmland r10 toward the center of gravity g16 of the land r16.

Furthermore, the control unit 11 calculates a vector v17 that points from the center of gravity g10 of farmland r10 to the center of gravity g17 of land r17, and calculates a vector v18 that points from the center of gravity g18 of farmland r10 to the center of gravity g18 of land r18. Note that vectors v11, ..., v18 starting from the center of gravity g10 of farmland r10 are defined as a vector group vg1.

<Land centroids and vectors of farmland management data>
8 is a diagram showing an example of land in the farmland management data. The control unit 11 acquires map data of an area ar2 designated by a user from the farmland management data. Note that the farmland management data does not include shapes of land other than farmland.

Therefore, the control unit 11 obtains the position and shape of the building from other data, and recognizes the land on which the building is located based on the shape of the building rather than the shape of the land. In the figure, polygons surrounded by thick solid lines indicate farmland, and polygons surrounded by thin solid lines indicate land recognized based on buildings. Lands r21, ..., r28 are located around farmland r20, and lands r31, ..., r38 are located around farmland r30.

Figure 9 is a diagram showing an example of the center of gravity of land in farmland management data. The control unit 11 determines the center of gravity of multiple land plots included in area ar2. The circles in the figure indicate the center of gravity of each polygon. The control unit 11 calculates the center of gravity g20 of farmland r20, and calculates the center of gravity g21, ..., g28 of each of the plots r21, ..., r28. The control unit 11 also calculates the center of gravity g30 of farmland r30, and calculates the center of gravity g31, ..., g38 of each of the plots r31, ..., r38.

Figure 10 is a diagram showing an example of vectors connecting the center points of gravity of land in farmland management data. The control unit 11 calculates a vector starting from the center point of gravity of the farmland and heading toward the center point of gravity of the land. In this example, the control unit 11 calculates a vector v21 starting from the center point g20 of farmland r20 and heading from the center point g20 to the center point g21 of land r21, and calculates a vector v22 from the center point g20 of farmland r20 to the center point g22 of land r22.

Similarly, the control unit 11 calculates a vector v23 extending from the center of gravity g20 of farmland r20 to the center of gravity g23 of land r23, and calculates a vector v24 extending from the center of gravity g20 of farmland r20 to the center of gravity g24 of land r24.

The control unit 11 also calculates a vector v25 from the center of gravity g20 of the farmland r20 toward the center of gravity g25 of the land r25, and calculates a vector v26 from the center of gravity g20 of the farmland r20 toward the center of gravity g26 of the land r26.

Furthermore, the control unit 11 calculates a vector v27 that points from the center of gravity g20 of the farmland r20 to the center of gravity g27 of the land r27, and calculates a vector v28 that points from the center of gravity g28 of the farmland r20 to the center of gravity g28 of the land r28. Note that the vectors v21, ..., v28 that start at the center of gravity g20 of the farmland r20 are defined as a vector group vg2.

Meanwhile, the control unit 11 calculates a vector v31 starting from the center of gravity g30 of farmland r30 toward the center of gravity g31 of land r31, and calculates a vector v32 starting from the center of gravity g30 of farmland r30 toward the center of gravity g32 of land r32.

Similarly, the control unit 11 calculates a vector v33 extending from the center of gravity g30 of farmland r30 to the center of gravity g33 of land r33, and calculates a vector v34 extending from the center of gravity g30 of farmland r30 to the center of gravity g34 of land r34.

The control unit 11 also calculates a vector v35 from the center of gravity g30 of the farmland r30 toward the center of gravity g35 of the land r35, and calculates a vector v36 from the center of gravity g30 of the farmland r30 toward the center of gravity g36 of the land r36.

Furthermore, the control unit 11 calculates a vector v37 that points from the center of gravity g30 of the farmland r30 to the center of gravity g37 of the land r37, and calculates a vector v38 that points from the center of gravity g38 of the farmland r30 to the center of gravity g38 of the land r38. Note that the vectors v31, ..., v38 that start at the center of gravity g30 of the farmland r30 are defined as a vector group vg3.

<Matching farmland based on similar vector groups>
11 is a diagram showing an example of farmland association based on similar vector groups. The control unit 11 searches for farmland having a vector group similar to the vector group vg1 calculated for farmland r10 included in the area ar1 of the real estate registry data from the area ar2 of the farmland management data.

In this example, region ar2 of the farmland management data includes farmland r20 having vector group vg2 and farmland r30 having vector group vg3. The control unit 11 searches region ar2 of the farmland management data for farmland having vector group vg2 similar to vector group vg1 of the real estate registry data, among vector groups vg2 and vg3 of region ar2 of the farmland management data.

Here, the control unit 11 determines whether the difference between the angle (direction) of each of the vectors v11, ..., v18 included in the vector group vg1 and the angle of each of the vectors v21, ..., v28, v31, ..., v38 included in the vector groups vg2 and vg3 is within the angle threshold.

Furthermore, the control unit 11 determines whether the difference between the distance of each of the vectors v11, ..., v18 included in the vector group vg1 and the distance of each of the vectors v21, ..., v28, v31, ..., v38 included in the vector groups vg2 and vg3 is within a distance threshold.

In this example, the control unit 11 determines that the difference between the angle of each of the vectors v11, ..., v18 included in the vector group vg1 and the angle of each of the vectors v21, ..., v28 included in the vector group vg2 is within the angle threshold.

Furthermore, the control unit 11 determines that the difference between the distances of the individual vectors v11, ..., v18 included in the vector group vg1 and the distances of the individual vectors v21, ..., v28 included in the vector group vg2 is within the distance threshold. In this case, the control unit 11 determines that the vector group vg2 is similar to the vector group vg1.

The control unit 11 recognizes the vector group vg2 in the farmland management data as a vector group similar to the vector group vg1 in the real estate registry data, and searches for farmland r20 that has the vector group vg2. The control unit 11 then associates the searched farmland r20 with farmland r10 in the real estate registry data, and assigns to farmland r20 the land number assigned to farmland r10 in the real estate registry data.

<Similarity Determination>
Next, the similarity determination of a vector group will be described with reference to Fig. 12 to Fig. 14. Fig. 12 is a flow chart showing an example of the operation of determining the similarity of a vector group. The control unit 11 compares one of the vectors calculated from the real estate register data with each of the vectors calculated from the farmland management data. Then, the control unit 11 determines a vector from the vectors of the farmland management data that is similar to the vector of the real estate register data.

[Step S11] The control unit 11 calculates the difference between the angle of one vector calculated from the real estate registry data and the angle of each of the multiple vectors calculated from the farmland management data.

[Step S12] The control unit 11 compares each of the multiple angle differences with a threshold value (angle threshold value), and includes vectors whose angle differences are within the angle threshold value as first similar vector candidates.
[Step S13] The control unit 11 calculates the difference between the distance of one vector calculated from the real estate registry data and the distance of each of the multiple vectors among the first similar vector candidates.

[Step S14] The control unit 11 compares each of the multiple distance differences with a threshold (distance threshold), and includes vectors whose distance differences are within the distance threshold as second similar vector candidates.
[Step S15] The control unit 11 determines whether the second similar vector candidates have been narrowed down to one vector. If the second similar vector candidates have not been narrowed down to one vector and there are multiple vectors, the process proceeds to step S16. If the second similar vector candidates have been narrowed down to one vector, the process proceeds to step S17.

[Step S16] The control unit 11 reduces either or both of the angle threshold value and the distance threshold value by a predetermined value, and then returns to the process of step S12.
[Step S17] The control unit 11 determines that one vector calculated from the real estate registry data is similar to one of the second similar vector candidates narrowed down to one in the farmland management data.

Here, one vector calculated from the real estate registry data is defined as vector va, and multiple vectors calculated from the farmland management data are defined as vectors vb1, . . . , vb10.
In step S11, the control unit 11 calculates the difference between the angle of one vector va calculated from the real estate registry data and the angle of each of the vectors vb1, . . . , vb10 calculated from the farmland management data.

In step S12, the control unit 11 compares each of the multiple angle differences with the angle threshold, and includes vectors whose angle differences are within the angle threshold in the first similar vector candidates. For example, assume that vectors vb1, ..., vb5 are included in the first similar vector candidates.

In step S13, the control unit 11 calculates the difference between the distance of one vector va calculated from the real estate registry data and the distance of each of the vectors vb1, ..., vb5 among the first similar vector candidates.

In steps S14 and S15, the control unit 11 compares each of the multiple distance differences with a distance threshold, and includes vectors whose distance differences are within the distance threshold as second similar vector candidates. Then, the control unit 11 determines whether the second similar vector candidates have been narrowed down to one vector. For example, it is assumed that vector vb1 is included in the second similar vector candidates.

In step S17, the control unit 11 determines that the vector va is similar to the vector vb1 of the second similar vector candidate that has been narrowed down to one. If the second similar vector candidate is not narrowed down to one vector and multiple vectors exist, the control unit 11 reduces one or both of the angle threshold and the distance threshold by a predetermined value (e.g., a 10% reduction) and repeats the similarity determination process.

<Specific example of first similar vector candidate with angle difference within angle threshold>
FIG. 13 is a diagram showing an example of extraction of a first similar vector candidate whose angle difference is within the angle threshold value.

[Step S21] The control unit 11 sets an orthogonal coordinate system of x and y axes for the center of gravity g10 of farmland r10 in area ar1 of the real estate registry data.
[Step S22] The control unit 11 calculates the angle of the vector v11 included in the vector group vg1. In the example of Fig. 13, the angle of the vector v1 is calculated to be 220° from the first quadrant of the coordinate system.

[Step S23a] The control unit 11 sets an orthogonal coordinate system of x-axis and y-axis for the center of gravity g20 of farmland r20 in area ar2 of the farmland management data, and calculates the angles of multiple vectors included in vector group vg2.

[Step S23b] The control unit 11 sets an orthogonal coordinate system of the x-axis and y-axis for the center of gravity g30 of farmland r30 in area ar2 of the farmland management data, and calculates the angles of the multiple vectors included in the vector group vg3.

[Step S24] The control unit 11 calculates the difference between the angle of vector v11 and the angles of all vectors included in vector groups vg2 and vg3 within area ar2 of the farmland management data, and determines whether the angle difference is within an angle threshold (e.g., within 5°).

[Step S25] The control unit 11 includes vectors whose angle difference is within 5° in the first similar vector candidates. For example, vectors v21 and v22 in vector group vg2 and vector v33 in vector group vg3 are included in the first similar vector candidates.

<Specific example of second similar vector candidate with distance difference within distance threshold>
FIG. 14 is a diagram showing an example of extraction of second similar vector candidates whose distance difference is within the distance threshold.

[Step S31] The control unit 11 calculates the distance of vector v11 included in vector group vg1. In this case, the distance from the origin of the coordinates set as described above (the start point of vector v1) to the end point of vector v1 is calculated. In the example shown in the figure, the distance of vector v1 is calculated to be 1.9 m from the origin of the coordinates.

[Step S32a] The control unit 11 calculates the distance between vectors v21 and v22 included in the first similar vector candidate.
[Step S32b] The control unit 11 calculates the distance of a vector v33 included in the first similar vector candidate.

[Step S33] The control unit 11 calculates the difference between the distance of vector v11 and the distances of vectors v21, v22, and v33, and determines whether the difference in distance is within a distance threshold (for example, within 1 m). Here, it is assumed that vector v21 is determined to be a vector with a distance difference of within 1 m and is included in the second similar vector candidates.

In this way, the control unit 11 executes the extraction process of the second similarity vector candidate for the vector v11 in the region ar1 of the real estate registry data and all vectors among the first similarity vector candidates. If the second similarity vector candidate cannot be narrowed down to one vector, as described above, the angle threshold or distance threshold value is reduced by a predetermined value and the similarity determination process similar to that described above is executed again.

<Voting for farmland with similar vectors>
15 is a diagram showing an example of voting for farmland having a similar vector. When the second similar vector candidates are narrowed down to one vector (similar vector), the control unit 11 votes for the farmland having the starting point of the similar vector. In this case, vector v21 has been narrowed down as the second similar vector candidate, so vector v21 is the similar vector, and a vote is cast for farmland r20 having vector v21, incrementing by 1. By carrying out this process, the control unit 11 associates the farmland with the most votes among farmlands r20 and r30 included in region ar2 of the farmland management data with farmland r10 of the real estate register data.

Figure 16 is a diagram showing an example of the number of votes for farmland in the farmland management data. Table T1 has items for multiple farms within the area of the farmland management data and the number of votes for each farm. In the example of Figure 16, farms R1, ..., R7 are located within the area of the farmland management data, and the voting results for farm R0 within the area of the real estate registry data are shown.

The number of votes for farmland R1, ..., R7 are 6, 3, 3, 1, 0, 0, and 2, respectively. In this case, farmland R1 has the most votes, so farmland R0 within the area of the real estate registry data is associated with farmland R1 within the area of the farmland management data.

<Overall flow chart>
17 and 18 are flowcharts showing an example of the operation of the information processing device.
[Step S41a] The control unit 11 performs an input process for real estate register data.

[Step S41b] The control unit 11 performs an input process for farmland management data.
[Step S42] The control unit 11 performs data pre-processing (described later with reference to FIGS. 19 to 21).
[Step S43] The control unit 11 calculates the center of gravity of the polygons indicated in the real estate registry data and the farmland management data.

[Step S44] For the polygons shown in the real estate registry data and the farmland management data, the control unit 11 calculates a vector starting from the center of gravity of the farmland polygon and pointing toward the center of gravity of the polygons other than the farmland.

[Step S45] The control unit 11 determines the similarity between the vectors of the real estate register data and the vectors of the farmland management data.
[Step S46] The control unit 11 searches for farmland in the farmland management data that has as its starting point a vector that is most similar to the vector in the real estate registry data, and votes for the found farmland.

[Step S47] The control unit 11 associates the farmland with the most votes in the farmland management data with the farmland in the real estate register data.
[Step S48] The control unit 11 assigns the land number of the farmland in the real estate registry data to the farmland in the associated farmland management data.

[Step S49] The control unit 11 displays the farmland management data including the farmland to which the lot number has been assigned.
<Data pre-processing>
FIG. 19 is a flowchart showing an example of the operation of data pre-processing.

[Step S51a] The control unit 11 performs an input process for real estate register data.
[Step S51b] The control unit 11 performs an input process for farmland management data.
[Step S52] The control unit 11 extracts feature polygon coordinates and land use items from the real estate registry data and farmland management data.

[Step S53] The control unit 11 determines whether the items extracted from the real estate registry data are the same as the items extracted from the farmland management data. If the items are the same, processing proceeds to step S54, and if the items are different, processing returns to step S52.

[Step S54] The control unit 11 updates the input data based on the extracted item data.
[Step S55a] The control unit 11 outputs the items extracted from the real estate registry data to the downstream processing unit (centre of gravity calculation unit).

[Step S55b] The control unit 11 outputs the items extracted from the farmland management data to a downstream processing unit (centre of gravity calculation unit).
<Data items>
FIG. 20 is a diagram showing an example of items of real estate registry data and farmland management data before data pre-processing is performed.

Data D1 shows the items of real estate registry data before data preprocessing is performed. Data D1 includes the following items: feature polygon coordinates, oaza code, chome code, hamlet code, reserve code, oaza name, chome name, hamlet name, reserve name, parcel number, accuracy classification, coordinate value type, and land use. Note that land use is information that indicates whether the land is agricultural or non-agricultural.

Data D2 shows the items of farmland management data before data preprocessing is performed. Data D2 includes the brush polygon ID, land use, and feature polygon coordinates. Note that brush polygons are farmland parcel information that is created based on satellite images, etc., according to the shape of each plot, for plots containing cultivated land, by dividing the land nationwide into 200 square meters (400 square meters in Hokkaido) as population information for cultivated land area surveys conducted by the Ministry of Agriculture, Forestry and Fisheries.

Figure 21 shows an example of items in real estate registry data and farmland management data after data preprocessing has been performed. Data D1a shows items in real estate registry data after data preprocessing has been performed. Data D1a includes items such as feature polygon coordinates and land use. Data D2a shows items in farmland management data after data preprocessing has been performed. Data D2a includes items such as feature polygon coordinates and land use.

Figure 22 shows an example of the items in the real estate registry data and farmland management data after the center of gravity has been calculated. Data D1b shows the items in the real estate registry data after the center of gravity has been calculated. Data D1b includes the following items: feature polygon coordinates, land use, and center of gravity coordinates. Data D2b shows the items in the farmland management data after the center of gravity has been calculated. Data D2b includes the following items: feature polygon coordinates, land use, and center of gravity coordinates.

Figure 23 shows an example of the items of real estate registry data and farmland management data after vectors have been calculated. Data D1c shows the items of real estate registry data after vectors have been calculated. Data D1c includes items such as feature polygon coordinates, land use, center of gravity coordinates, and vector values (with angles and distances) from farmland polygons to surrounding polygons. Data D2c shows the items of farmland management data after vectors have been calculated. Data D2c includes items such as feature polygon coordinates, land use, center of gravity coordinates, and vector values (with angles and distances) from farmland polygons to surrounding polygons.

<Data structure>
Fig. 24 is a diagram showing an example of the data structure of real estate registry data before data pre-processing is performed. In the explanations of Fig. 24 to Fig. 26, the data structure of the farmland management data is omitted because the farmland management data has basically the same data structure as the real estate registry data.

Data D10 includes polygon coordinates d1 and land use d2. Polygon coordinates d1 shows the coordinates of its four vertices: (291.299,-242.635), (294.42,-234.014), (291.317,-236.635), and (291.299,-242.635), and land use d2 is a field.

Figure 25 shows an example of the data structure of real estate registry data after the center of gravity has been calculated. Data D10-1 includes polygon coordinates d1, land use d2, and center of gravity coordinates d3. The center of gravity coordinates d3 are (291.012, -237.7613333333333).

Figure 26 shows an example of the data structure of real estate registry data after vector calculation. Data D10-2 includes polygon coordinates d1, land use d2, center of gravity coordinates d3, and vector information d40. Vector information d40 is information on the end point side of the vector, and includes vector target information d41 and vector value d42.

The vector target information d41 includes polygon coordinates d41a, land use d41b, and center of gravity coordinates d41c. The polygon coordinates d41a indicate the coordinates of the four vertices (114.732, -267.787), (116.212, -268.048), (117.241, -271.576), and (117.124, -271.726), the land use d41b is a public road, and the center of gravity coordinates d41c are (108.53208103445978, -283.88808889480634). The vector value d42 has an angle of 192.10007224366763 and a distance of 192.893526554897.

<Overlay display of real estate registry data and farmland management data>
27 is a diagram showing an example of an overlapping display of real estate registry data and farmland management data. After associating the farmland in the real estate registry data with the farmland in the farmland management data, the control unit 11 displays the farmland and its surroundings in the real estate registry data and the farmland and its surroundings in the farmland management data in an overlapping manner.

Now, let us assume that farmland r10 in the real estate registry data has been associated with farmland r20 in the farmland management data. At this time, the control unit 11 converts the coordinates of the polygon in the real estate registry data into the coordinates of the polygon in the farmland management data, and displays farmland r10 and its surroundings overlaid with farmland r20 and its surroundings. This allows the user to clearly see the correspondence between the real estate registry data and the farmland management data, thereby improving convenience.

<Display of farmland management data with assigned land numbers>
28 is a diagram showing an example of display of farmland management data to which a land number has been assigned. The control unit 11 assigns a land number to the farmland in the farmland management data associated with the farmland in the real estate registry data, and then displays the area ar2-1 of the farmland management data and the feature information D20 of the farmland to which the land number has been assigned.

The feature information D20 includes the address of the farmland, the brush polygon number, the type of land, the oaza, and the lot number. The address of the farmland r20 is 123 in City A, and the brush polygon identification number is 22208-0927590037023. The type of land is a field, the oaza is Izu Shimada, and the lot number is 541-1.

In this way, the display screen displays the farmland management data and feature information including the land number assigned to the farmland. This allows the user to visually recognize the farmland management data including the farmland with the assigned land number.

As described above, according to the present invention, land to be assigned a land number is searched for in real estate registry data using a group of vectors that indicate the positional relationship of the land to surrounding land, rather than the location or shape of the land. This makes it possible to match map data that do not have consistency in location or shape.

The processing functions of the information processing devices 1 and 10 of the present invention described above can be realized by a computer. In this case, a program is provided that describes the processing contents of the functions that the information processing devices 1 and 10 should have. By executing the program on a computer, the above processing functions are realized on the computer.

The program describing the processing contents can be recorded on a computer-readable recording medium. Examples of computer-readable recording media include magnetic storage units, optical disks, magneto-optical recording media, and semiconductor memories. Examples of magnetic storage units include hard disk drives (HDDs), flexible disks (FDs), and magnetic tapes. Examples of optical disks include CD-ROM/RWs. Examples of magneto-optical recording media include MOs (Magneto Optical disks).

When distributing a program, for example, a portable recording medium such as a CD-ROM on which the program is recorded is sold. The program can also be stored in the memory of a server computer, and then transferred from the server computer to other computers via a network.

A computer that executes a program stores, for example, a program recorded on a portable recording medium or a program transferred from a server computer in its own memory. The computer then reads the program from its own memory and executes processing according to the program. Note that the computer can also read a program directly from a portable recording medium and execute processing according to that program.

The computer can also execute processing according to the received program each time a program is transferred from a server computer connected via a network. At least some of the above processing functions can also be realized by electronic circuits such as DSPs, ASICs, and PLDs.

Although the above is an example of an embodiment, the configuration of each part shown in the embodiment can be replaced with other parts having similar functions. In addition, any other components or processes may be added. Furthermore, any two or more configurations (features) of the above-mentioned embodiments may be combined.

REFERENCE SIGNS LIST 1 Information processing device 1a Control unit 1b Memory unit M1, M2 Map data R11, ..., R14 Land R21, ..., R24 Land R31, ..., R34 Land G11, ..., G14 Representative point G21, ..., G24 Representative point G31, ..., G34 Representative point V11, V12, V13 Vector V21, V22, V23 Vector V31, V32, V33 Vector Vg10, Vg20, Vg30 Vector group

Claims (6)

The computer
Calculating a first representative point of a first land included in a first map and a plurality of second representative points of a plurality of land excluding the first land from the first map;
calculating a first vector group connecting the first representative point and the second representative point;
Calculating a third representative point of a second land included in a second map having a measurement standard different from that of the first map, and a plurality of fourth representative points of a plurality of land excluding the second land from the second map;
calculating a second group of vectors connecting the third representative point and the fourth representative point;
calculating a similarity between the first vector group and the second vector group, and searching the second map for the second land having the second vector group similar to the first vector group based on the similarity;
Corresponding the searched second land to the first land;
How to search for land. The land search method according to claim 1, wherein the computer determines that the second vector group is similar to the first vector group when the difference between the angle of each vector included in the first vector group and the angle of each vector included in the second vector group is within an angle threshold, and the difference between the distance of each vector included in the first vector group and the distance of each vector included in the second vector group is within a distance threshold. the computer includes the second vector in a first similar vector candidate if a difference between a first angle of a first vector in the first vector group and a second angle of a second vector in the second vector group is within the angle threshold;
performing a similarity determination for each vector included in the first vector group to include the second vector in a second similar vector candidate when a difference between a first distance of the first vector in the first vector group and a second distance of the second vector included in the first similar vector candidate is within the distance threshold;
When the second similar vector candidates are narrowed down to one second vector, the second vector is determined as a similar vector similar to the first vector;
If there are a plurality of vectors as the second similar vector candidates, the angle threshold or the distance threshold is reduced by a predetermined value and the similarity determination is performed again.
The land search method according to claim 2. The land search method according to claim 3, wherein the computer votes for the land having the starting point of the similarity vector, and the land with the most votes among all the land included in the second map is selected as the second land. The land search method according to claim 1, wherein the first land is a partitioned area shown on the first map, and the second land is a partitioned area shown on the second map. On the computer,
Calculating a first representative point of a first land included in a first map and a plurality of second representative points of a plurality of land excluding the first land from the first map;
calculating a first vector group connecting the first representative point and the second representative point;
Calculating a third representative point of a second land included in a second map having a measurement standard different from that of the first map, and a plurality of fourth representative points of a plurality of land excluding the second land from the second map;
calculating a second group of vectors connecting the third representative point and the fourth representative point;
calculating a similarity between the first vector group and the second vector group, and searching the second map for the second land having the second vector group similar to the first vector group based on the similarity;
Corresponding the searched second land to the first land;
A program that executes a process.

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