System, information processing method, and recording medium

Abstract

To improve the efficiency of observations using 3D data. [Solution] The system according to the embodiment includes an information processing device and a reference station whose location information is known. The information processing device comprises an acquisition unit, an extraction unit, a matching unit, and an assignment unit. The acquisition unit acquires a reference station image, which is an image including the reference station, to which first location information based on the location information of the reference station is assigned to a first area indicated by the reference station image, and a satellite image taken by a predetermined satellite. The extraction unit extracts feature quantities in the first area and the second area indicated by the satellite image. The matching unit matches the feature quantities between the first area and the second area. The assignment unit assigns second location information corresponding to the first location information to the second area based on the matching result.

Description

Technical Field

[0001] The present invention relates to a system, an information processing method, and a recording medium.

Background Art

[0002] In recent years, observations using three-dimensional data have been carried out.

[0003] For example, in Patent Document 1, a method for accurately detecting an air target from a captured image using the captured image of an air target for measuring a building, earthwork volume, etc. has been proposed.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] There is a demand for improving the efficiency of observations using three-dimensional data.

Means for Solving the Problems

[0006] A system according to an aspect of the present invention is a system including an information processing device and a reference station with known position information, wherein the information processing device includes an acquisition unit that acquires a reference station image that is an image including the reference station and in which first position information based on the position information of the reference station is given to a first area shown in the reference station image, and a satellite image captured by a predetermined satellite; an extraction unit that extracts feature amounts in the first area and a second area shown in the satellite image; a matching unit that matches the feature amounts between the first area and the second area; and an assignment unit that assigns second position information corresponding to the first position information to the second area based on the result of the matching.

Brief Description of the Drawings

[0007] [Figure 1] Figure 1 shows an overview of ground surface observation using SAR interferometry. [Figure 2] Figure 2 is an explanatory diagram illustrating the proposed technology of the present invention. [Figure 3] Figure 3 shows an example of the configuration of an information processing device according to the embodiment. [Figure 4] Figure 4 shows a specific example of feature matching according to the embodiment. [Figure 5] Figure 5 is a flowchart showing the procedure for pre-treatment PR1 according to the embodiment. [Figure 6] Figure 6 is a flowchart showing the procedure for pre-treatment PR2 according to the embodiment. [Figure 7] Figure 7 shows a specific example of adding location information. [Figure 8] Figure 8 is a flowchart showing the steps of the granting process and generation process according to the embodiment. [Figure 9] Figure 9 is a flowchart showing the procedure for the calculation process according to the embodiment. [Figure 10] Figure 10 is a flowchart showing the procedure for the estimation process according to the embodiment. [Figure 11] Figure 11 is a hardware configuration diagram showing an example of a computer that implements the functions of the information processing device according to the embodiment. [Modes for carrying out the invention]

[0008] Embodiments of this disclosure will be described in detail below with reference to the attached drawings. In this specification and the drawings, components having substantially the same functional configuration are denoted by the same reference numerals, and redundant descriptions will be omitted.

[0009] The one or more embodiments (including examples, modifications, and applications) described below can each be implemented independently. On the other hand, at least some of the embodiments described below may be implemented in appropriate combination with at least some of the other embodiments. These embodiments may contain novel features that differ from each other. Therefore, these embodiments may contribute to solving different objectives or problems and may produce different effects.

[0010] Furthermore, in the following embodiments, the events targeted by the proposed technology of the present invention are described as natural disasters that cause changes in topography, particularly sediment-related disasters (landslides). However, the proposed technology of the present invention can be applied to various events, not limited to natural disasters.

[0011] <Embodiment> [1. Introduction] (About SAR) Observations using Synthetic Aperture Radar (SAR) are well known. Unlike passive sensors that utilize electromagnetic energy emitted from the sun, SAR is an active sensor that emits microwaves from its antenna towards the object being observed and receives the electromagnetic energy scattered by the object. Therefore, it can observe regardless of whether it is day or night. Furthermore, because SAR emits microwaves with wavelengths longer than the size of particles in the atmosphere such as clouds and rain, it is less affected by cloud cover and other factors. Thus, SAR has the advantage of being all-weather, allowing observations regardless of the time of day or weather conditions.

[0012] Furthermore, because SAR can capture objects with a spatial resolution unattainable by other observation methods, it is also used to observe sediment-related disasters such as landslides and ground surface deformation caused by subsidence. For example, a satellite-mounted SAR emits microwaves perpendicular to the satellite's direction of travel and obliquely downwards, observes the backscattered waves from the ground surface, and analyzes the observed data to obtain a SAR image with high spatial resolution.

[0013] SAR images store information on the scattering intensity and phase for each pixel. The scattering intensity includes information on the slope, roughness, dielectric constant, etc. of the ground surface, and the phase is information related to the distance from the satellite to the pixel and the scattering pattern, etc. Thus, although a SAR image is an intensity image that includes phase information, it is difficult to observe changes in the ground surface using only one such image.

[0014] Therefore, techniques such as SAR interferometry (InSAR) are known. SAR interferometry is a technique for observing the shape and changes of the ground surface at the observation location by observing the same location multiple times using a satellite-borne SAR. Here, Fig. 1 shows an overview of ground surface observation by SAR interferometry. Fig. 1 shows a scene where two SAR observations are performed on the same location on the ground surface using a SAR satellite 10, which is an artificial satellite equipped with a SAR.

[0015] In this way, by performing two SAR observations to prepare two SAR images (phase images), and performing interference analysis (also called SAR analysis or interferometry) by interfering them and taking the difference, it becomes possible to utilize information on a slight distance difference Δd, and it becomes possible to capture fluctuations in the ground surface. Also, the analysis result by interference analysis is generally represented by an interference image (interferogram) showing the fluctuations as rainbow-colored stripes. According to the example of Fig. 1, the interference image shows an interference image having interference stripes such as terrain stripes and orbital stripes by interfering the first observation SAR image and the second observation SAR image. From this, in SAR interferometry, for example, by performing interference analysis using two observation data acquired before and after an earthquake, it becomes possible to visualize crustal movements associated with the earthquake.

[0016] There are several processing levels in SAR images, and the product that is the data immediately after imaging and serves as the basis for subsequent processing is also called SLC (Single Look Complex) data. SLC data is a SAR image in the state with the highest resolution, and in addition to the amplitude information that can be recognized as an image, it includes phase information, etc.

[0017] For example, in interferometric analysis, a differential interferogram is generated using the SLC data obtained in the first observation and the SLC data obtained in the second observation according to the following procedure. Specifically, the procedure includes alignment of the SLC data obtained in the first observation and the SLC data obtained in the second observation (step 1), performing an interference process to generate an interferogram from the two SLC data (step 2), and generating a differential interferogram by removing interference fringes from the interferogram (step 3).

[0018] Here, in the alignment of step 1, based on the SLC data obtained in the first observation and the SLC data obtained in the second observation, the difference in corresponding pixel coordinates is measured at the sub-pixel level, and a conversion coefficient for calculating the pixel position of the SLC data obtained in the second observation corresponding to the pixel of the SLC data obtained in the first observation is obtained. Based on this conversion coefficient, the alignment of these two SLC data is performed. In such alignment, one of the problems of the present invention is that a laborious operation such as manually overlaying landmarks occurs. The problems of the present invention will be specifically described below.

[0019] (Problem) Quick response is required in the event of a disaster. By comparing three-dimensional point cloud data (hereinafter referred to as "3D point cloud data") acquired by a laser scanner, for example, damaged areas and normal areas can be discriminated, and usefulness in measures and rescue by local governments has been found. However, means for generating high-precision 3D point cloud data for the entire country (for example, the entire country of Japan) are limited, and rapid generation of high-precision 3D point cloud data in a wide area is required. However, the current method is problematic in that it takes time and cost.

[0020] Therefore, the inventors of this invention focused on the possibility that high-precision 3D point cloud data could be generated using SAR images as source images by utilizing SAR, which is all-weather, independent of time of day and weather conditions, and capable of capturing high-resolution images. Furthermore, SAR missions capable of observation at higher intervals are now being realized, and multiple SAR satellites 10 are being operated simultaneously. As a result, it is possible to observe the same location multiple times a day using multiple SAR satellites 10, and SAR is considered useful in terms of accelerating disaster response.

[0021] On the other hand, when generating 3D point cloud data from SAR images, alignment of the SAR images is performed, but as mentioned above, this alignment requires manual work, which is time-consuming and costly. This is because SAR images do not have absolute coordinates, making it difficult to determine their position. For example, while relative coordinate changes can be monitored from phase changes on different time axes in SAR images, the SAR images themselves do not have absolute coordinates. For instance, the management company of SAR satellite 10 performs the laborious task of overlaying SAR images to represent coordinates based on the positions of landmarks and other elements contained within the SAR images.

[0022] In this regard, the inventors of the present invention have found that absolute coordinates can be assigned to SAR images by combining RTK (Real Time Kinematic) positioning technology, which can determine positional information with high accuracy of an error of a few centimeters, with SAR observation technology.

[0023] (Summary of proposed technology) Based on the above, the proposed technology of the present invention extracts feature quantities of the captured area (for example, feature quantities of objects included in the area) from a reference station image that has absolute coordinates based on the position coordinates of a reference station used for RTK positioning, and from a SAR image that does not have absolute coordinates. Then, absolute coordinates are added to the SAR image by feature matching using the extracted feature quantities. As a result, the proposed technology of the present invention enables position identification of the SAR image, and 3D point cloud data is generated by combining multiple SAR images to which absolute coordinates have been added.

[0024] Furthermore, the feature matching algorithm may utilize a predetermined deep learning-based machine learning model, and for 3D point cloud data generation, photogrammetry techniques that generate orthomosaic images by superimposing multiple SAR images may be employed.

[0025] [2. Overview of the proposed technology] Here, we will explain the overall picture of the proposed technology of the present invention using Figure 2. Figure 2 is an explanatory diagram illustrating the proposed technology of the present invention. Figure 2 shows a system Sy in which the information processing related to the proposed technology of the present invention is realized. System Sy may include a SAR observation system 1 that performs SAR observation using SAR mounted on a SAR satellite 10, and an RTK positioning system 2. The mechanism of SAR observation in the SAR observation system 1 is as described above, so we will omit the explanation.

[0026] The RTK positioning system 2 may include a receiving device 20, a reference station 30, a GNSS (Global Navigation Satellite System) satellite 40, and a positioning core system 50.

[0027] The receiving device 20 may be a portable information processing terminal and can be installed, for example, at any location where a user wishes to know their location. The receiving device 20 may receive GNSS signals from GNSS satellites 40. That is, the receiving device 20 may be equipped with a GNSS module (positioning module) and antenna corresponding to a GNSS receiver in RTK, for example. The receiving device 20 may also be equipped with a communication module for communicating with the positioning core system 50.

[0028] Reference station 30 may function as a reference station in RTK calculations. That is, reference station 30 may have known coordinates that indicate its own position. Also, if there are multiple reference stations 30, each of the multiple reference stations 30 may have known coordinates that are defined for it. Reference station 30 may receive GNSS signals from GNSS satellite 40.

[0029] In RTK positioning, first, the receiving device 20 and multiple reference stations 30 whose precise position information (absolute coordinates) is known receive GNSS signals from the GNSS satellite 40 and each acquires its own position information based on the received GNSS signals. Next, the receiving device 20, which is trying to determine its position, transmits its approximate position information (hereinafter referred to as approximate position information) obtained based on the GNSS signals to the positioning core system 50. Then, based on the transmitted approximate position information, the positioning core system 50 acquires the position information of the multiple reference stations 30 located near the receiving device 20 in real time, which is obtained based on the GNSS signals of those reference stations 30.

[0030] Next, the positioning core system 50 generates correction information by calculating the difference between known, precise position information of the reference station 30 (for example, pre-stored in the positioning core system 50) and position information obtained from the reference station 30 based on the GNSS signal. This correction information is used to correct the approximate position information of the receiving device 20 obtained based on the GNSS signal in real time. In other words, in RTK positioning, the approximate position information of the receiving device 20 obtained based on the GNSS signal is corrected using correction information generated from the position information of the reference station 30 (position information obtained based on the GNSS signal, precise position information) installed in a receiving environment that is estimated to be close to the GNSS signal receiving environment of the receiving device 20 whose position is to be determined.

[0031] With this type of RTK positioning, by performing the corrections described above, it is possible to correct errors caused by ionospheric disturbances, multipath, and clock drift in GNSS signals, thereby reducing the error included in the position information to a few centimeters. As a result, RTK positioning makes it easy to acquire position information of the receiving device 20 with minimal error.

[0032] In the RTK positioning that can be used in this embodiment, it is preferable that the multiple reference stations 30 are set up so that the distance between them is within 10 kilometers.

[0033] Furthermore, in RTK positioning, the positioning core system 50 or the receiving device 20 uses correction information generated by the positioning core system 50 to correct the approximate position information obtained based on the GNSS signal, thereby obtaining position information of the receiving device 20 with less error. More specifically, RTK positioning can be mainly divided into two methods: a device RTK positioning method in which the above correction is performed on the receiving device 20 side, and a server RTK positioning method in which the above correction is performed on the positioning core system 50.

[0034] In the information processing according to the embodiment implemented in System Sy, known and accurate location information is used for each of the multiple reference stations 30.

[0035] Here, the reference station image BG, which includes the reference station 30, contains the location information of the reference station 30. As described above, this location information of the reference station 30 is precise location information predetermined for the reference station 30, and is absolute location information (absolute coordinates) used to identify the location of the area AR indicated by the reference station image BG. The reference station image BG may be a full-color image of the ground taken by a predetermined flying object (e.g., an optical satellite or drone), and includes the reference station 30. The number of reference stations 30 included in the reference station image BG is not limited.

[0036] In this state, the information processing according to the embodiment assigns first position information to the reference station image BG, based on the precise position information of the reference station 30. Specifically, the information processing according to the embodiment assigns first position information to the area AR indicated by the reference station image BG (specifically, objects around the reference station 30 that exist within the area AR), based on the precise position information of the reference station 30. The first position information can be described as absolute position information (absolute coordinates) based on the precise position information of the reference station 30.

[0037] Furthermore, the SAR satellite 10 takes images while orbiting along a specific orbit. Therefore, sequential SAR observations corresponding to the orbit allow for the acquisition of a SAR image SG covering the entire country (for example, all of Japan), similar to the reference station image BG. The SAR image SG itself does not have absolute coordinates, but absolute coordinates are assigned to it by information processing according to the embodiment.

[0038] Specifically, feature quantities F are extracted from area AR indicated by the reference station image BG (hereinafter referred to as "Area AR1") and area AR indicated by the SAR image SG (hereinafter referred to as "Area AR2"). For example, feature quantities F are extracted for objects included in Area AR1 and for objects included in Area AR2.

[0039] Next, in the information processing according to the embodiment, a feature matching process is performed to match feature quantities F between area AR1 and area AR2, specifically matching feature quantities F between objects contained in area AR1 and objects contained in area AR2. Based on the matching result, second location information corresponding to the first location information is assigned to the SAR image SG. Specifically, the second location information corresponding to the first location information is assigned to area AR2 indicated by the SAR image SG (specifically, objects located within area AR2). The second location information can be described as the absolute location information (absolute coordinates) of each object located within area AR2.

[0040] Here, for example, by observing the same location multiple times, it is possible to obtain multiple SAR images SG of the same location, each with a second position information attached. By combining these multiple SAR images SG with the second position information attached, 3D point cloud data DA can be generated. For example, in the information processing according to the embodiment, a method may be employed in which a photogrammetry technique is used to superimpose multiple SAR images SG with the second position information attached to generate an orthomosaic image, and then 3D point cloud data DA is obtained from the orthomosaic image.

[0041] For example, by comparing 3D point cloud data DA1, which is a combination of multiple SAR images SG obtained during the first period T1, with 3D point cloud data DA2, which is a combination of multiple SAR images SG obtained during the second period T2, which is after the first period T1, it becomes possible to capture ground surface displacement.

[0042] In the information processing according to this embodiment, for example, the volume of sediment may be calculated based on 3D point cloud data DA1 and 3D point cloud data DA2. For example, the relative positions of 3D point cloud data DA1 and 3D point cloud data DA2 can be matched, and based on the matching result, the difference between the terrain in the first period T1 and the terrain in the second period T2 can be calculated, and the sediment portion can be detected based on the difference. Once the sediment portion can be detected in this way, the volume of sediment can be calculated from the 3D point cloud data DA of the sediment portion.

[0043] The information processing according to the embodiment described in Figure 2 may be performed by the information processing device 100. Therefore, the information processing device 100 enables more efficient observation using 3D point cloud data. For example, since the information processing device 100 is introduced into a system Sy that includes a SAR observation system 1 and an RTK positioning system 2, it can perform information processing that combines RTK positioning technology and SAR observation technology, enabling the rapid generation of high-precision 3D point cloud data over a wide area without incurring significant time and cost. As a result, the information processing device 100 can support a rapid response in the event of a disaster. For example, if a landslide occurs due to an earthquake, the information processing device 100 can estimate the type and number of equipment required for removing the sediment based on the volume of the sediment and notify the local government of the estimation results.

[0044] [3. Configuration of the Information Processing Device] An information processing device 100 according to an embodiment will be described using Figure 3. Figure 3 is a diagram showing an example configuration of the information processing device 100 according to an embodiment. As shown in Figure 3, the information processing device 100 may have a communication unit 110, a storage unit 120, and a control unit 130. Note that the information processing device 100 may be a server device implemented in the cloud.

[0045] (Communications Department 110) The communication unit 110 is implemented, for example, by a NIC (Network Interface Card). For example, the communication unit 110 transmits and receives information between the SAR observation system 1 and the RTK positioning system 2. The communication unit 110 may also transmit and receive information between the user U's terminal device DV (not shown). The user U here may be, for example, an employee of a local government in a disaster-stricken area.

[0046] (Storage unit 120) The storage unit 120 is implemented by, for example, a semiconductor memory element such as RAM (Random Access Memory) or flash memory, or a storage device such as a hard disk or optical disc. The storage unit 120 may store, for example, data and programs related to the information processing according to the embodiment. The storage unit 120 may also store data necessary for the information processing according to the embodiment, or data obtained from the information processing according to the embodiment.

[0047] (Control unit 130) The control unit 130 is implemented by a CPU (Central Processing Unit) or MPU (Micro Processing Unit), etc., which executes various programs (for example, information processing programs according to the embodiment) stored in the memory device inside the information processing device 100 using RAM as the working area. Alternatively, the control unit 130 can be implemented by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array).

[0048] As shown in Figure 3, the control unit 130 includes an acquisition unit 131, an extraction unit 132, a matching unit 133, an assignment unit 134, a generation unit 135, a calculation unit 136, an estimation unit 137, and a notification unit 138, and realizes or executes the information processing functions and operations described below. Note that the internal configuration of the control unit 130 is not limited to the configuration shown in Figure 3, and other configurations are also possible as long as they perform the information processing described later. Also, the connection relationships of the various processing units in the control unit 130 are not limited to the connection relationships shown in Figure 3, and other connection relationships are also possible.

[0049] (Acquisition part 131) The acquisition unit 131 may acquire a reference station image BG, which is an image including the reference station 30. Specifically, the acquisition unit 131 may acquire a reference station image BG to which first position information based on the precise position information (absolute coordinates) of the reference station 30 is attached to area AR1, which is the first area indicated by the reference station image BG. The acquisition unit 131 may also acquire a SAR image SG obtained from SAR observations by the SAR satellite 10.

[0050] Furthermore, the SAR image acquired by the acquisition unit 131 does not necessarily have to include the reference station 30. According to the information processing according to this embodiment, feature matching is performed between the reference station image BG, to which first position information based on accurate position information (absolute coordinates) is assigned, and the SAR image SG. Therefore, even if the SAR image does not include the reference station 30, absolute coordinates can be assigned to the SAR image.

[0051] (Extraction part 132) The extraction unit 132 extracts feature quantities in area AR1 and area AR2, which is a second area indicated by the SAR image. Here, the assignment unit 134, described later, may assign first position information to objects included in area AR1 as objects around the reference station 30, based on the precise position information of the reference station 30. As a result, the extraction unit 132 may extract feature quantities of objects included in area AR1 based on the first position information.

[0052] Furthermore, the extraction unit 132 may extract feature quantities of objects included in area AR2 based on the SAR image. For example, the extraction unit 132 may use any feature extraction algorithm to extract feature quantities that characterize objects included in the SAR image SG.

[0053] (Matching section 133) The matching unit 133 may match feature quantities F between area AR1 and area AR2. For example, the matching unit 133 may match feature quantities F between objects contained in area AR1 and objects contained in area AR2. Algorithms such as SIFT (Scale-Invariant Feature Transform), SURF (Speeded Up Robust Features), ORB (Oriented FAST and Rotated BRIEF), BRISK (Binary Robust Invariant Scalable Keypoints), FREAK (Fast Retina Keypoint), and SuperGlue may be used for feature matching.

[0054] (Granting section 134) The assignment unit 134 may assign location information to the image. Specifically, the assignment unit 134 may assign location information to area AR1 indicated by the base station image BG and area AR2 indicated by the SAR image SG.

[0055] For example, the assignment unit 134 may assign first location information to objects included in area AR1 as objects in the vicinity of the reference station 30, based on the precise location information of the reference station 30. Furthermore, the assignment unit 134 may assign second location information to area AR2 based on the matching results obtained by feature matching, corresponding to the first location information. For example, the assignment unit 134 may assign absolute location information to each object included in area AR2 as second location information corresponding to the first location information.

[0056] (Generation unit 135) The generation unit 135 may generate 3D point cloud data DA by combining pre-assigned SAR image SGs, which are SAR image SGs to which second position information has been assigned to area AR2. For example, the generation unit 135 may generate 3D point cloud data DA by combining pre-assigned SAR image SGs that correspond to SAR image SGs taken at different times, where at least a portion of area AR2 overlaps. More specifically, the generation unit 135 may repeat the process of generating 3D point cloud data DA at predetermined intervals by combining pre-assigned SAR image SGs for a predetermined period when a predetermined period's worth of pre-assigned SAR image SGs have been accumulated.

[0057] The generation unit 135 may also perform a process to update the previously generated 3D group data DA using the SAR image SG that has been assigned this time.

[0058] (Calculation section 136) The matching unit 133 may further perform a process to match the relative positional relationship between the first 3D point cloud data DA1, which is generated by combining the assigned SAR images SG for the first period T1, and the second 3D point cloud data DA2, which is generated by combining the assigned SAR images SG for the second period T2, which is after the first period T1. Based on the matching result, the calculation unit 136 may calculate the volume of the portion that has changed between the terrain in the first period T1 and the terrain in the second period T2.

[0059] (Estimation Department 137) The estimation unit 137 may estimate the type and number of equipment required for removal. Specifically, the estimation unit 137 may estimate the type and number of equipment required for soil removal based on the volume (soil volume) calculated by the calculation unit 136.

[0060] For example, the estimation unit 137 may estimate the type and number of equipment required for soil removal in a rule-based manner. Specifically, if the type and number of equipment required for soil removal are defined as estimation rules for each volume of soil, the estimation unit 137 may estimate the type and number of equipment required for soil removal by comparing the current volume of soil with the estimation rules.

[0061] As another example, the estimation unit 137 may estimate the type and number of equipment required for soil removal based on a machine learning model. In this case, the machine learning model may be trained to output the type and number of equipment required for soil removal when the volume of soil is input.

[0062] (Regarding Notification Section 138) The notification unit 138 may notify the user U of information regarding the event that occurred when it is determined that an event has occurred. For example, if the notification unit 138 determines that a landslide has occurred, it may notify the user U of alert information that includes at least information indicating the occurrence of a landslide and information about the area where the landslide occurred.

[0063] Furthermore, the notification unit 138 may notify the user U of information regarding the volume of sediment, the geographical area newly covered by sediment due to the sediment disaster (sediment disaster area), and the types and number of equipment necessary for sediment removal.

[0064] [4. Specific Examples of Feature Matching] Figure 4 shows a specific example of feature matching according to the embodiment. Before explaining feature matching, Figure 4(a) will be used to explain wide-area observation by the SAR satellite 10.

[0065] SAR satellite 10 orbits the Earth, and its orbit includes a path descending from north to south (southern orbit) and a path ascending from south to north (northern orbit). When performing feature matching between the reference station image BG and the SAR image SG, it is preferable that the observation ranges of the reference station image BG and the SAR image SG are the same or partially overlap.

[0066] Figure 4(a) shows SAR satellite 10 performing SAR observations of the ground L while moving along a single orbit TR.

[0067] As shown in the example in Figure 4(a), the SAR satellite 10 observes areas R1, R2, R3, and R4 by performing wide-area observations from position P1 on the orbit TR. As a result, the information processing device 100 can obtain SAR images SG taken from each of the areas R1, R2, R3, and R4.

[0068] Furthermore, as shown in the example in Figure 4(a), the SAR satellite 10 observes areas R5, R6, R7, and R8 by performing wide-area observations from position P2 on the orbit TR. As a result, the information processing device 100 can obtain SAR images SG taken from areas R5, R6, R7, and R8, respectively.

[0069] Furthermore, as shown in the example in Figure 4(a), the SAR satellite 10 observes areas R9, R10, R11, and R12 by performing wide-area observations from position P3 on the orbit TR. As a result, the information processing device 100 can obtain SAR images SG taken from areas R9, R10, R11, and R12, respectively.

[0070] Furthermore, since the SAR satellite 10 orbits the Earth along its orbit TR and observes the same observation range at predetermined intervals, the information processing device 100 can sequentially acquire SAR images SG of range R1 at a rate of, for example, 6 images per hour (144 images per 24 hours). The same applies to ranges R2 to R12.

[0071] Next, a specific example of feature matching according to the embodiment will be explained using Figure 4(b). Figure 4(b) shows a scenario in which feature matching is performed between a reference station image BG1 and a SAR image SG1, both of which share the same observation range R1. Thus, area AR1 shown in the reference station image BG1 corresponds to the area R1. Similarly, area AR2 shown in the SAR image SG1 also corresponds to the area R1.

[0072] Furthermore, according to the example in Figure 4(b), the reference station image BG1 includes a reference station 31 (an example of a reference station 30) installed in building ST1 (an example of building ST). Similarly, the SAR image SG1 also includes a reference station 31 installed in building ST1. In the example in Figure 4(b), it is assumed that first position information, based on the precise position information of reference station 31, is assigned to the location of building ST1 in the reference station image BG1. According to this example, the first position information is the absolute coordinates of building ST1. Therefore, in the example in Figure 4(b), the reference station image BG1 has absolute coordinates. On the other hand, the SAR image SG1 does not have absolute coordinates at the time it was acquired. Specifically, the SAR image SG1 does not have absolute coordinates, which are the absolute position information of building ST1.

[0073] Therefore, the extraction unit 132 may extract feature quantities F1 of objects included in area AR1 (for example, various objects including building ST1) from the reference station image BG1. For example, the extraction unit 132 may extract feature quantities F1 of objects included in area AR1 based on first location information. The extraction unit 132 may also extract feature quantities F2 of objects included in area AR2 (for example, various objects including building ST1) from the SAR image SG1. For example, the extraction unit 132 may extract feature quantities F2 of objects included in area AR2 by applying an arbitrary feature extraction algorithm to the SAR image SG1.

[0074] In this state, the matching unit 133 may match the feature quantities F between the objects included in the reference station image BG1 and the objects included in the SAR image SG1. Specifically, the matching unit 133 may match the feature quantity F1 of the objects included in area AR1 with the feature quantity F2 of the objects included in area AR2. As described above, algorithms such as SIFT may be used for feature matching.

[0075] When generating 3D point cloud data, it is preferable that the SAR images SG to be combined are taken from the same orbit (same path number), have the same or partially overlapping observation ranges, and use the same radio frequency band. For example, even if the observation ranges are the same, the position of the SAR satellite 10 when this observation range was photographed may differ, and SAR images SG taken from different positions will appear differently because they are taken in different directions. By combining not only SAR images SG that appear the same, but also SAR images SG that appear differently, it is possible to generate 3D point cloud data with higher accuracy.

[0076] [5. Processing Procedure (1)] The information processing according to this embodiment is divided into three parts: pre-processing for assigning absolute coordinates to SAR images SG, assignment processing in which absolute coordinates are assigned to SAR images SG based on the results of pre-processing, and generation processing in which 3D point cloud data is generated by combining SAR images SG that have absolute coordinates.

[0077] (5-1. Pre-processing procedure (1)) Figure 5 is a flowchart showing the procedure for pre-processing PR1 according to the embodiment. Pre-processing PR1 may be a process that uses the precise location information of the reference station 30 to transfer location information to the building ST.

[0078] As described above, each of the 30 reference stations in each region has predetermined precise location information. Therefore, the acquisition unit 131 may acquire the precise location information of each of the 30 reference stations (step S501). For example, the acquisition unit 131 may acquire the precise location information of each of the 30 reference stations from the accession core system 50.

[0079] Furthermore, the acquisition unit 131 may acquire an installation drawing that shows information about the installation location of each reference station 30 (step S502). It is preferable that the reference stations 30 be installed in a location where there are no obstacles that create blind spots and radio waves can be easily received. For this reason, reference stations 30 are often installed on the rooftop of a building, etc. For this reason, the installation drawing may include, for example, structural information of the building ST on which the reference station 30 is installed. For example, if the building ST is a cubic building, the structural information may include information on the length, width, and height of the building ST.

[0080] The assignment unit 134 may calculate the location information LI1 of a predetermined object based on the precise location information of the reference station 30 and the installation drawing (step S503). For example, the assignment unit 134 may calculate the location information LI1 of the structure ST on which the reference station 30 is installed, and link the calculated location information LI1 to the installation drawing as a variable (step S503). Specifically, the assignment unit 134 may calculate the location information LI1 of the structure ST as first location information based on the precise location information of the reference station 30.

[0081] (5-2. Pre-processing procedure (2)) Figure 6 is a flowchart showing the procedure for preprocessing PR2 according to the embodiment. Preprocessing PR2 may be a process that extracts feature quantities of objects included in the reference station image BG based on the position information LI1 of the structure ST.

[0082] The acquisition unit 131 may acquire a reference station image BS (step S601). Multiple reference station image BS may exist depending on the area where the reference station 30 is installed. Multiple reference station image BS may exist depending on the observation range of the SAR satellite 10. In addition, the reference station image BS may be a full-color optical image of the ground L taken from above.

[0083] The assignment unit 134 may determine whether or not there are any base station image BGs that do not have location information assigned to them (step S602).

[0084] If there is a reference station image BG without location information (step S602; Yes), the assignment unit 134 may extract one reference station image BG without location information and identify the structure ST included in the extracted reference station image BG (step S603). The structure ST may be an object on which the reference station 30 is installed, and the structure ST can be identified based on the installation drawing corresponding to the reference station 30.

[0085] Next, the assignment unit 134 may assign location information LI1 to the location of the structure ST (structure ST identified in step S603) on area AR1 indicated by the extracted unassigned reference station image BG (step S604). The location information LI1 assigned here corresponds to first location information (absolute coordinates) based on the precise location information of the reference station 30 included in the reference station image BG.

[0086] The extraction unit 132 may extract the feature quantity F1 of the structure ST based on the location information LI1 assigned in step S604 (step S605).

[0087] Then, the extraction unit 132 may associate the feature quantity F1 of the structure ST (structure ST identified in step S603) at the location of the structure ST on area AR1 indicated by the extracted unassigned reference station image BG (step S606).

[0088] Furthermore, if there are no items for which location information has not yet been assigned (step S602; No), the assignment unit 134 may terminate the process.

[0089] Here, Figure 7 shows a specific example in which location information LI1 is added to the base station image BG by the preprocessing (PR1, PR2) described in Figures 5 and 6. In Figure 7, a specific example of adding location information LI1 is explained using an enlarged portion of the base station image BG1 shown in Figure 4(b). The base station image BG1 shown in Figure 7 includes a base station 31 (an example of a base station 30) and a building ST1 (an example of a building ST) on which the base station 31 is installed.

[0090] Furthermore, as shown in the example in Figure 7, the precise location information of the reference station 31 is assumed to be absolute coordinates (X0, Y0, Z0). In this state, the assignment unit 134 may calculate the location information LI1 of the structure ST1 in step S503 based on the absolute coordinates (X0, Y0, Z0) and an installation drawing that includes the structural information of the building ST1. For example, the assignment unit 134 may calculate the location information LI1 of the structure ST1 based on the absolute coordinates (X0, Y0, Z0) and the information of the length, width, and height of the building ST.

[0091] Here, in step S602, the assignment unit 134 extracts the base station image BG1 as a base station image BG without location information assigned. In step S603, the assignment unit 134 determines that the structure ST on which the base station 31 included in the base station image BG1 is installed is structure ST1.

[0092] As a result, in step S604, the assignment unit 134 assigns the location information LI1 of structure ST1 to the location of structure ST1 on area AR1 indicated by the reference station image BG1. Figure 7 shows an example in which the assignment unit 134 assigns absolute coordinates (X1,Y1,Z1), absolute coordinates (X2,Y2,Z2), absolute coordinates (X3,Y3,Z3), and absolute coordinates (X4,Y4,Z4) as the location information LI1 of structure ST1, based on the absolute coordinates (X0,Y0,Z0) of the reference station 31.

[0093] Furthermore, in step S605, the extraction unit 132 extracts the feature quantity F1 of the structure ST1 based on absolute coordinates (X1, Y1, Z1), absolute coordinates (X2, Y2, Z2), absolute coordinates (X3, Y3, Z3), and absolute coordinates (X4, Y4, Z4).

[0094] Figure 7 shows an example in which the assignment unit 134 assigns location information LI1 of the building ST1 where the reference station 31 is installed to the reference station image BG1, and the extraction unit 132 extracts feature quantities F1 of the building ST1 based on the location information LI1.

[0095] However, the reference station image BG1 includes various objects other than the building ST1 (for example, other buildings, parking lots, roads, etc.). Therefore, the assignment unit 134 may assign location information LI1 to objects other than the building ST1 on which the reference station 31 is installed, and the extraction unit 132 may extract feature quantities F1 for the other objects as well. The assignment unit 134 can calculate the location information LI1 for each object around the building ST1 based on the positional relationship between the building ST1 on which the reference station 31 is installed and the objects around the building ST1 (which can be determined from the installation drawing), and the location information LI1 of the building ST1.

[0096] Furthermore, Figure 7 shows an example in which a single reference station image BG, called reference station image SG1, is assigned location information LI1 (first location information based on the precise location information of the reference station 30) for objects contained in reference station image SG1.

[0097] However, the assignment unit 134 may combine multiple reference station image backgrounds (BGs) and assign the location information LI1 of the objects contained in each of the multiple reference station image backgrounds to each of the multiple reference station image backgrounds. For example, the assignment unit 134 may calculate the location information LI1 of the building ST (object on which the reference station 30 is installed) contained in each reference station image background 30 based on the accurate location information of the reference stations 30 contained in each reference station image background 30 in which at least a portion of the area AR overlaps. Then, the assignment unit 134 may further calculate the location information LI1 of objects existing between the building STs based on the location information LI1 calculated for each building ST contained in each reference station image background 30.

[0098] (5-3. Procedure for granting and generating data) Figure 8 is a flowchart showing the procedures for the assignment and generation processes according to the embodiment. The assignment and generation processes according to the embodiment may be executed in real time after the preprocessing (PR1, PR2) described in Figures 5 and 6 has been completed.

[0099] The acquisition unit 131 may determine whether or not it is time for processing (step S801). In this embodiment, the timing for processing is assumed to be when the day of the week changes. If it is not time for processing (step S801; No), the acquisition unit 131 waits until it is time for processing.

[0100] Here, as explained in Figure 4(a), the SAR satellite 10 may orbit the Earth along its orbit TR, observing the same observation range at predetermined intervals, and transmitting the observed SAR image SR to the information processing device 100 each time it is captured. For this reason, the acquisition unit 131 may sequentially acquire SAR images SG for each observation range by sequential SAR observations corresponding to the orbit of the SAR satellite 10. The processing described below will be assumed to be for range R1 (Figure 4(a)) of the observation range, but similar processing will be performed for other observation ranges as well.

[0101] When it is time to process (step S801; Yes), the acquisition unit 131 determines whether or not it was able to acquire the SAR image SG (SAR image SG obtained by observing range R1) from the SAR satellite 10 (step S802).

[0102] If the acquisition unit 131 has not been able to acquire the SAR image SG (step S802; No), it waits until it can acquire the SAR image SG.

[0103] On the other hand, if a SAR image SG is acquired (step S802; Yes), the extraction unit 132 may extract feature quantities F2 of the objects contained in the acquired SAR image SG (abbreviated as "the current SAR image SG") (step S803). For example, the extraction unit 132 may extract feature quantities that characterize the objects contained in the current SAR image SG by applying an arbitrary feature extraction algorithm to the current SAR image SG.

[0104] Next, the matching unit 133 may determine the information of area AR2 indicated by the SAR image SG based on the approximate positional information contained in the SAR image SG (step S804). For example, the matching unit 133 may determine that area AR2 indicated by the SAR image SG is data resulting from observation of range R1.

[0105] Therefore, the matching unit 133 may extract a reference station image BG in which the range R1 has been captured (step S805). For example, the matching unit 133 may extract a reference station image BG in which at least a part of the range R1 has been captured. This reference station image BG is data to which first position information has been added by preprocessing (PR1, PR2) and from which feature quantity F1 has been extracted.

[0106] The matching unit 133 may perform feature matching between the extracted reference station image BG and the current SAR image SG (step S806). Specifically, the matching unit 133 may match the feature quantity F1 of each object included in area AR1 shown in the extracted reference station image BG with the feature quantity F2 of each object included in area AR2 shown in the current SAR image SG.

[0107] Next, the assignment unit 134 may, based on the matching results, assign location information LI2 to the location of an object (an object from which feature quantity F2 has been extracted) on area AR2 indicated by the current SAR image SG (step S807). The location information LI2 assigned here is second location information corresponding to location information LI1 (first location information), and can therefore be said to be absolute location information based on the precise location information of the reference station 30.

[0108] Furthermore, the assignment unit 134 may store the assigned SAR image SG, from which the feature quantity F2 has been extracted and to which the location information LI2 has been assigned, in the storage unit 120 (step S808). The storage unit 120 is a recording medium for recording the assigned SAR image SG and may be controlled to provide the assigned SAR image SG in response to external access. As a result, for example, user U will be able to analyze crustal deformation using the assigned SAR image SG.

[0109] Here, the granting unit 134 may determine whether a predetermined period T has elapsed since it was determined in step S801 that it was time to process (step S809). In this embodiment, the predetermined period T is assumed to be 24 hours.

[0110] If the predetermined period T has not elapsed (step S809; No), the granting unit 134 may return to step S802 so that the processing from step S802 is repeated.

[0111] On the other hand, if a predetermined period T has elapsed (step S809; Yes), the generation unit 135 may acquire the SAR images SG obtained during the predetermined period T (SAR images SG obtained for the predetermined period T) (step S810).

[0112] The generation unit 135 may then generate 3D point cloud data DA by combining SAR images SG that have been assigned for a predetermined period T (step S811).

[0113] It is possible to generate 3D point cloud data DA by combining multiple reference station image SGs. However, in situations where a rapid response is required when a disaster occurs, for example, if a drone is flown after a disaster occurs to collect reference station image SGs as aerial images from various locations, and then 3D point cloud data DA is generated from the collected reference station image SGs, the response would be delayed. On the other hand, the proposed technology of the present invention has the advantage of being able to obtain high-resolution SAR image SGs over a wide area and at a high frequency, regardless of conditions such as time of day or weather, and since the data for assigning absolute coordinates is available through preprocessing, it can be said that it is more suitable for the need for a rapid response when a disaster occurs compared to generating 3D point cloud data DA using reference station image SGs.

[0114] Here, as shown in the example in Figure 8, the information processing device 100 stores the applied SAR images SG obtained sequentially over a predetermined period T (for example, 1 day), and when the predetermined period T has elapsed, it combines the applied SAR images SG for the predetermined period T to generate 3D point cloud data DA, repeating this process every predetermined period T (for example, every day). As a result, the information processing device 100 can detect changes in terrain and determine the volume of the changed portion as the volume of soil and sediment based on, for example, the first 3D point cloud data DA1, which is a combination of multiple SAR images SG obtained during a first predetermined period T1 (for example, September 20, 2024), and the second 3D point cloud data DA2, which is a combination of multiple SAR images SG obtained during a second predetermined period T2 (for example, September 21, 2024).

[0115] Furthermore, instead of generating 3D point cloud data DA by combining the SAR images SG that have been applied for a predetermined period T once that period T has elapsed, the information processing device 100 may perform a process in which it updates the 3D point cloud data DA generated so far using the SAR images SG that have been applied sequentially during the predetermined period T.

[0116] [6. Processing Procedure (2)] The information processing according to the embodiment may further include a calculation process for calculating the volume of sediment. Figure 9 is a flowchart showing the procedure for the calculation process according to the embodiment. Figure 9 shows the procedure for calculating the volume of sediment based on first 3D point cloud data DA1 generated from multiple SAR images SG for a first period T1 and second 3D point cloud data DA2 generated from multiple SAR images SG for a second period T2. This calculation process may be performed, for example, if an earthquake occurs at some point during the second period T2.

[0117] As shown in the example in Figure 9, first the acquisition unit 131 may acquire the first 3D point cloud data DA1 and the second 3D point cloud data DA2 (step S901).

[0118] The matching unit 133 may match the relative positional relationship between the first 3D point cloud data DA1 and the second 3D point cloud data DA2 (step S902).

[0119] The calculation unit 136 may calculate the difference between the terrain in the first period T1 and the terrain in the second period T2 based on the results of the relative positional relationship matching (step S903).

[0120] Furthermore, the calculation unit 136 may detect the sediment portion based on the difference calculated in step S903 (step S904). For example, the calculation unit 136 may detect the portion corresponding to the difference (i.e., the portion that has changed between the topography in the first period and the topography in the second period) as the sediment portion.

[0121] The calculation unit 136 may then calculate the volume of soil corresponding to the soil portion based on the 3D point cloud data corresponding to the soil portion (step S905).

[0122] Furthermore, the calculation unit 136 may further detect whether or not a landslide has occurred based on the calculated volume of sediment. For example, the calculation unit 136 may determine whether or not the volume of sediment calculated in step S905 exceeds a threshold (step S906). If the volume of sediment does not exceed the threshold (step S906; No), the process may be terminated.

[0123] On the other hand, the calculation unit 136 may detect that a landslide has occurred if the volume of sediment calculated in step S905 exceeds a threshold (step S906; Yes).

[0124] [7. Processing Procedure (3)] The information processing according to the embodiment may further include estimation processing based on the volume of soil. Figure 10 is a flowchart showing the procedure of the estimation processing according to the embodiment.

[0125] If the estimation unit 137 detects that a landslide has occurred, it may estimate the type and number of equipment necessary to remove the soil based on the volume of the soil (step S1001).

[0126] The notification unit 138 may be controlled so that information based on the estimation result is notified to the user U (step S1002).

[0127] [8. Hardware Configuration] The information processing device 100 according to the embodiment may be implemented by a computer 1000 having a configuration such as that shown in Figure 11. Figure 11 is a hardware configuration diagram showing an example of a computer that implements the functions of the information processing device 100 according to the embodiment. The computer 1000 has a CPU 1100, RAM 1200, ROM 1300, HDD 1400, communication interface (I / F) 1500, input / output interface (I / F) 1600, and media interface (I / F) 1700.

[0128] The CPU 1100 operates based on programs stored in the ROM 1300 or HDD 1400, controlling various components. The ROM 1300 stores boot programs executed by the CPU 1100 when the computer 1000 starts up, as well as programs that depend on the computer 1000's hardware.

[0129] The HDD1400 stores programs executed by the CPU1100, as well as data used by such programs. The communication interface1500 receives data from other devices via a predetermined communication network and sends it to the CPU1100, and transmits data generated by the CPU1100 to other devices via the predetermined communication network.

[0130] The CPU 1100 controls output devices such as displays and input devices such as keyboards via the input / output interface 1600. The CPU 1100 acquires data from input devices via the input / output interface 1600. The CPU 1100 also outputs the generated data to output devices via the input / output interface 1600.

[0131] The media interface 1700 reads a program or data stored in the recording medium 1800 and provides it to the CPU 1100 via the RAM 1200. The CPU 1100 loads the program from the recording medium 1800 onto the RAM 1200 via the media interface 1700 and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or PD (Phase Change Rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0132] For example, when the computer 1000 functions as an information processing device 100 according to the embodiment, the CPU 1100 of the computer 1000 realizes the functions of the control unit 130 by executing a program loaded on the RAM 1200. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800, but as another example, these programs may be obtained from other devices via a predetermined communication network.

[0133] [9. Other] Furthermore, among the processes described in each of the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically by known methods. In addition, the processing procedures, specific names, and information including various data and parameters shown in the above document and drawings can be changed at will unless otherwise specified. For example, the various information shown in each figure is not limited to the information shown.

[0134] Furthermore, the components of each illustrated device are functionally conceptual and do not necessarily need to be physically configured as shown. In other words, the specific forms of distribution and integration of each device are not limited to those shown, and all or part of them can be functionally or physically distributed and integrated in any unit according to various loads and usage conditions.

[0135] Furthermore, the above embodiments can be combined as appropriate, provided that the processing content is not contradictory.

[0136] Although some embodiments of the present application have been described in detail above with reference to the drawings, these are illustrative examples, and the present invention can be implemented in various other forms with modifications and improvements based on the knowledge of those skilled in the art, including the embodiments described in the section on the present invention. [Explanation of Symbols]

[0137] Sy System 1. SAR observation system 2 RTK positioning systems 10 SAR satellites 20 Receiving device 30 Reference station 40 GNSS satellites 50. Core System for the Enthronement 100 Information Processing Devices 120 Storage section 130 Control Unit 131 Acquisition Department 132 Extraction part 133 Matching Department 134 Assignment section 135 Generation part 136 Calculation Section 137 Estimation Department 138 Notification Department

Claims

1. A system including an information processing device and a reference station whose location information is known, The aforementioned information processing device is An acquisition unit that acquires a reference station image which is an image including the reference station, and to which a first position information based on the position information of the reference station is assigned to the first area indicated by the reference station image, and a satellite image taken by a predetermined satellite, An extraction unit that extracts feature quantities in the first area and the second area shown by the satellite image, A matching unit that matches the feature quantities between the first area and the second area, Based on the matching results, a assigning unit assigns second position information corresponding to the first position information to the second area, A system equipped with these features.

2. The assignment unit assigns the first position information, based on the position information of the reference station, to objects included in the first area as objects in the vicinity of the reference station. The extraction unit extracts feature quantities of objects included in the first area based on the first position information. The system according to claim 1.

3. The extraction unit extracts feature quantities of objects included in the second area based on the satellite image, The matching unit matches the feature quantities between the objects included in the first area and the objects included in the second area. The assigning unit assigns absolute position information of an object included in the second area to the object as second position information corresponding to the first position information. The system according to claim 2.

4. The acquisition unit acquires the satellite image in accordance with the predetermined period of imaging by the predetermined satellite. The matching unit extracts a reference station image to be matched from the reference station image based on the approximate positional information contained in the current satellite image, and matches the feature quantities between the first area shown by the extracted reference station image to be matched and the second area shown by the current satellite image. The system according to claim 1.

5. A generation unit generates three-dimensional point cloud data by combining the satellite image to which the second position information has been assigned to the second area with the assigned satellite image, To prepare further, The system according to claim 1.

6. The acquisition unit acquires the satellite images taken sequentially by the predetermined satellite as it moves along a predetermined orbit, each time an image is taken. The generation unit generates the three-dimensional point cloud data by combining the assigned satellite images corresponding to each of the satellite images taken at different times, where at least a portion of the second area overlaps among the satellite images. The system according to claim 5.

7. The generation unit, at the time when the assigned satellite images for a predetermined period have been accumulated, repeatedly performs the process of generating the three-dimensional point cloud data by combining the assigned satellite images for the predetermined period, at each predetermined period. The system according to claim 6.

8. The matching unit further performs a process to match the relative positional relationship between the first three-dimensional point cloud data generated by combining the assigned satellite images for a first period and the second three-dimensional point cloud data generated by combining the assigned satellite images for a second period that is later than the first period. Based on the matching results, a calculation unit calculates the volume of the portion that has changed between the topography in the first period and the topography in the second period. To prepare further, The system according to claim 7.

9. The calculation unit calculates the difference between the topography during the first period and the topography during the second period based on the matching results, and calculates the volume of the portion that has changed between the topography during the first period and the topography during the second period based on the difference. The system according to claim 8.

10. An information processing method performed by a system including an information processing device and a reference station whose location information is known, The aforementioned information processing device An acquisition step of acquiring a reference station image which is an image including the reference station, and to which a first location information based on the location information of the reference station is assigned to the first area indicated by the reference station image, and a satellite image taken by a predetermined satellite, An extraction step for extracting feature quantities in the first area and the second area shown in the satellite image, A matching step of matching the feature quantities between the first area and the second area, A step of assigning second location information corresponding to the first location information to the second area based on the matching results, Information processing methods including

11. In a system including an information processing device and a reference station whose location information is known, The aforementioned information processing device A reference station image is an image that includes the aforementioned reference station, wherein a first location information is assigned to the first area indicated by the reference station image based on the location information of the reference station, and a satellite image is acquired that is taken by a predetermined satellite. Extract the feature quantities in the first area and the second area shown in the satellite image, The feature quantities are matched between the first area and the second area. Based on the matching results, the assigned satellite image obtained by assigning second position information corresponding to the first position information to the second area is A recording medium.

Images