JP2022119040A - Road area identification device, road area identification method, and program - Google Patents

Abstract

To identify a road area precisely.SOLUTION: A road area identification device includes: a first acquisition unit for acquiring a numerical value altitude model showing altitude of the earth's surface of a predetermined geographical area and a numerical value surface layer model showing altitude of a feature surface of a geographical area; a second acquisition unit for acquiring ridge/valley line data showing a ridge line and a valley line of a geographical area; a calculation unit for calculating an inclination distribution and a feature height distribution of a geographical area on the basis of a numerical value altitude model and numerical value surface layer model; an extraction unit for extracting a road candidate area from a geographical area on the basis of an inclination distribution and a height distribution; an identification unit for identifying areas excluding areas corresponding to a ridge line and a valley line shown in ridge/valley line data from a road candidate area as road areas; and an output unit for outputting information regarding a road area.SELECTED DRAWING: Figure 6

Description

The present invention relates to a road area identification device, a road area identification method, and a program.

In recent years, many of the artificial forests created after the war have reached the end of their useful life, and there is a need to promote the logging of artificial forests for forest resource recycling. It is effective to develop a network of forest roads for efficient logging. However, depending on the planted forest, information on the existing road network, which is necessary for designing the road network, may not be updated. In this case, prior to designing a new road network, it is necessary to grasp information on the existing road network and convert it into data, and the number of man-hours required for this has become a problem.

In Patent Document 1, the land use status indicating whether the land is used as a road is estimated based on images taken from satellites and aircraft, and the route from the start point to the end point is calculated based on the land use status. A route creation system to create is described. According to the route creation system of Patent Literature 1, an appropriate route is created even if the land use situation is unknown.

JP 2013-19683 A

However, since the route creation system of Patent Document 1 estimates the land use situation based on the captured image, there is a problem that the accuracy of the route may not be sufficient due to noise such as clouds and sunlight included in the captured image. . Therefore, the route generation system of Patent Document 1 cannot estimate the road area with high accuracy.

Also, the height of the feature is obtained from the digital elevation model that indicates the elevation of the ground surface and the digital surface layer model that indicates the elevation of the surface of the feature, and based on the height of the feature, the road area is estimated without using the photographed image. be able to. However, in this case, rivers along valleys and premises around facilities have the same height as the road area, which causes erroneous extraction of the road area. Therefore, it was not possible to estimate the road area with high accuracy by this method.

SUMMARY OF THE INVENTION It is an object of the present invention to provide a road area identification device, a road area identification method, and a program capable of identifying a road area with high accuracy. .

A road area identification device according to the present invention comprises: a first acquisition unit for acquiring a digital elevation model indicating the elevation of the ground surface in a predetermined geographical area; A second acquisition unit that acquires ridge valley line data indicating the ridge lines and valley lines of the region, and a calculation that calculates the slope distribution and the height distribution of the geographical feature based on the digital elevation model and the digital surface layer model. an extraction unit for extracting a road candidate area from the geographical area based on the slope distribution and the height distribution; and an extraction unit for excluding areas corresponding to the ridge line and the valley line shown in the ridge valley line data from the road candidate area. and an output unit for outputting information about the road area.

Further, in the road area identification device according to the present invention, the extraction unit divides the geographical area into a flat area and a non-flat area based on the gradient distribution, and divides the geographical area into a covering area and a non-flat area based on the height distribution. It is preferable to divide the area into a covered area and to extract an area included in both the flat area and the uncovered area as the road candidate area.

In addition, the road area identification device according to the present invention further includes a third acquisition unit that acquires facility areas in which facilities are provided in the geographical area, and the identification unit further excludes the facility areas from the road candidate areas. Preferably, the area is identified as a road area.

A road area identification method according to the present invention is a road area identification method executed by a road area identification device, and includes a digital elevation model indicating the elevation of the ground surface in a predetermined geographical area and the elevation of the surface of a feature in the geographical area. obtain a digital surface layer model that indicates the ridge line and valley line of the geographical area, obtain ridge valley line data that indicates the ridge line and valley line of the geographical area, and based on the digital elevation model and the digital surface layer model, determine the slope distribution of the geographical area and the height of the feature We calculated the height distribution, extracted the road candidate area from the geographical area based on the slope distribution and the height distribution, and excluded the area corresponding to the ridge line and the valley line shown in the ridge valley line data from the road candidate area. identifying the area as a road area and outputting information about the road area.

A program according to the present invention acquires a digital elevation model indicating the elevation of the ground surface in a predetermined geographical area and a digital surface layer model indicating the elevation of the surface of a feature in the geographical area, and calculates ridge lines and valley lines in the geographical area. Obtain the ridge valley line data shown, calculate the slope distribution of the geographical area and the height distribution of features based on the digital elevation model and the digital surface layer model, and calculate the geographical area based on the slope distribution and height distribution a road candidate area is extracted from the road candidate area, areas corresponding to the ridge line and the valley line shown in the ridge valley line data are excluded from the road candidate area, and the road area is specified as the road area, and information about the road area is output. It is characterized by executing

INDUSTRIAL APPLICABILITY A road area identification device, a road area identification method, and a program according to the present invention make it possible to identify a road network of forest roads with high accuracy.

1 is a schematic diagram for explaining the outline of the present invention; FIG. 1 is a diagram showing an example of a schematic configuration of a road area specifying device 1; FIG. It is a figure which shows an example of the data structure of digital elevation model T1. It is a figure which shows an example of the data structure of the ridge valley line data T2. It is a figure which shows an example of the data structure of facility area|region data T3. FIG. 5 is a flow diagram showing an example of the flow of road area identification processing; FIG. 5 is a flow diagram showing an example of the flow of extraction processing;

Various embodiments of the present invention will now be described with reference to the drawings. It should be noted that the scope of the invention is not limited to those embodiments, but rather extends to the claims and their equivalents.

FIG. 1 is a schematic diagram for explaining the outline of the present invention. A road area identification device according to the present invention acquires a digital elevation model and a digital surface layer model of a target area, which is a geographical area to be identified as a road area. The digital elevation model is data representing the elevation of the ground surface S1 of the target area. The numerical surface layer model is data indicating the elevation of the feature surface S2 of the target area. The digital elevation model and the digital surface model are data in which a plurality of meshes, which are small rectangular regions obtained by dividing the target region, are associated with the elevation value of the representative point in each mesh. A digital elevation model and a digital surface model are generated in advance by remote sensing and surveying from the sky, and are stored in the road area identification device.

Also, the road area identification device acquires the ridge valley line data of the target area. The ridge valley line data is data indicating ridge lines and valley lines included in the target area, and is, for example, polygon data indicating the shapes of the ridge lines and valley lines with polylines. Ridge valley line data is generated in advance by executing a known hydrological analysis process using a digital elevation model, and is stored in the road area identification device.

The road area identification device calculates the gradient distribution and the height distribution of the feature in the target area based on the digital elevation model and the digital surface layer model. The slope distribution is data indicating the slope of the ground surface of the target area, and is calculated, for example, by taking the difference between the altitude values of adjacent meshes of the digital elevation model. The height distribution is data indicating the height of features in the target area, and is calculated by taking the difference between the elevation value of the digital surface model and the elevation value of the digital elevation model for each mesh. When the target area is forest land, the height distribution indicates the crown height distribution.

The road area identification device extracts road candidate areas from the target area based on the gradient distribution and the height distribution. In general, forest roads are provided in areas with small slopes so that vehicles can easily pass through them. In general, trees are not planted in areas where forest roads exist, so the height of the feature takes a value of zero or a small value. Therefore, the road area identification device can extract road candidate areas based on the gradient distribution and the height distribution.

For example, the road area identification device divides the target area into a flat area F and a non-flat area NF based on the gradient distribution. The flat region F is a region with a small slope, and the non-flat region NF is a region with a large slope. Further, the road area identification device divides the target area into a covered area C and a non-covered area NC based on the height distribution. The covered area C is an area where the height of the feature is large, and the non-covered area NC is an area where the height of the feature is small. The road area identification device extracts an area included in both the flat area F and the uncovered area NC as a road candidate area.

The road area specifying device specifies, as a road area R, an area obtained by excluding the neighboring area N of the ridge line and the valley line shown in the ridge valley line data from the road candidate area. The neighboring area N is an area whose distance from the ridge line and the valley line is equal to or less than a predetermined distance. In general, forest roads are not built near ridges. In general, the vicinity of the valley is a river, so it is not used as a forest road. The road area identification device identifies an area obtained by excluding these neighboring areas N from the road candidate areas as the road area R, thereby making it possible to identify the road network of forest roads with high accuracy.

The above description is for a better understanding of the invention. The present invention is implemented specifically by the following embodiments and various modifications.

FIG. 2 is a diagram showing an example of a schematic configuration of the road area specifying device 1 according to the present invention. The road area identification device 1 has a storage unit 11 , a communication unit 12 , a display unit 13 and a processing unit 14 .

The storage unit 11 is configured to store data and programs, and includes, for example, a semiconductor memory. The storage unit 11 stores an operating system program, a driver program, an application program, data, and the like used for processing by the processing unit 14 . The program is installed by a setup program from a computer-readable non-temporary portable storage medium such as a CD (Compact Disc)-ROM (Read Only Memory).

The communication unit 12 is configured to enable the road area identification device 1 to communicate with other devices, and includes a communication interface circuit. The communication interface circuit provided in the communication unit 12 is a communication interface circuit for wired LAN (Local Area Network), wireless LAN, LTE (Long Term Evolution), or the like. The communication unit 12 supplies the data received from the other device to the processing unit 14 and transmits the data supplied from the processing unit 14 to the other device.

The display unit 13 is configured to display an image, and includes, for example, a liquid crystal display or an organic EL (Electro-Luminescence) display. The display unit 13 displays images based on the display data supplied from the processing unit 14 .

The processing unit 14 is configured to collectively control the operation of the road area identification device 1, and includes one or a plurality of processors and their peripheral circuits. The processing unit 14 includes, for example, a CPU (Central Processing Unit), LSI (Large Scale Integration), or ASIC (Application Specific Integrated Circuit). The processing unit 14 may include a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), an FPGA (Field Programmable Gate Array), and the like. Based on the program stored in the storage unit 11, the processing unit 14 controls the operation of each component of the road area identification device 1 so that various processes of the road area identification device 1 are appropriately executed, process.

The processing unit 14 has a first acquisition unit 141 , a second acquisition unit 142 , a third acquisition unit 143 , a calculation unit 144 , an extraction unit 145 , a specification unit 146 and an output unit 147 . Each of these units is a functional module implemented by a program executed by the processing unit 14 . Each of these units may be implemented in the road area identification device 1 as firmware.

FIG. 3 is a diagram showing an example of the data structure of the digital elevation model T1 pre-stored in the storage unit 11. As shown in FIG. The digital elevation model T1 associates and stores mesh IDs, mesh positions, and elevation values for each of a plurality of meshes that are sub-regions obtained by dividing the target region.

A mesh ID is information for identifying each of a plurality of meshes. The mesh position is information for specifying the position of the mesh in the target area. In the example shown in FIG. 3, each mesh is rectangular and specified by two-dimensional coordinate values (for example, latitude and longitude) of four vertices. The altitude value is the altitude value of the ground surface at the representative point of each mesh (for example, the center of gravity of the mesh).

The digital surface layer model has the same data structure as the digital elevation model T1, except that the elevation value of the feature surface at the representative point of each mesh is stored as the elevation value. The numerical surface model is pre-stored in the storage unit 11 .

FIG. 4 is a diagram showing an example of the data structure of ridge valley line data T2 pre-stored in the storage unit 11. As shown in FIG. The ridge valley line data T2 stores polygon IDs, vertex positions and attributes in association with each other for each polygon corresponding to the ridge line or valley line.

A polygon ID is information for identifying each polygon corresponding to a ridge line or a valley line. A vertex position is a two-dimensional coordinate value of a vertex forming each polygon. A polygonal line indicating a ridge line or a valley line is formed by sequentially connecting the vertices whose vertex positions are stored with line segments. The attribute is an attribute of each polygon, and stores a value indicating whether each polygon corresponds to a ridge line or a valley line.

The ridge valley line data T2 is generated, for example, as follows. Based on the digital elevation model, the slope direction for each mesh included in the target area is calculated. By grouping adjacent meshes whose tilt directions are similar to each other, a plurality of mesh groups representing slopes whose tilt directions are substantially uniform are calculated. That the tilt directions are close to each other means, for example, that the angle formed by the tilt directions of the two meshes is less than or equal to a predetermined angle. By executing a known hydrological analysis process based on the slope indicated by each mesh group, a catchment area is calculated for each river included in the target area. Boundaries of catchments of mutually adjacent rivers are extracted as ridge lines. Further, valley lines are extracted by assigning a negative sign to the altitude value of each mesh and executing the above-described processing. By generating polygon data representing the shapes of the extracted ridge line and valley line, ridge valley line data T2 is generated.

FIG. 5 is a diagram showing an example of the data structure T3 of facility area data. The facility area data T3 is generated in advance by remote sensing or surveying from the sky and stored in the storage unit 11 . The facility area data stores polygon IDs and vertex positions in association with each other for each polygon corresponding to the facility area.

The polygon ID is information for identifying each polygon corresponding to the facility area. The facility area refers to an area in which facilities are provided within the target area, such as an area indicating the outer circumference of a building. A vertex position is a two-dimensional coordinate value of a vertex forming each polygon.

FIG. 6 is a flow diagram showing an example of the flow of road area identification processing executed by the road area identification device 1. As shown in FIG. The road area identification process is realized by the cooperation of each component of the road area identification device 1 based on the program stored in the storage unit 11 .

First, the first acquisition unit 141 acquires a digital elevation model indicating the elevation of the ground surface of the target area and a digital surface model indicating the elevation of the feature surface of the target area (S101). The first acquisition unit 141 acquires the digital elevation model and the digital surface model from the storage unit 11 . The first acquisition unit 141 may acquire the digital elevation model and the digital surface model via the communication unit 12 from a geographic information server storing the digital elevation model and the digital surface model in advance.

Subsequently, the second acquiring unit 142 acquires ridge valley line data indicating the ridge line and valley line of the target area (S102). The second acquisition unit 142 acquires ridge valley line data from the storage unit 11 . The second acquisition unit 142 may generate and acquire ridge valley line data based on the digital elevation model acquired by the first acquisition unit.

Subsequently, the third acquisition unit 143 acquires facility areas in which facilities are provided in the target area (S103). The third acquisition unit 143 acquires facility area data from the storage unit 11 .

Subsequently, the calculation unit 144 calculates the gradient distribution of the target area and the height distribution of the features based on the digital elevation model and the digital surface layer model (S104). The gradient distribution is data indicating the magnitude of the gradient of each mesh. The calculation unit 144 calculates the slope of each mesh in the first direction by taking the difference between the altitude value of each mesh of the digital elevation model and the altitude value of the mesh adjacent to each mesh in the first direction (for example, south side). Calculate the size of Further, the calculation unit 144 obtains the difference between the altitude value of each mesh and the altitude value of the mesh adjacent to each mesh in a second direction (for example, east side) perpendicular to the first direction, thereby calculating the Calculate the magnitude of the tilt in two directions. The calculation unit 144 calculates the magnitude of the gradient of each mesh by calculating the square root of the sum of the squares of the magnitude of the gradient in the first direction and the magnitude of the gradient in the second direction of each mesh.

The feature height distribution is data indicating the height of features arranged in each mesh. The calculation unit 144 calculates the height of the feature placed on each mesh by taking the difference between the altitude value of each mesh of the digital surface model and the altitude value of each mesh of the digital altitude model.

Subsequently, the extraction unit 145 executes extraction processing (S105). The extraction process is a process of extracting road candidate areas from the target area based on the gradient distribution and height distribution. Details of the extraction process will be described later.

Subsequently, the specifying unit 146 specifies, as a road area, an area obtained by excluding areas near ridge lines and valley lines and facility areas shown in the ridge valley line data from the road candidate area (S106). The specifying unit 146 sets a neighboring area based on the ridge valley line data. The neighboring area is generated, for example, as polygon data indicating an area that is a set of points whose distance from the ridge line or valley line indicated by the ridge valley line data is a predetermined distance (for example, 10 m) or less. The specifying unit 146 specifies, as a road region, a set of meshes that are included in neither the neighborhood region nor the facility region among the meshes included in the road candidate region. A mesh not included in either the neighborhood area or the facility area is, for example, a mesh whose center point is not included in either the neighborhood area or the facility area.

Subsequently, the output unit 147 outputs information about the road area (S107), and terminates the road area identification process. For example, the output unit 147 generates raster data with information indicating whether or not each mesh is a road area, and outputs by transmitting to another device via the communication unit 12 . The output unit 147 may generate display data for displaying a road area superimposed on a map image stored in advance, and display the data on the display unit 13 for output.

FIG. 7 is a flow diagram showing an example of the flow of extraction processing. The extraction process is executed in S105 of the road area specifying process.

First, the extraction unit 145 groups meshes having similar slopes within the target region based on the slope distribution (S201). Grouping is performed using the ISODATA method as described below so that the gradient magnitudes of each mesh belonging to a single group are close to each other.

First, the extraction unit 145 randomly classifies each mesh in the target region into one of a predetermined number (for example, 20) of groups. Subsequently, the extraction unit 145 counts the number of meshes included in each group. The extracting unit 145 combines groups with the number of meshes equal to or less than the lower limit with other groups, and divides groups with the number of meshes equal to or greater than the upper limit into two groups. Subsequently, the extracting unit 145 calculates the average value of the slope magnitudes of the meshes belonging to each group, and reclassifies each mesh into the group having the average value closest to the slope magnitude of each mesh. The extraction unit 145 repeats the above-described combination and division processing and reclassification processing until a predetermined termination condition is satisfied. In this way, the extraction unit 145 groups the meshes so that the magnitudes of the gradients of the meshes belonging to a single group are mutually similar. The termination condition is that the number of iterations is a predetermined number or more, or that the number of meshes belonging to different groups before and after reclassification is a predetermined value or less.

Grouping is not limited to the ISODATA method described above, and may be performed using other clustering methods such as the k-means method and mean-shift clustering. Further, the extraction unit 145 may group the meshes such that meshes that are adjacent to each other and have a slope difference equal to or less than a predetermined value belong to the same group.

Subsequently, the extraction unit 145 classifies each grouped region into either a flat region or a non-flat region (S202). The extraction unit 145 calculates the representative value of the inclination for each group. The representative value of the slope is the arithmetic mean value of the slopes of the meshes included in each group, but is not limited to this, and may be the geometric mean value, the median value, or the like. The extraction unit 145 classifies the groups in which the representative value of the inclination is less than a predetermined value as flat regions, and classifies the groups in which the representative value is equal to or greater than the predetermined value as non-flat regions. In this manner, the extractor 145 divides the target region into flat and non-flat regions based on the gradient distribution. In addition, we create training data that includes gradient distributions, flat regions, and non-flat regions in advance, create a trained model based on the training data, and use the trained model to generate target data based on the gradient distribution. A region may be divided into flat and non-flat regions. Algorithms used in trained models can be, for example, semantic segmentation in deep learning.

Subsequently, the extraction unit 145 groups meshes having similar heights within the target region based on the height distribution (S203). Grouping is performed using techniques such as those described above, such that meshes belonging to a single group are close to each other and feature heights are close to each other.

Subsequently, the extraction unit 145 classifies each grouped area into either a covered area or an uncovered area (S204). The extraction unit 145 calculates the representative value of the height of the feature for each group. The extraction unit 145 classifies groups in which the representative value of the height of the feature is equal to or greater than a predetermined value as covered areas, and classifies groups in which the representative value is less than the predetermined value as uncovered areas. In this manner, the extraction unit 145 divides the target area into covered areas and uncovered areas based on the height distribution.
In addition, we create teacher data that includes the height distribution of features, covered areas, and uncovered areas in advance, create a trained model based on the teacher data, and use the created trained model to calculate the height A region of interest may be divided into covered and uncovered regions based on the distribution. Algorithms used in trained models can be, for example, semantic segmentation in deep learning.

Subsequently, the extraction unit 145 extracts an area included in both the flat area and the uncovered area as a road candidate area (S205), and ends the extraction process. The extraction unit 145 determines whether each mesh is included in the flat area and included in the uncovered area. The extraction unit 145 extracts, as a road candidate area, an area formed by a set of meshes determined to be included in the flat area and included in the uncovered area.

As described above, the road region identification device 1 extracts road candidate regions based on the gradient distribution and height distribution of the target region, and from the road candidate regions, determines neighboring regions corresponding to ridge lines and valley lines. Identify road areas by exclusion. As a result, the road area identification device 1 can identify the road area with high accuracy without using the captured image.

In addition, the road area identification device 1 groups the meshes in the target area based on the gradient distribution, and classifies each grouped area into either a flat area or a non-flat area, thereby determining the target area. Divide into flat and non-flat regions. The grouping is such that the meshes belonging to a single group are close to each other and have similar slope magnitudes to each other. In this manner, by grouping the meshes based on the positional relationship of each mesh before dividing the target region, even if the target region is a region with a large amount of change in inclination, the segmentation can be performed with high accuracy.

In addition, the road area identification device 1 groups the meshes in the target area based on the height distribution, and classifies each grouped area into either a covered area or an uncovered area to determine the target area. is divided into covered and uncovered regions. The grouping is such that the meshes belonging to a single group are close to each other and feature heights are close to each other. In this manner, by grouping the meshes based on the positional relationship of each mesh before dividing the target region, even if the target region is a region with a large amount of change in height, the segmentation can be performed with high accuracy.

In the above description, the road area identifying device 1 acquires facility areas in which facilities are provided in the target area, but the present invention is not limited to this example. For example, facility location coordinates (eg, latitude and longitude) may be known, but facility area data may not exist. In this case, in S103 of the road area identification process, the third acquisition unit 143 may acquire the positional coordinates of the facility, and generate and acquire data of the facility area based on the positional coordinates of the facility. At this time, the facility area is set as, for example, a circular or rectangular area with the positional coordinates of the facility as the center of gravity.

In the above description, the small areas obtained by dividing the target area are rectangular meshes, but the present invention is not limited to this example. The subregions may be of any shape, such as triangles or polygons with pentagons or more. Also, the size and shape of each small region may be different from each other.

A part of the processing executed by the road area identification device 1 may be realized by another device.

It should be understood by those skilled in the art that various changes, substitutions and modifications can be made thereto without departing from the spirit and scope of the present invention. For example, the processing of each unit described above may be performed in a different order as appropriate within the scope of the present invention. Also, the above-described embodiments and modifications may be implemented in appropriate combinations within the scope of the present invention.

1 road area identification device 11 storage unit 12 communication unit 13 display unit 141 first acquisition unit 142 second acquisition unit 143 third acquisition unit 144 calculation unit 145 extraction unit 146 identification unit 147 output unit

Claims (5)

a first acquisition unit that acquires a digital elevation model indicating the elevation of the ground surface in a predetermined geographical area and a digital surface layer model indicating the elevation of the surface of the feature in the geographical area;
a second acquiring unit for acquiring ridge and valley line data indicating ridge lines and valley lines of the geographical area;
a calculation unit that calculates the slope distribution and the height distribution of the feature in the geographical area based on the digital elevation model and the digital surface model;
an extraction unit that extracts a road candidate area from the geographical area based on the gradient distribution and the height distribution;
an identifying unit that identifies, as a road area, an area obtained by excluding areas corresponding to the ridge line and the valley line indicated in the ridge valley line data from the road candidate area;
an output unit that outputs information about the road area;
A road area identification device characterized by comprising: The extractor is
dividing the geographical area into a flat area and a non-flat area based on the slope distribution;
dividing the geographical area into a covered area and an uncovered area based on the height distribution;
extracting an area included in both the flat area and the uncovered area as the road candidate area;
The road area specifying device according to claim 1. further comprising a third acquiring unit that acquires a facility area in which facilities are provided in the geographical area;
The identifying unit identifies, as the road area, an area obtained by excluding the facility area from the road candidate area.
3. The road area identification device according to claim 1 or 2. A road area identification method executed by a road area identification device, comprising:
obtaining a digital elevation model indicating the elevation of the surface of a given geographic area and a digital surface model indicating the elevation of the surface of a feature in said geographic area;
obtaining ridge and valley line data indicating ridge and valley lines of the geographic region;
Based on the digital elevation model and the digital surface layer model, calculate the slope distribution and the height distribution of the feature in the geographical area,
extracting a road candidate area from the geographical area based on the slope distribution and the height distribution;
specifying, as a road area, an area obtained by excluding areas corresponding to the ridge line and the valley line shown in the ridge valley line data from the road candidate area;
outputting information about the road area;
A road area identification method characterized by comprising: obtaining a digital elevation model indicating the elevation of the surface of a given geographic area and a digital surface model indicating the elevation of the surface of a feature in said geographic area;
obtaining ridge and valley line data indicating ridge and valley lines of the geographic region;
Based on the digital elevation model and the digital surface layer model, calculate the slope distribution and the height distribution of the feature in the geographical area,
extracting a road candidate area from the geographical area based on the slope distribution and the height distribution;
specifying, as a road area, an area obtained by excluding areas corresponding to the ridge line and the valley line shown in the ridge valley line data from the road candidate area;
outputting information about the road area;
A program that causes a computer to do something.

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