Vegetation management system and vegetation management method

Abstract

To improve accuracy of detecting a tree crown and robust property in vegetation management.SOLUTION: A system identifies a vegetation boundary in a target district on the basis of a remote sensing image of the target district and DEM data, and generate a height map which indicates a height of each position in the target district. The system detects an edge treetop which is in a buffer distance from an asset in the target district. The system estimates a tree crown size corresponding to a height indicated by the height map, using a plant allometry model which is a model based on a domain knowledge and is a model which has the height as input and has the tree crown size as output. The system estimates the tree crown area from the tree crown size corresponding to the edge treetop, estimates a bearing angle on the basis of a relationship between the tree crown area and the vegetation boundary, and generates a 3D bounding box of a tree having the edge treetop, on the basis of the height of the edge treetop, the tree crown size corresponding to the edge treetop, and the bearing angle.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates generally to vegetation management. [Background technology]

[0002] When trees encroach on assets (for example, power lines or houses), there is a possibility that the assets may be damaged or a fire (for example, a forest fire) may occur. Therefore, a vegetation management method disclosed in Patent Document 1, for example, is known. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] US11270111 Summary of the Invention [Problem to be solved by the invention]

[0004] The method disclosed in Patent Document 1 detects tree crowns by image processing based on similarity and discontinuity of the spectral values ​​of pixels in an image. However, this method does not provide high accuracy and robustness in detecting tree crowns. In particular, in dense forests where tree crowns overlap, many unnecessary tree crowns (e.g., crowns of trees that cannot invade an asset) may be detected. [Means for solving the problem]

[0005] The system uses a semantic segmentation model to identify vegetation boundaries in a target area based on remote sensing images, which are satellite images or aerial photographs, of the target area, and DEM (Digital Elevation Model) data of the target area. The system generates a height map, which is data representing the height of each position in the target area, based on the remote sensing images and the DEM data. The system detects edge treetops, which are treetops that meet a predetermined height condition and are within a buffer distance from an asset in the target area, from the height map. The system estimates tree crown sizes corresponding to the heights represented by the height map using a plant allometry model, which is a domain knowledge-based model that takes height as input and crown size as output. The system estimates a crown area from the detected crown size corresponding to the edge treetop, estimates an azimuth angle based on a relationship between the crown area and the vegetation boundary, and generates a 3D bounding box of an edge tree, which is a tree with an edge treetop, based on the height of the edge treetop, the detected crown size corresponding to the edge treetop, and the azimuth angle. Effect of the Invention

[0006] According to the present invention, the accuracy and robustness of tree crown detection in vegetation management is improved. [Brief description of the drawings]

[0007] [Figure 1] 1 shows an example of the configuration of a vegetation management system according to an embodiment. [Diagram 2] An example of the overall system configuration including a vegetation management system is shown. [Diagram 3] This shows the DB (database) stored in the vegetation management system. [Figure 4] The overall flow of processing performed by the vegetation management system is shown below. [Diagram 5] 1 shows the flow of a shoot detection process. [Figure 6] The flow of the tree crown size estimation process is shown. [Figure 7] 1 shows the flow of bounding box generation processing. [Figure 8] 4 shows the flow of risk analysis processing. [Figure 9] 13 illustrates a schematic overview of an example of a height estimation process. [Figure 10] 13A and 13B show schematic diagrams of an example of a boundary identification process. [Figure 11] 13 is a schematic diagram illustrating an example of a process for detecting shoots. [Figure 12] 3 illustrates a schematic diagram of an example of finding parameter values ​​for a 3D bounding box; [Figure 13] 1 illustrates the construction of a 3D bounding box. [Figure 14] 1 illustrates the construction of a bounding box for each edge tree. [Figure 15] The outline of risk analysis is shown diagrammatically. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] In the following description, an "interface unit" may refer to one or more interface devices. The one or more interface devices may be at least one of the following: An I / O interface apparatus which is one or more I / O (Input / Output) interface devices. The I / O (Input / Output) interface devices are interface devices to at least one of an I / O device and a remote display computer. The I / O interface device to the display computer may be a communications interface device. The at least one I / O device may be a user interface device, for example, either an input device such as a keyboard and a pointing device, or an output device such as a display device. A communication interface apparatus which is one or more communication interface devices. The one or more communication interface devices may be one or more homogeneous communication interface devices (e.g., one or more NICs (Network Interface Cards)) or two or more heterogeneous communication interface devices (e.g., a NIC and an HBA (Host Bus Adapter)).

[0009] In the following description, a "memory" refers to one or more memory devices, which are an example of one or more storage devices, and may typically be a primary storage device. At least one memory device in the memory may be a volatile memory device or a non-volatile memory device.

[0010] In the following description, a "persistent storage device" may be one or more persistent storage devices, which are an example of one or more persistent storage devices. A persistent storage device may typically be a non-volatile storage device (e.g., an auxiliary storage device), and more specifically, may be, for example, a hard disk drive (HDD), a solid state drive (SSD), a non-volatile memory express (NVME) drive, or a storage class memory (SCM).

[0011] Also, in the following description, "storage device" may be at least one of memory and persistent storage device.

[0012] Furthermore, in the following description, a "processor" may be one or more processor devices. The at least one processor device may typically be a microprocessor device such as a CPU (Central Processing Unit), but may also be other types of processor devices such as a GPU (Graphics Processing Unit). The at least one processor device may be a single-core or multi-core. The at least one processor device may be a processor core. The at least one processor device may also be a broader processor device such as a circuit that is a collection of gate arrays written in a hardware description language that performs part or all of the processing (e.g., an FPGA (Field-Programmable Gate Array), a CPLD (Complex Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit)).

[0013] In the following description, functions may be described using expressions such as "yyy unit", but the functions may be realized by one or more computer programs being executed by a processor, or by one or more hardware circuits (e.g., FPGA or ASIC), or by a combination thereof. When a function is realized by a program being executed by a processor, the function may be at least a part of the processor, since the specified processing is performed using a storage device and / or an interface device, etc., as appropriate. Processing described with a function as the subject may be processing performed by a processor or a device having the processor. A program may be installed from a program source. The program source may be, for example, a program distribution computer or a storage medium (e.g., a non-transitory storage medium) that can be read by a computer. The description of each function is an example, and multiple functions may be combined into one function, or one function may be divided into multiple functions.

[0014] In the following description, data that produces an output in response to an input may be described using expressions such as "xxxDB", but the data may be data of any structure (for example, structured data or unstructured data), or may be a neural network that generates an output in response to an input, or a learning model such as a genetic algorithm or random forest. Therefore, "xxxDB" may be referred to as "xxx dataset". In the following description, the configuration of each DB is an example, and one DB may be divided into two or more DBs, or all or part of two or more DBs may be one DB. "DB" is an abbreviation for database.

[0015] In the following description, when elements of the same type are described without distinction, common reference symbols will be used, and when elements of the same type are described with distinction, reference symbols will be used.

[0016] Fig. 1 shows an example of the configuration of a vegetation management system according to an embodiment, and Fig. 2 shows an example of the configuration of the entire system including the vegetation management system.

[0017] In this embodiment, the vegetation management system 100 is a physical computer system (one or more physical computers), but may be a logical computer system (e.g., a virtual machine or a cloud computing service) based on a physical computer system (e.g., a cloud platform). The vegetation management system 100 includes an interface device 211, a storage device 212, and a processor 213 connected to them. The vegetation management system 100 is, for example, a server system.

[0018] The interface device 211 can communicate with a UI (User Interface) device 250 and a remote sensor 201 via a communication network (e.g., the Internet or a WAN (Wide Area Network)). The UI device 250 may be an input device or a display device, or may be a remote device such as a client computer equipped with them. The user of the UI device 250 may be, for example, an administrator. The remote sensor 201 is typically a camera equipped on an artificial satellite or an aircraft. Therefore, the remote sensing image 202 output from the remote sensor 201 is typically image data of a satellite image or an aerial photograph image. The interface device 211 receives the remote sensing image 202 output from the remote sensor 201, and a processor 213 (e.g., a data interface unit not shown) stores the remote sensing image 202 in the storage device 212. In addition, although not shown, the interface device 211 receives weather data, DEM (Digital Elevation Model) data, and environmental data from an external data source (e.g., a server), and the processor 213 (e.g., a data interface unit not shown) stores the received data in the storage device 212.

[0019] Data and programs are stored in the storage device 212. For example, a weather DB 101, an image DB 102, a DEM DB 103, and an environmental DB 104 are stored in the storage device 212. Programs are read from the storage device 212 and executed by the processor 213, thereby realizing functions such as a height estimation unit 111, a boundary identification unit 112, an edge treetop detection unit 113, a crown size estimation unit 114, a bounding box generation unit 115, and a risk analysis unit 116.

[0020] FIG. 3 shows an example of DBs 101 to 104 in the storage device 212.

[0021] The weather DB 101 has one or more weather data, for example, the amount of precipitation, wind speed, and temperature for each day. The image DB 102 includes one or more remote sensing images 202. The DEM DB 103 includes one or more DEM data 301. Each of the weather data, the remote sensing images 202, and the DEM data 301 may be by region, and the region may be associated with latitude and longitude. The source of these data may be, for example, the LANDFIRE dataset.

[0022] The environment DB 104 includes one or more pieces of environment data, for example, soil data 311 and climate data 312. The soil data 311 indicates the latitude and longitude, soil type, soil moisture, and soil contents for each location (for example, region). The climate data 312 indicates the latitude and longitude and climate classification (for example, Köppen climate classification) for each location (for example, region).

[0023] Below, an example of the processing performed in this embodiment will be described, and each function of the vegetation management system 100 will be described as appropriate.

[0024] FIG. 4 shows the overall flow of the processing performed by the vegetation management system 100.

[0025] The height estimation unit 111 acquires meteorological data from the meteorological DB 101 for the area subject to vegetation management, for example, using the latitude and longitude of the area as a key (S401), acquires DEM data 301 from the DEM DB 103 (S402), and acquires the remote sensing image 202 from the image DB 102 (S403). The height estimation unit 111 performs height estimation processing based on the data acquired in S401 to S403 (S404). In S404, for example, the height estimation unit 111 generates a height map using the data acquired in S401 to S403 and a height estimation model. Note that the height estimation model may be a model (typically a machine learning model) that receives meteorological data, DEM data 301, and remote sensing image 202 as input and outputs a height map (an example of data representing height). In S401 to S403, in addition to data on the area subject to vegetation management, data for learning the height estimation model may also be acquired, and the acquired data may be used to learn the height estimation model in S404. The height map represents an estimated height for each region (e.g., one or more pixels) in the image represented by the remote sensing image 202. FIG. 9 shows a schematic overview of an example of height estimation processing. The remote sensing image 202A is input to the height estimation model, and a height map 902 is output. The height map 902 has a value for each pixel in the remote sensing image 202A according to the height of the position (latitude and longitude) corresponding to the pixel. Weather data does not necessarily need to be used for height estimation.

[0026] The boundary identification unit 112 performs S402 and S403 for the area of ​​the vegetation management target, for example, using the latitude and longitude of the area as a key. The boundary identification unit 112 performs boundary identification processing (S405). In S405, the boundary identification unit 112 finds the vegetation boundary by segmentation. Specifically, in S405, the boundary identification unit 112 identifies the vegetation boundary using the data acquired in S402 and S403 and a semantic segmentation model. Note that the semantic segmentation model may be a model that inputs the DEM data 301 and the remote sensing image 202 and outputs a vegetation boundary identification result. In S402 and S403, in addition to the data on the area of ​​the vegetation management target, data for learning the semantic segmentation model may also be acquired, and the semantic segmentation model may be learned in S405 using the acquired data. FIG. 10 shows an outline of an example of the boundary identification processing. The remote sensing image 202B is input to the semantic segmentation model, and a vegetation boundary identification result 1002 is output. The vegetation boundary identification result 1002 is image data in which the vegetation area represented by the remote sensing image 202B is masked. Specifically, for example, the vegetation boundary identification result 1002 is image data in which each pixel is expressed in binary, and pixels in the vegetation area are expressed in black, and pixels in areas other than the vegetation area are expressed in white. The vegetation boundary is the boundary of the generated mask of the vegetation area.

[0027] The edge treetop detection unit 113 performs a treetop detection process (S406). The crown size estimation unit 114 performs a crown size estimation process (S407). The bounding box generation unit 115 performs a bounding box generation process (S408). The risk analysis unit 116 performs a risk analysis process (S409).

[0028] S406 to S409 will be explained in detail below.

[0029] FIG. 5 shows the flow of the treetop detection process.

[0030] The edge detection unit 113 acquires the height map estimated by the height estimation unit 111 (S501).

[0031] The edge treetop detection unit 113 finds a local maximum value as a treetop (top of a tree) from the height map (S502). Specifically, for example, the edge treetop detection unit 113 finds a local maximum value assumed to be a treetop from the height map by applying a filtering window (maximum filtering window) to the height map. Specifically, for example, as shown in FIG. 11, by using the height map of the remote sensing image 202C and the filtering window, many treetops (local maximum values) 1101 are detected. Note that the remote sensing image 202C illustrated in FIG. 11 has an asset area 1102. The asset area 1102 is an area defined to be occupied by an asset. In this embodiment, the asset is a power transmission line or a steel tower supporting the power transmission line.

[0032] Of the treetops detected in S502, the edge treetop detection unit 113 maintains edge treetops that are within a buffer distance from the asset (S503). Specifically, for example, as shown in FIG. 11, the edge treetop detection unit 113 maintains (leaves) treetops 1101 that are within a buffer distance from the asset area 1102 out of multiple treetops 1101, and disables the other treetops. Treetops maintained in this way, i.e., treetops that are within the buffer distance from the asset, may be referred to as "edge treetops" hereinafter. A treetop with an edge treetop may be referred to as an "edge tree crown". A tree with an edge tree crown may be referred to as an "edge tree".

[0033] FIG. 6 shows the flow of the crown size estimation process.

[0034] The crown size estimation unit 114 trains a plant allometry model using a data set including a plurality of height-crown pairs (S601). The plant allometry model is a domain knowledge-based model (typically a machine learning model) that takes tree height as input and tree crown size as output. A height-crown pair is a pair of height and crown size. The plant allometry model may be trained in advance, and therefore S601 may not be required for the crown size estimation process.

[0035] The tree crown size estimation unit 114 acquires the height map estimated by the height estimation unit 111 (S602).

[0036] The crown size estimation unit 114 estimates the crown size of all trees in the height map by inputting the height map into a plant allometry model (S603). Of the heights represented by the height map, heights equal to or greater than a threshold may be regarded as the height of the trees. Alternatively, the crown size may be output for each height represented by the height map.

[0037] FIG. 7 shows the flow of the bounding box generation process.

[0038] The bounding box generation unit 115 acquires the vegetation boundary (vegetation boundary identification result) identified by the boundary identification unit 112 (S701), acquires the height map estimated by the height estimation unit 111 (S702), and acquires data regarding each edge branch detected by the edge branch detection unit 113 (e.g., data including position data of each edge branch) (S703).

[0039] The bounding box generation unit 115 determines the height of the edge treetops from the data obtained in S703 (data regarding each edge treetop) and the height map obtained in S702, and determines the tree crown size (crown size of the edge tree crown) corresponding to the height of the edge treetops from the estimation results by the tree crown size estimation unit 114 (crown sizes of all trees in the height map) (S704).

[0040] The bounding box generation unit 115 estimates the canopy area using the canopy radius (S705). The bounding box generation unit 115 identifies boundary segments that are ranges delimited by the estimated canopy area from the vegetation boundary acquired in S701 (S706).

[0041] Bounding box generating section 115 generates a 3D bounding box (three-dimensional bounding box) of the tree based on the results of S704 to S706 (S707).

[0042] A specific example of generating a 3D bounding box will be described below with reference to FIGS.

[0043] 12, three edge crowns 1201A to 1201C (i.e., three edge trees) are arranged side by side, and each of the edge crowns 1201A to 1201C partially overlaps with the adjacent edge crown in a planar view. Taking the edge crown 1201B as an example, the generation of a 3D bounding box of an edge tree having the edge crown 1201B will be described.

[0044] Assume that the edge treetop obtained in S703 is edge treetop C. Assume also that the treetop size identified in S704 is a treetop radius r. In this case, in S705, the bounding box generating unit 115 estimates an area surrounded by a circle of a treetop radius r centered on the edge treetop C as the treetop area 1250 of the edge treetop 1201B.

[0045] It is assumed that the vegetation boundary acquired in S701 is a vegetation boundary 1202. In S706, the bounding box generation unit 115 specifies a boundary segment AB, which is a range of the vegetation boundary 1202 that is bounded by the estimated canopy area 1250. The boundary segment AB is a line segment that connects two intersection points A and B between the perimeter of the canopy area 1250 and the vegetation boundary 1202.

[0046] Then, in S707, the bounding box generation unit 115 generates a 3D bounding box of the edge tree having the edge crown 1201B as follows.

[0047] That is, the bounding box generation unit 115 translates the line including the boundary segment AB (line segment AB) so that it passes through the edge treetop C (center of the circle). This creates intersections D and E between the circumference of the treetop canopy area 1250 and the translated line passing through the edge treetop C. There is also a vertical diameter XY (intersections X and Y between the circumference of the treetop canopy area 1250 and the vertical line passing through the edge treetop C). As a result, an angle XCD is created. The angle XCD is the azimuth angle, and the magnitude of the azimuth angle is "θ". Furthermore, the height of the edge treetop C is "h".

[0048] The bounding box generator 115 constructs a 3D bounding box with a width and height of "2r" (twice r) and a height of "h" as shown in Fig. 13, and rotates the 3D bounding box horizontally by an azimuth angle θ around the edge treetop C (i.e., adapts the 3D bounding box to the orientation of the tree). The 3D bounding box rotated by the azimuth angle θ in this way is the 3D bounding box of the edge tree having the edge crown 1201B.

[0049] The bounding box generator 115 generates a 3D bounding box of an edge tree having each edge tree by the method described with reference to Fig. 12 and Fig. 13. As a result, 3D bounding boxes 1300A-1300C are constructed as shown in Fig. 14 for the edge tree crowns 1201A-1201C shown in Fig. 12. The bounding box generator 115 stores data relating to the 3D bounding box of each edge tree in the storage device 212. For each edge tree, the data relating to the 3D bounding box may include width, length and height values, an azimuth angle value, and position data of the edge tree.

[0050] FIG. 8 shows the flow of the risk analysis process.

[0051] The risk analysis unit 116 acquires the 3D bounding box (data relating to the 3D bounding box) of each edge tree from the storage device 212 (S801). Hereinafter, the 3D bounding box of an edge tree may be referred to as an "edge tree box."

[0052] The risk analysis unit 116 acquires the 3D bounding box of the asset (data related to the 3D bounding box) from the storage device 212 (S802). For example, according to the remote sensing image 202C shown in FIG. 11, the asset area 1102 is identified from the remote sensing image 202C, and data related to the 3D bounding box of each asset (e.g., a tower) in the asset area 1102 is stored in the storage device 212. The 3D bounding box of each asset may be generated by the bounding box generation unit 115 based on the height map of the remote sensing image 202C. Hereinafter, the 3D bounding box of the asset may be referred to as an "asset box."

[0053] The risk analysis unit 116 acquires meteorological data from the meteorological DB 101 and environmental data from the environmental DB 104 for the area subject to vegetation management, for example, using the latitude and longitude of the area as a key (S803).

[0054] The risk analysis unit 116 simulates the edge tree collapse (S804). Specifically, for example, the risk analysis unit 116 places the edge tree box 1300 of each edge tree at a position based on the position data of the edge tree, places the asset box of each asset at a position based on the position data of the asset, and then rotates each edge tree box 1300 in a three-dimensional direction, for example, 0 degrees to 90 degrees, as shown in Fig. 15. The risk analysis unit 116 determines for each edge tree whether the edge tree box contacts the asset box.

[0055] The risk analysis unit 116 estimates the contact risk (S805). Specifically, for example, the risk analysis unit 116 may estimate the contact risk (for example, calculate a numerical value representing the magnitude of the contact risk) based on the number of edge tree boxes that contact any of the asset boxes. The risk analysis unit 116 may calculate the probability and degree of edge tree collapse (for example, blow-in or fall-in) occurring during the period represented by the acquired weather data from the weather data (for example, data representing wind speed, etc.) and environmental data (for example, data representing the soil and climate zone at the position of the edge tree) acquired in S803, and use the probability and degree of collapse as the basis for estimating the contact risk. In addition, the risk analysis unit 116 may calculate the probability that the edge tree will collapse and contact the asset from the probability and degree of collapse and the rotation angle when the edge tree box contacts the asset box, and use the calculated contact probability as the basis for estimating the contact risk.

[0056] Various data acquired or generated in the vegetation management process may be stored in the storage device 212. For example, a height map, position data of the detected edge treetops, data on the crown size of each edge treetop, data representing vegetation boundaries, data on the 3D bounding box of the edge treetops, data representing the results of the risk analysis process (e.g., the simulation results of S804 and the contact risk estimated in S805), and the like may be stored in the storage device 212. The processor 213 (e.g., a data interface unit not shown) may display information represented by the data stored in the storage device 212 on the UI device 250. The displayed information may be, for example, at least one of a height map, a remote sensing image on which an object representing an edge treetop, a vegetation boundary, an asset area, or the like is superimposed, a 3D bounding box of the edge treetops, an animation of the rotation of the 3D bounding box of the edge treetops, a 3D bounding box of the asset, and a result of the risk analysis process (e.g., the simulation results of S804 and the contact risk estimated in S805).

[0057] Although one embodiment has been described above, this is merely an example for explaining the present invention, and the scope of the present invention is not limited to this embodiment. The present invention can be implemented in various other forms.

[0058] The above description can be summarized as follows. The following summary may include supplementary explanations and explanations of modifications of the above description.

[0059] The vegetation management system 100 has a boundary identification unit 112, a height estimation unit 111, an edge treetop detection unit 113, a crown size estimation unit 114, and a bounding box generation unit 115 (in other words, of the functions 111 to 116 shown in Figure 1, at least the risk analysis unit 116 is not required).

[0060] The boundary identification unit 112 identifies the vegetation boundary in the target area using a semantic segmentation model based on a remote sensing image 202 (typically a satellite image or an aerial photograph image) of the target area and DEM data of the target area. The height estimation unit 111 generates a height map, which is data representing the height of each position in the target area, based on the remote sensing image and the DEM data. The edge treetop detection unit 113 detects edge treetops 1101, which are treetops that satisfy a predetermined height condition and are located within a buffer distance from an asset in the target area, from the height map. The crown size estimation unit 114 estimates the crown size corresponding to the height represented by the height map using a plant allometry model, which is a domain knowledge-based model that takes height as input and crown size as output. The bounding box generation unit 115 estimates the canopy area from the detected canopy size corresponding to the edge treetop 1101, estimates the azimuth angle based on the relationship between the canopy area and the vegetation boundary, and generates a 3D bounding box of the edge tree based on the height of the edge treetop, the detected canopy size corresponding to the edge treetop 1101, and the azimuth angle.

[0061] This will improve the accuracy and robustness of tree crown detection in vegetation management.

[0062] Specifically, it is expected that the accuracy of tree crown detection will be improved because it generates 3D bounding boxes only for edge trees based on domain knowledge, rather than for all trees. More specifically, it is highly interpretable because it uses satellite or aerial imagery and is based on vegetation boundaries and plant allometry models (domain knowledge).

[0063] In addition, the height of the 3D bounding box is the height of the edge tree as the top of the edge tree (the longest vertical distance), so coverage of the edge tree height is guaranteed. Also, risk analysis can be performed using the 3D bounding box, so it is more robust.

[0064] In addition, the crown size estimation unit 114 may estimate the crown size corresponding to the height of each position represented by the height map, or may estimate the crown size corresponding to the height (and position) of only the edge treetops detected by the edge treetop detection unit 113.

[0065] The crown area may be the crown area 1250 as a circle with the edge treetop at the center C and the crown radius based on the crown size. The 3D bounding box of the edge tree may be the 3D bounding box 1300 shown in FIG. 13, that is, a 3D bounding box with a width and length of 2r and a height of h. "r" may be the crown radius. "h" may be the height of the edge treetop 1101. The azimuth angle may be the central angle XCD of the circle (e.g., 90 degrees or less). X may be the intersection point of a straight line along the vertical direction that overlaps the diameter of the crown area 1250 and passes through the center C, and the perimeter of the crown area 1250. D may be the intersection point of a straight line that passes through two intersection points between the vegetation boundary and the perimeter of the crown area, translated so as to pass through the center C, and the perimeter of the crown area. In this way, it is expected that a 3D bounding box suitable for the overall size and orientation of the edge tree will be generated.

[0066] The vegetation management system 100 may include a risk analysis unit 116. The risk analysis unit 116 may place an edge tree box (a 3D bounding box of an edge tree) based on the position of an edge tree, place an asset box (a 3D bounding box of an asset) based on the position of an asset, and estimate the contact risk based on whether the edge tree box may come into contact with the asset box when rotated in a three-dimensional direction. Since the 3D bounding box has a width and a length in addition to a height, an accurate estimation of the contact risk is expected. The risk analysis unit may calculate the probability that the edge tree will collapse and come into contact with the asset based on at least one of meteorological data related to the weather of the target area (e.g., data showing a time series of wind speed in the target area (e.g., forecast data)) and environmental data including data related to the soil of the edge tree (e.g., soil at the same position as the edge tree or in the vicinity thereof), and may use the probability as the basis for estimating the contact risk. This is expected to estimate the contact risk more accurately. The risk analysis unit 116 may identify the rotation angle (rotation angle of the edge tree box) when the edge tree box comes into contact with the asset box, and calculate the probability that the edge tree will collapse to the extent of that rotation angle (the probability that the edge tree will collapse and come into contact with the asset) based on that rotation angle and at least one of the weather data and environmental data. [Explanation of symbols]

[0067] 100: Vegetation management system

Claims

1. a boundary identifying unit that identifies vegetation boundaries in the target area using a semantic segmentation model based on a remote sensing image, which is a satellite image or an aerial photograph image, of the target area and DEM (Digital Elevation Model) data of the target area; a height estimation unit that generates a height map, which is data representing the height of each position in the target area, based on the remote sensing image and the DEM data; an edge treetop detection unit that detects edge treetops, which are treetops that satisfy a predetermined height condition and are located within a buffer distance from an asset in the target area, from the height map; a crown size estimation unit that estimates a crown size corresponding to the height represented by the height map using a plant allometry model that is a domain knowledge-based model in which height is input and crown size is output; a bounding box generation unit that estimates a crown area from the detected crown size corresponding to the edge treetop, estimates an azimuth angle based on a relationship between the crown area and the vegetation boundary, and generates a 3D bounding box of an edge tree that is a tree having the edge treetop, based on the height of the edge treetop, the detected crown size corresponding to the edge treetop, and the azimuth angle; A vegetation management system equipped with

2. a risk analysis unit that places an edge tree box, which is a 3D bounding box of the edge tree, based on the position of the edge treetop, places an asset box, which is a 3D bounding box of the asset, based on the position of the asset, and estimates a contact risk based on whether the edge tree box may come into contact with the asset box when rotated in a three-dimensional direction; The vegetation management system of claim 1 further comprising:

3. the risk analysis unit calculates a probability that the edge tree will collapse and come into contact with the asset based on at least one of meteorological data related to the weather in the target area and environmental data including data related to the soil of the edge tree, and uses the calculated probability as a basis for estimating the contact risk; The vegetation management system of claim 2.

4. The crown area is a circle having a center C at the edge treetop and a crown radius based on the crown size, The 3D bounding box has a width and length of 2r and a height of h, r is the crown radius, h is the height of the edge tree, the azimuth angle is the central angle XCD of the circle, X is the intersection point of a straight line along a vertical direction that overlaps the diameter of the canopy area and passes through the center C with the perimeter of the canopy area; D is an intersection point between a line passing through two intersection points between the vegetation boundary and the perimeter of the canopy area, the line being translated so as to pass through the center C, and the perimeter of the canopy area; The vegetation management system of claim 1 .

5. Identifying vegetation boundaries in the target area using a semantic segmentation model based on a remote sensing image, which is a satellite image or an aerial photograph image, of the target area and DEM (Digital Elevation Model) data of the target area; generating a height map based on the remote sensing image and the DEM data, the height map being data representing the height of each position in the target area; Detecting edge treetops from the height map, which are treetops that satisfy a predetermined height condition and are within a buffer distance from an asset in the target area; A plant allometry model is a domain knowledge-based model that takes height as input and crown size as output, and estimates a crown size corresponding to the height represented by the height map; a crown area is estimated from the detected crown size corresponding to the edge treetop, an azimuth angle is estimated based on a relationship between the crown area and the vegetation boundary, and a 3D bounding box of an edge tree, which is a tree having the edge treetop, is generated based on the height of the edge treetop, the detected crown size corresponding to the edge treetop, and the azimuth angle; The vegetation management method is carried out by a computer.

6. Identifying vegetation boundaries in the target area using a semantic segmentation model based on a remote sensing image, which is a satellite image or an aerial photograph image, of the target area and DEM (Digital Elevation Model) data of the target area; generating a height map based on the remote sensing image and the DEM data, the height map being data representing the height of each position in the target area; Detecting edge treetops from the height map, which are treetops that satisfy a predetermined height condition and are within a buffer distance from an asset in the target area; A plant allometry model is a domain knowledge-based model that takes height as input and crown size as output, and estimates a crown size corresponding to the height represented by the height map; a crown area is estimated from the detected crown size corresponding to the edge treetop, an azimuth angle is estimated based on a relationship between the crown area and the vegetation boundary, and a 3D bounding box of an edge tree, which is a tree having the edge treetop, is generated based on the height of the edge treetop, the detected crown size corresponding to the edge treetop, and the azimuth angle; A computer program that causes a computer to do something.

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