Land-condition determining apparatus, land-condition determining method, and land-condition determining program

Abstract

A land-condition determining apparatus (100) comprises a land-use classification unit (110) that analyzes a remote-sensing image (52) and sets label information (511) indicating a coverage condition for a piece of land in a target region. A sensor-data acquisition unit (120) acquires sensor data (53) from an on-ground sensor that detects an environmental state in the target region. On the basis of the remote-sensing image (52) and the sensor data (53), a high-precision information generation unit (130) analyzes the coverage condition and the environmental state of the land in the target region, and generates high-precision condition information (54) indicating the state of the land with higher precision than the label information (511).

Description

Land status assessment device, land status assessment method, and land status assessment program

[0001] The present disclosure relates to a land status assessment device, a land status assessment method, and a land status assessment program.

[0002] There is a method for collecting information from multiple devices and estimating the extent of a natural disaster based on the collected information. Patent Literature 1 discloses a technology for extracting candidate areas based on sensor information from ground surface information acquired from remote sensing images at the time of the disaster, and determining the investigation area.

[0003] International Publication No. 2023 / 017612

[0004] The technology of Patent Document 1 combines sensor information with ground surface information or information on changes in the ground surface to determine the survey area in the event of a disaster. The technology of Patent Document 1 outputs the image area of ​​the target for disaster survey and assessment as the survey area. Therefore, the technology of Patent Document 1 has a problem in that it is not possible to grasp the land cover status and environmental state of the land in the survey area.

[0005] The present disclosure aims to grasp the land cover status and environmental state of a target area with higher accuracy.

[0006] The land condition assessment device according to the present disclosure includes a land use classification unit that acquires a remote sensing image and analyzes the remote sensing image to set label information representing the land coverage status of a target area; a sensor data acquisition unit that is provided in the target area and acquires sensor data from a ground sensor that detects the environmental status in the target area; and a high-precision information generation unit that analyzes the land coverage status and the environmental status in the target area based on the remote sensing image and the sensor data, and generates high-precision situation information that represents the land status with higher precision than the label information.

[0007] In the land status assessment device according to the present disclosure, a high-precision information generation unit analyzes the land coverage and environmental conditions in a target area based on remote sensing images and sensor data. The high-precision information generation unit then generates high-precision situation information that represents the land status with greater precision than the label information for the target area. Therefore, the land status assessment device according to the present disclosure has the effect of being able to assess the land coverage and environmental conditions with greater precision.

[0008] 1 is a diagram showing an example of the configuration of a land status assessment device according to embodiment 1. FIG. 2 is a flow diagram showing an example of the operation of a land status assessment device according to embodiment 1. FIG. 3 is a diagram showing an example of data acquired by a land status assessment device according to embodiment 1. FIG. 4 is a diagram showing an example of the configuration of item correspondence information according to embodiment 1. FIG. 5 is a schematic diagram showing an example of the flow of land status assessment processing according to embodiment 1. FIG. 6 is a schematic diagram showing example 1 of land use assessment processing in a target area according to embodiment 1. FIG. 7 is a diagram showing an example in which interpolation using sensor data F is performed in example 1 of land use assessment processing according to embodiment 1. FIG. 8 is a schematic diagram showing example 2 of land use assessment processing in a target area according to embodiment 1. FIG. 9 is a schematic diagram showing example 3 of land use assessment processing in a target area according to embodiment 1. FIG. 10 is a flow diagram showing an example of the operation of a land status assessment device according to a modified example of embodiment 1. FIG. 11 is a diagram showing an example of the configuration of a land status assessment device according to a modified example of embodiment 1.

[0009] The present embodiment will be described below with reference to the drawings. In each drawing, identical or corresponding parts are designated by the same reference numerals. In the description of the embodiment, the description of identical or corresponding parts will be omitted or simplified as appropriate. Arrows in the drawings mainly indicate the flow of data or the flow of processing. Furthermore, the sized relationships between components in the following drawings may differ from the actual relationships. Furthermore, in the description of the embodiment, directions or positions such as up, down, left, right, front, rear, front and back may be indicated. These notations are used for convenience of explanation and do not limit the placement, direction or orientation of devices, instruments, parts, etc.

[0010] Embodiment 1. *** Description of Configuration *** Figure 1 is a diagram showing an example of the configuration of a land status assessment device 100 according to this embodiment. The land status assessment device 100 is a computer. The land status assessment device 100 includes a processor 910, as well as other hardware such as a memory 921, an auxiliary storage device 922, an input interface 930, an output interface 940, and a communication device 950. The processor 910 is connected to the other hardware via signal lines and controls this other hardware.

[0011] The land status grasping device 100 includes, as functional elements, a land use classification unit 110, a sensor data acquisition unit 120, a high-precision information generation unit 130, an information display unit 140, and a storage unit 150. The storage unit 150 stores a land use map 51, remote sensing images 52, sensor data 53, high-precision situation information 54, and item correspondence information 55. The land use map 51 includes label information 511 that indicates the land coverage status.

[0012] The functions of the land use classification unit 110, the sensor data acquisition unit 120, the high-precision information generation unit 130, and the information display unit 140 are realized by software. The storage unit 150 is provided in the memory 921. The storage unit 150 may be provided in the auxiliary storage device 922, or may be provided separately in the memory 921 and the auxiliary storage device 922.

[0013] The processor 910 is a device that executes a land status assessment program. The land status assessment program is a program that realizes the functions of the land use classification unit 110, the sensor data acquisition unit 120, the high-precision information generation unit 130, and the information display unit 140. The processor 910 is an IC that performs arithmetic processing. Specific examples of the processor 910 are a CPU, a DSP, and a GPU. IC is an abbreviation for Integrated Circuit. CPU is an abbreviation for Central Processing Unit. DSP is an abbreviation for Digital Signal Processor. GPU is an abbreviation for Graphics Processing Unit.

[0014] The memory 921 is a storage device that temporarily stores data. Specific examples of the memory 921 are SRAM and DRAM. SRAM is an abbreviation for Static Random Access Memory. DRAM is an abbreviation for Dynamic Random Access Memory. The auxiliary storage device 922 is a storage device that saves data. A specific example of the auxiliary storage device 922 is an HDD. The auxiliary storage device 922 may also be a portable storage medium such as an SD (registered trademark) memory card, CF, NAND flash, flexible disk, optical disk, compact disk, Blu-ray (registered trademark) disk, or DVD. Note that HDD is an abbreviation for Hard Disk Drive. SD (registered trademark) is an abbreviation for Secure Digital. CF is an abbreviation for CompactFlash (registered trademark). DVD is an abbreviation for Digital Versatile Disk.

[0015] The input interface 930 is a port connected to an input device such as a mouse, keyboard, or touch panel. Specifically, the input interface 930 is a USB terminal. The input interface 930 may also be a port connected to a LAN. USB is an abbreviation for Universal Serial Bus. LAN is an abbreviation for Local Area Network.

[0016] The output interface 940 is a port to which a cable of an output device such as a display is connected. Specifically, the output interface 940 is a USB terminal or an HDMI (registered trademark) terminal. Specifically, the display is an LCD. The output interface 940 is also called a display interface. HDMI (registered trademark) is an abbreviation for High Definition Multimedia Interface. LCD is an abbreviation for Liquid Crystal Display.

[0017] The communication device 950 has a receiver and a transmitter. The communication device 950 is connected to a communication network such as a LAN, the Internet, a telephone line, or Wi-Fi (registered trademark). Specifically, the communication device 950 is a communication chip or NIC. NIC is an abbreviation for Network Interface Card.

[0018] The land situation determination program is executed in the land situation determination device 100. The land situation determination program is read into the processor 910 and executed by the processor 910. The memory 921 stores not only the land situation determination program but also an OS. OS is an abbreviation for Operating System. The processor 910 executes the land situation determination program while executing the OS. The land situation determination program and the OS may be stored in an auxiliary storage device 922. The land situation determination program and the OS stored in the auxiliary storage device 922 are loaded into the memory 921 and executed by the processor 910. Note that part or all of the land situation determination program may be incorporated into the OS.

[0019] The land status assessment device 100 may include multiple processors that replace the processor 910. These multiple processors share the task of executing the land status assessment program. Each processor is a device that executes the land status assessment program in the same way as the processor 910.

[0020] Data, information, signal values ​​and variable values ​​used, processed or output by the land condition assessment program are stored in memory 921, secondary storage device 922, or registers or cache memory within processor 910.

[0021] The "unit" of each of the land use classification unit 110, the sensor data acquisition unit 120, the high-accuracy information generation unit 130, and the information display unit 140 may be interpreted as a "circuit," "step," "procedure," "process," or "circuitry." The land status assessment program causes a computer to execute a land use classification process, a sensor data acquisition process, a high-accuracy information generation process, and an information display process. The "processing" of the land use classification process, the sensor data acquisition process, the high-accuracy information generation process, and the information display process may be interpreted as a "program" or a "program product." Alternatively, the "processing" of the land use classification process, the sensor data acquisition process, the high-accuracy information generation process, and the information display process may be interpreted as a "computer-readable storage medium storing a program." Alternatively, the "processing" of the land use classification process, the sensor data acquisition process, the high-accuracy information generation process, and the information display process may be interpreted as a "computer-readable storage medium recording a program." Furthermore, the land status assessment method is a method performed by the land status assessment device 100 executing the land status assessment program. The land status assessment program may be provided in a state stored in a computer-readable recording medium, or may be provided as a program product.

[0022] ***Explanation of Operation*** Figure 2 is a flow diagram showing an example of the operation of the land status assessment device 100 according to this embodiment. The operation of the land status assessment device 100 according to this embodiment will be described. The operating procedure of the land status assessment device 100 corresponds to a land status assessment method. Furthermore, a program that realizes the operation of the land status assessment device 100 corresponds to a land status assessment program.

[0023] <Land Use Classification Process 1: Step S101> The land use classification unit 110 analyzes the remote sensing image 52 and sets label information 511 that represents the land coverage status of the target area 62. Specifically, this is as follows.

[0024] 3 shows an example of data acquired by the land status assessment device 100 according to this embodiment. The data acquired by the land status assessment device 100 includes a remote sensing image 52 and sensor data 53. The land use classification unit 110 acquires the remote sensing image 52.

[0025] The land use classification unit 110 acquires a plurality of remote sensing images 52 with different granularities. Specifically, as shown in Fig. 3, the remote sensing images 52 are images such as (1) satellite multi-sensing data, (2) aerial photography data, or (3) drone photography data. For example, the remote sensing images 52 have increasingly finer granularities in the order of (1) satellite multi-sensing data, (2) aerial photography data, and (3) drone photography data.

[0026] The land use classification unit 110 performs image analysis on the remote sensing image 52 and outputs a land use map 51. The land use classification unit 110 also performs image analysis on the remote sensing image 52 and sets label information 511 that represents the land coverage status of the target area. The label information 511 may be associated with the land use map 51. The label information 511 that represents the land coverage status includes at least classifications such as buildings, water bodies, bare land, and soil and sand. The land coverage status includes statuses that indicate what the land is used for, the condition of the land, or how the land is changing.

[0027] <Sensor Data Acquisition Process: Step S102> The sensor data acquisition unit 120 acquires sensor data 53 from ground sensors 61 installed in the target area and configured to detect environmental conditions in the target area. The sensor data acquisition unit 120 acquires position information and sensor data 53 from multiple ground sensors related to the target area whose land use status has been classified using label information 511. The sensor data acquisition unit 120 acquires multiple pieces of sensor data 53 from multiple ground sensors. The sensor data acquisition unit 120 also acquires multiple types of sensor data 53 from multiple types of ground sensors. For example, the sensor data acquisition unit 120 acquires sensor data 53 from ground sensors installed in buildings installed in the target area. Specifically, this is as follows.

[0028] As shown in FIG. 3 , specific examples of sensor data 53 include (4) video data, (5) building sensing data, (6) home appliance sensing data, (7) vehicle flow data, and (8) SNS data. SNS is an abbreviation for Social Networking Service. (4) Video data is, for example, images or videos acquired by security cameras installed in a city or local area. (5) Building sensing data is data acquired by a building condition sensor installed inside a building. For example, building sensing data is data of vibration waveforms indicating the state of a building's vibration. Alternatively, the building sensing data may include images or videos capturing the state inside a building. (6) Home appliance sensing data is acquired by sensors installed in home appliances with a communication function called IoT. Home appliance sensing data is data indicating the state of the home appliance or the state around the home appliance. IoT is an abbreviation for Internet of Things. (7) Vehicle flow data is data acquired by ETC, a drive recorder, or a GPS receiver. Vehicle flow data is data that indicates the distribution and flow of vehicles. ETC is an abbreviation for Electronic Toll Collection System. GPS is an abbreviation for Global Positioning System. (8) SNS data is data that people post via SNS, and is information such as text, images, or videos.

[0029] <High-precision information generation process: step S103> The high-precision information generation unit 130 analyzes the land coverage and environmental state in the target area based on the remote sensing image 52 and the sensor data 53. The high-precision information generation unit 130 uses the analysis results to generate high-precision situation information 54 that represents the land situation with higher precision than the label information 511.

[0030] The high-precision information generating unit 130 generates the high-precision situation information 54 when some kind of event occurs in the target area. Specifically, the high-precision information generating unit 130 generates the high-precision situation information 54 when a disaster occurs in the target area. Alternatively, the high-precision information generating unit 130 may generate the high-precision situation information 54 periodically or irregularly. Furthermore, the high-precision information generating unit 130 may generate the high-precision situation information 54 in response to a request from a user.

[0031] The high-precision information generator 130 generates high-precision situation information 54 that accurately represents the land situation by combining the land coverage situation obtained from the remote sensing image 52 with sensor data 53 from buildings or houses. The high-precision situation information 54 includes information indicating the land coverage situation in the target area and the situation and state of the buildings or houses. The high-precision situation information 54 also includes information indicating changes in the situation or state, which is obtained by combining, in time series, the classification results of the land use situation with sensor data 53 acquired at a different data acquisition timing than the time of the classification.

[0032] For example, in the event of a disaster, the high-precision information generator 130 generates high-precision situation information 54 that accurately represents the damage situation in a target area by combining the land coverage situation obtained from the remote sensing image 52 with sensor data 53 from buildings or houses. The high-precision situation information 54 includes information indicating the damage situation of the land in the target area and the damage situation and damage state of the buildings or houses. The high-precision situation information 54 also includes information indicating the damage situation or changes in the damage state, which is obtained by combining, in chronological order, the classification results of the land coverage situation with sensor data 53 at the time of the disaster, which is different from the time of the classification.

[0033] The high-accuracy information generating unit 130 feeds back the high-accuracy situation information 54 to the land use classifying unit 110. The high-accuracy information generating unit 130 also outputs the high-accuracy situation information 54 to the information display unit 140.

[0034] <Information display process: step S104> The information display unit 140 displays on a display device the high-accuracy situation information 54. For example, for each of a plurality of users who require different items to be displayed among the plurality of items, the information display unit 140 displays the high-accuracy situation information 54 including items appropriate for the user.

[0035] FIG. 4 is a diagram showing an example of the configuration of item correspondence information 55 according to this embodiment. The item correspondence information 55 is information indicating the relationship between the items of the high-precision situation information 54 and users who wish to understand the contents of the items. In FIG. 4, an example of a user is a company that handles infrastructure, etc. The user is in a position where he or she wishes to understand the occurrence of a disaster. The user may be, for example, an electric power company, a railway company, a communications company, a highway company, or a local government.

[0036] The items 551 in the item correspondence information 55 are composed of major categories and medium categories. The items 551 are, for example, disaster assessment targets. For each user, it is set whether the item is one that should be assessed immediately or as needed in the initial stage of a disaster and in the recovery and reconstruction stage. It is also set whether the item is one that should be assessed frequently or as needed in normal times.

[0037] The high-accuracy status information 54 has a plurality of items. Specifically, the high-accuracy status information 54 includes the items shown as the items 551 in the item correspondence information 55.

[0038] The information display unit 140 displays high-precision situation information 54 including items corresponding to each of a plurality of users based on the item correspondence information 55 .

[0039] <Land Use Classification Process 2: Step S101> The land use classification unit 110 acquires the high-precision situation information 54 and resets the label information 511 based on the remote sensing image 52 and the high-precision situation information 54. The reset label information 511 further refines and refines classifications such as buildings, water bodies, bare land, and sediment with a high degree of accuracy. The land status assessment device 100 grasps the land cover status through observation using remote sensing images. Possible land cover information includes classifications such as buildings, water bodies, bare land, and sediment determined based on image features of optical images and SAR images. Another example includes classifications such as water bodies, vegetation, clouds, snow cover, soil grain size, urban areas, and shadows determined based on statistics of wavelength information in optical images. Yet another example includes classifications such as water bodies, fields, forests, urban areas, farmland, and snow cover determined based on polarization information and coherence information in SAR images.

[0040] <Example of Land Status Grasping Processing> Next, an example of land status grasping processing according to this embodiment will be described with reference to Fig. 5 to Fig. 9. In Fig. 5 to Fig. 9, remote sensing images may be abbreviated as remote sensing or remote sensing.

[0041] 5 is a schematic diagram showing an example of the flow of the land status ascertainment process according to this embodiment, which shows processes 1 to 6 in the land status ascertainment process.

[0042] In FIG. 5 , a remote sensing image 52 is shown in the upper row, and sensor data 53 is shown in the lower row. In FIG. 5 , the remote sensing images 52 include a remote sensing image A obtained by an optical satellite or SAR, a remote sensing image B obtained by an aircraft, and a remote sensing image C obtained by a drone. SAR is an abbreviation for synthetic aperture radar. Also listed as ground sensors are sensor D, which is a drive recorder, sensor E, which is a security camera, and sensor F, which may be related to buildings, home appliances, or social media. The sensor data 53 includes sensor data D from sensor D, sensor data E from sensor E, and sensor data F from sensor F.

[0043] <<Spatially Interpolating Observations of a Target Area Using Sensor Data>> In each of processes 1 to 6 in FIG. 5 , the land status assessment device 100 observes a target area for which label information has been set using remote sensing images during a disaster. The land status assessment device 100 assesses the land cover status through the observation using remote sensing images. Possible land cover information includes classifications such as buildings, water bodies, bare land, and sediment determined based on image features of optical images and SAR images. Another example includes classifications such as water bodies, vegetation, clouds, snow cover, soil grain size, urban areas, and shadows determined based on statistics of wavelength information in optical images. Yet another example includes classifications such as water bodies, fields, forests, urban areas, farmland, and snow cover determined based on polarization information and coherence information in SAR images. In this case, the land status assessment device 100 acquires sensor data from a ground sensor that can acquire locally accurate information within a narrow range within the observation range of the remote sensing images. The land status assessment device 100 uses sensor data to interpolate observation information from remote sensing images, thereby enabling the land status assessment device 100 to generate high-precision situation information 54 that reflects highly accurate sensor data information, thereby enabling the observation range to be expanded with high precision.

[0044] <<Spatial and Temporal Interpolation of Observations of the Target Area Using Sensor Data>> In each of processes 1 to 6 in FIG. 5 , the land status assessment device 100 combines the damage status or damage state with sensor data in a time series. Specifically, the land status assessment device 100 chronologically combines the damage status or damage state of specific areas, buildings, and houses with sensor data acquired at different timings than the land use classification results. In this way, the land status assessment device 100 acquires changes in the damage status or damage state. In this process, the land status assessment device 100 uses sensor data that is narrower in the observation range of the remote sensing image but has high local accuracy and can acquire information more frequently than the remote sensing image. The land status assessment device 100 uses this sensor data to spatially and temporally interpolate the observation information of the remote sensing image. This allows the land status assessment device 100 to generate high-precision situation information 54 that reflects information from the highly accurate and frequent sensor data, thereby expanding the observation range with high precision.

[0045] Note that processes 1 to 6, which are labeled "label update" in Fig. 5, are executed, for example, by the high-precision information generation unit of the land status assessment device. In each of processes 1 to 6, the high-precision information generation unit of the land status assessment device may proactively select remote sensors B and C, or sensors D, E, and F. Alternatively, in each of processes 1 to 6, the high-precision information generation unit of the land status assessment device may passively select remote sensors B and C, or sensors D, E, and F. Specifically, it is as follows.

[0046] An example of a proactive approach is an automated case where the process is completed within the land status assessment device. The land status assessment device pre-specifies a specific location and state, and monitors the situation and state. If there is a change in the monitored information, the land status assessment device will acquire the situation, state, or information of the location using a more granular remote sensor or sensor.

[0047] In a passive example, a specific location or state is specified from outside the land status assessment device via an input interface. The specific location or state is monitored externally via an output interface. If a change is detected by the monitor, a finer-grained remote sensor or sensor is specified to obtain the situation, state, and information for that location. In this case, the process of detecting changes by monitoring may be performed automatically within the land status assessment device. If necessary, it is also possible to gradually increase the granularity, adaptively switch locations, or continue monitoring repeatedly.

[0048] For example, remote sensors B and C or sensors D, E, and F may be selected depending on the intended use of the high-precision situation information 54. Specific examples of intended uses include planning evacuation routes from disaster areas, predicting changes in disaster situations, and notifying disaster victims of these changes. Furthermore, users such as the government, local governments, fire departments, and police may plan rescue team dispatch routes or determine rescue urgency levels.

[0049] An example of the land use grasping process will be described in more detail below. Note that in Fig. 6 to Fig. 9 described later, sensor F is used as an example of the sensor that acquires the sensor data. This is just an example, and the present embodiment can be applied even if sensor F is replaced with sensor D or sensor E.

[0050] <Example 1 of Land Use Grasping Processing> Figure 6 is a schematic diagram showing Example 1 of land use grasping processing in a target area according to this embodiment. In Example 1 of land use grasping processing, the land use classification unit 110 acquires remote sensing image A and sets label information. Here, the label information set includes buildings, water bodies, bare land, and soil. The sensor data acquisition unit 120 acquires sensor data F from sensor F. The high-precision information generation unit 130 generates high-precision situation information 54 using the remote sensing image A and the sensor data F. The land use classification unit 110 updates the label information using the high-precision situation information 54.

[0051] 7 is a diagram showing a process in which interpolation using sensor data F is performed in Example 1 of the land use grasping process according to this embodiment. The high-precision information generation unit 130 may analyze the state of the building from the sensor data F, estimate the land coverage and environmental state based on the state of the building, and generate the high-precision situation information 54. Alternatively, the high-precision information generation unit 130 may estimate the land coverage and environmental state in an area between multiple ground sensors based on multiple sensor data, and generate the high-precision situation information 54.

[0052] The land status assessment device 100 interpolates the observation information of the remote sensing A using the sensor F, which has a narrow range but can acquire the state of land and buildings locally with high accuracy within the observation range of the remote sensing A. This enables the land status assessment device 100 to expand the observation range based on the information of the highly accurate sensor F.

[0053] Furthermore, the land status assessment device 100 continuously interpolates the observation information of remote sensing A spatially and temporally using sensor F, which has a narrow range but can steadily acquire locally highly accurate information on the state of land and buildings within the observation range of remote sensing A. This allows the land status assessment device 100 to expand the observation range based on the highly accurate information of sensor F while updating sensor data F, which is the information from sensor F. Note that, hereinafter, the information acquired from sensor F (sensor data F) may be simply referred to as sensor F. The same applies to sensors D and E.

[0054] <<Spatial Edition (Remote Sensing A + Sensor F)>> (Disaster Response) This section describes an example in which the land situation assessment device 100 uses sensor data to spatially interpolate high-precision situation information 54 with high accuracy during a disaster. The land situation assessment device 100 combines information classified into a land use map based on a pre-analysis of land use status and observation information from remote sensing A with low-precision information from sensor F. This interpolates the observation information from remote sensing A. Remote sensing A is, for example, remote sensing images obtained by optical satellites or SAR. Sensor F is information from IoT and the like found in various facilities and home appliances. As a result, the land situation assessment device 100 is configured to comprehensively assess a wide range of disaster situations, such as building damage, fires, flooding, and landslides, early in the course of a disaster.

[0055] <<Space & Time Edition (Remote Sensing A + Sensor F) & Time>> (Disaster Response) This section describes an example in which the land status assessment device 100 uses sensor data to interpolate high-precision situation information 54 spatially and temporally with high accuracy during a disaster. The land status assessment device 100 continuously acquires information from Sensor F that is classified into a land use map based on a pre-analysis of land use status and observation information from Remote Sensing A. The land status assessment device 100 then combines the continuously acquired information from Sensor F with the low-precision information from Remote Sensing A. In this way, the land status assessment device 100 continuously interpolates and updates the observation information from Remote Sensing A spatially and temporally using Sensor F. This allows the land status assessment device 100 to comprehensively and continuously assess a wide range of disaster situations, such as building damage, fires, flooding, and landslides, and changes in those situations, in an early stage of a disaster.

[0056] 8 is a schematic diagram showing Example 2 of the land use assessment process for a target area according to this embodiment. Here, an example will be described in which high-precision situation information 54 is interpolated with high precision using remote sensing images A and B and sensor F.

[0057] The land status assessment device 100 performs observations using remote sensing B based on the state of the land and buildings acquired by sensor F and the observation information of remote sensing A interpolated by sensor F. Remote sensing B has a narrower observation range than remote sensing A, but is capable of acquiring highly accurate information. In this way, the observation range can be expanded by using the highly accurate sensor F and remote sensing B.

[0058] The land status assessment device 100 uses sensor F within the observation range of remote sensing A to perform observation by remote sensing B, which has a narrower observation range than remote sensing A but is capable of acquiring observation information with high accuracy and frequency. Sensor F has a narrower range but can steadily acquire information on the condition of land and buildings with high local accuracy. Furthermore, by using such sensor F, the land status assessment device 100 can perform observation by remote sensing B based on observation information that is updated by continuously spatially interpolating the observation information of remote sensing A. In other words, the land status assessment device 100 can perform spatial and temporal interpolation of observation information obtained by a combination of sensor F, remote sensing A, and remote sensing B. The land status assessment device 100 can expand the observation range of sensor F and remote sensing B, which are continuously updated with high accuracy.

[0059] <<Spatial Edition (Remote Sensing A + Remote Sensing B + Sensor F)>> (Disaster Response) This section describes an example in which the land situation assessment device 100 uses sensor data to spatially interpolate high-precision situation information 54 with high accuracy during a disaster. In order to determine areas with high priority for evacuation and rescue of disaster victims and the type of damage from a comprehensive assessment of the damage situation early on after a disaster occurs, the land situation assessment device 100 performs observations as follows. The land situation assessment device 100 performs observations using remote sensing B based on information classified into a land use map in advance after analyzing land use status and observation information from remote sensing A interpolated using sensor F. Remote sensing B has a narrower observation range than remote sensing A but can acquire information with higher accuracy, such as aircraft imaging. Through such observations, the land situation assessment device 100 interpolates observation information obtained using a combination of sensor F, remote sensing A, and remote sensing B. The land situation assessment device 100 is configured to use highly accurate sensors F and remote sensing B to assess areas with high priority for evacuation and rescue of victims, as well as the type of damage and damage situation in a variety of disaster situations such as building damage, fire, flooding, and landslides.

[0060] <<Space & Time Edition (Remote Sensing A + Remote Sensing B + Sensor F) & Time>> (Disaster Response) This section describes an example in which the land situation assessment device 100 uses sensor data to spatially and temporally interpolate high-precision situation information 54 with high accuracy during a disaster. In order to consistently identify areas with high priority for evacuation and rescue of disaster victims and the type of damage from a comprehensive assessment of the damage situation early after a disaster occurs, the land situation assessment device 100 performs observations as follows. The land situation assessment device 100 performs observations using remote sensing B based on information classified into a land use map in advance based on an analysis of land use status and observation information from remote sensing A interpolated using sensor F. Remote sensing B has a narrower observation range than remote sensing A but can acquire information with higher accuracy and frequency, such as aerial imaging. Through such observations, the land situation assessment device 100 spatially and temporally interpolates the observation information obtained by combining sensor F with remote sensing A and remote sensing B. The land situation assessment device 100 is configured to adaptively assess areas with high priority for evacuation and rescue of victims, the type of damage, the damage situation, and any changes thereto, in a variety of disaster situations such as building damage, fire, flooding, and landslides, using highly accurate sensors F and remote sensing B.

[0061] 9 is a schematic diagram showing Example 3 of the land use assessment process for a target area according to this embodiment. Here, an example will be described in which high-precision situation information 54 is interpolated with high precision using remote sensing images A, B, and C and sensor F.

[0062] When more accurate observation information is required, the land status assessment device 100 performs observation as follows: Based on the observation information of remote sensing A and remote sensing B interpolated by sensor F, the land status assessment device 100 performs observation using remote sensing C, which has a narrower observation range than remote sensing B but can acquire more accurate information. This observation is an interpolation of observation information obtained by combining sensor F, remote sensing A, remote sensing B, and remote sensing C. The highly accurate sensor F and remote sensing C can widen the observation range.

[0063] When more accurate and frequent observation information is required, the land status assessment device 100 performs observation as follows: Based on the observation information of remote sensing A and remote sensing B interpolated by sensor F, the land status assessment device 100 performs observation using remote sensing C, which has a narrower observation range than remote sensing B but can acquire information with higher accuracy and frequency. This observation is an interpolation of observation information obtained by combining sensor F, remote sensing A, remote sensing B, and remote sensing C. The observation range can be expanded by using sensor F and remote sensing C, which have higher accuracy and frequency.

[0064] <<Spatial Edition (Remote Sensing A + Remote Sensing B + Remote Sensing C + Sensor F)>> (Disaster Response) This section describes an example in which the land situation assessment device 100 uses sensor data to spatially interpolate high-precision situation information 54 with high accuracy during a disaster. During a disaster, the following observations are performed to obtain highly accurate observation information for determining specific information necessary for the movement and rescue operations of rescue teams, such as police and fire departments, heading to the disaster site based on the disaster situation, which identifies areas with high priority for victim evacuation and rescue and the type of damage. The land situation assessment device 100 performs observations using remote sensing C based on information classified into a land use map obtained by prior analysis of land use status and observation information obtained by remote sensing A and remote sensing B interpolated using sensor F. Remote sensing C has a narrower observation range than remote sensing B but can obtain information with high accuracy, such as drone imaging. Through such observations, the land situation assessment device 100 interpolates observation information obtained using a combination of sensor F, remote sensing A, remote sensing B, and remote sensing C. The land situation assessment device 100 is configured to use highly accurate sensors F and remote sensing C to assess areas with high priority for evacuation and rescue of disaster victims, the type of damage, and the extent of the damage.

[0065] <<Space & Time Edition (Remote Sensing A + Remote Sensing B + Remote Sensing C + Sensor F) & Time>> (Disaster Response) This observation is as follows. An example will be described in which, during a disaster, the land situation assessment device 100 uses sensor data to interpolate high-precision situation information 54 spatially and temporally with high accuracy. During a disaster, if highly accurate observation information is continuously required to grasp specific information necessary for the movement of rescue teams, such as police and fire departments, heading to the disaster site and for rescue operations, based on the disaster situation, which identifies areas with high priority for victim evacuation and rescue and the type of damage, observation is performed as follows. The land situation assessment device 100 performs observation using remote sensing C based on information classified into a land use map obtained by analyzing land use status in advance and observation information from remote sensing A and remote sensing B interpolated by sensor F. Remote sensing C has a narrower observation range than remote sensing B, but is capable of acquiring information with high accuracy and frequency, such as drone imaging. Through such observations, the land status assessment device 100 interpolates the observation information obtained by combining sensor F, remote sensing A, remote sensing B, and remote sensing C. The land status assessment device 100 is configured to use highly accurate sensor F and remote sensing C to grasp movement to specific disaster areas or disaster points, as well as the detailed conditions and changes in the disaster areas or disaster points.

[0066] <Land Use Grasping Processing Example 4> <Land Use Label Update to Reflect Disaster Information> (Disaster Response) <<Spatial Edition>> The land status assessment device 100 updates labels based on remote sensing images and sensor data. The land status assessment device 100 improves observation accuracy and expands observation range by combining remote sensing images and sensor data according to the use, purpose, situation, etc. This enables highly accurate and reliable updating of land use labels based on a land use map that can be used for adaptive disaster status assessment and disaster response.

[0067] <Space & Time Edition> The land status assessment device 100 updates labels based on time-series remote sensing images and time-series sensor data. The land status assessment device 100 combines remote sensing images and sensor data in time series, with observation information and data acquired at different times depending on the application, purpose, situation, etc. Through this combination, the land status assessment device 100 continuously improves observation accuracy and expands the observation range. This enables the land status assessment device 100 to update land use labels with high accuracy and reliability based on land use maps that can be used for adaptive disaster status assessment and disaster response.

[0068] ***Other Configurations*** <Variation 1> Fig. 10 is a flow diagram showing an example of the operation of the land status assessment device 100 according to Variation 1 of this embodiment. As described above in step S101, the land use classification unit 110 performs image analysis on the remote sensing image 52 and outputs the land use map 51. The land use classification unit 110 also performs image analysis on the remote sensing image 52 and sets label information 511 that represents the land coverage status of the target area. The label information 511 may be associated with the land use map 51. In Variation 1 of this embodiment, the land use classification unit 110 updates the land use map 511 based on the remote sensing image 52 and the high-precision status information 54.

[0069] In the first modification of this embodiment, step S103a is added after step S103 in Fig. 10. In step S103a, the land use classification unit 110 executes a map update process to update the land use map 511 based on the remote sensing image 52 and the high-precision situation information 54. The other processes are the same as those in Fig. 2.

[0070] The map update process may be executed after the land use classification process 2 in step S101 described in embodiment 1. That is, the land use classification unit 110 may acquire the high-accuracy situation information 54, reset the label information 511 based on the remote sensing image 52 and the high-accuracy situation information 54, and update the land use map 511.

[0071] <Variation 2> In this embodiment, an example of a land status ascertainment process for ascertaining the land status when a disaster occurs has been described, but this embodiment can be applied not only to disasters but also to peacetime. Basically, the land status ascertainment process in peacetime is similar to disaster response. The land status ascertainment process in peacetime may differ from that in a disaster in that the time axis is long, the focus is primarily on operational aspects such as maintenance, and the main function is updating by resetting.

[0072] The land use understanding process corresponding to Figures 6 and 7 can also be applied in peacetime. In the observation range of remote sensing A, sensor F, which has a narrow range but can acquire the state of land and buildings with high accuracy locally, is used to increase the accuracy of the observation information of remote sensing A, and sensor F, which has a wide range, is used to expand the observation range.

[0073] <Spatial Edition (Remote Sensing A + Sensor F)> (Peacetime Response) This observation is as follows. The land status assessment device 100 combines information obtained by analyzing land use status in advance and classifying it into a land use map with observation information from remote sensing A, and the information from Sensor F with low-precision information from remote sensing A to interpolate the observation information from remote sensing A. Remote sensing A is remote sensing images obtained by optical satellites and SAR. Sensor F is information such as IoT from various facilities and home appliances. As a result, the land status assessment device 100 is configured to comprehensively assess the status and condition of land, rivers, ports, facilities, and buildings, including changes, weathering, deterioration, and damage, over a wide range.

[0074] <Space & Time Edition (Remote Sensing A + Sensor F) & Time> (Normal Operation) The land status assessment device 100 constantly acquires information from Sensor F that has been previously analyzed for land use status and classified into a land use map, as well as observation information from Remote Sensing A. The land status assessment device 100 then combines this information with low-precision information from Remote Sensing A and continuously interpolates and updates the observation information from Remote Sensing A spatially and temporally. Here, Remote Sensing A is remote sensing imagery obtained by optical satellites or SAR. Sensor F is information from IoT and other sources found in various facilities and home appliances. As a result, the land status assessment device 100 is configured to comprehensively assess the status and state of land, rivers, ports, facilities, and buildings, including changes, weathering, deterioration, and damage, and their changes, over a wide range.

[0075] The land use grasping process corresponding to FIG. 8 can also be applied in normal times.

[0076] <Spatial Edition (Remote Sensing A + Remote Sensing B + Sensor F)> (Peacetime Response) The land status assessment device 100 performs the following observations to grasp the status and condition of land, rivers, ports, facilities, and buildings, such as changes, weathering, deterioration, and damage. The land status assessment device 100 performs observations using remote sensing B based on information classified into a land use map based on a prior analysis of land use status and observation information from remote sensing A interpolated using sensor F. Remote sensing B has a narrower observation range than remote sensing A but can acquire highly accurate information, such as aircraft imaging. Through such observations, the land status assessment device 100 interpolates observation information obtained by a combination of sensor F, remote sensing A, and remote sensing B. In this way, the land status assessment device 100 is configured to grasp the status and condition of land, rivers, ports, facilities, and buildings, such as changes, weathering, deterioration, and damage, using highly accurate sensor F and remote sensing B.

[0077] <Space & Time Edition (Remote Sensing A + Remote Sensing B + Sensor F) & Time> (Normal Operation) The following observations are performed to constantly grasp the status and state of land, rivers, ports, facilities, and buildings, such as changes, weathering, deterioration, and damage. The land status assessment device 100 performs observations using remote sensing B based on information previously analyzed and classified into a land use map of land use status and observation information from remote sensing A interpolated using sensor F. Remote sensing B has a narrower observation range than remote sensing A but can acquire information with higher accuracy and frequency, such as aerial imaging. Through such observations, the land status assessment device 100 performs spatial and temporal interpolation of observation information obtained by a combination of sensor F, remote sensing A, and remote sensing B. The land status assessment device 100 is configured to adaptively grasp the status and state, such as changes, weathering, deterioration, and damage, of land, rivers, ports, facilities, and buildings, as well as changes therein, using highly accurate sensor F and remote sensing B.

[0078] The land use grasping process corresponding to FIG. 9 can also be applied in normal times.

[0079] <Spatial Edition (Remote Sensing A + Remote Sensing B + Remote Sensing C + Sensor F)> (Peacetime Response) This observation is as follows. The land status assessment device 100 performs the following observations to comprehensively assess the status and condition of land / rivers / harbors / facilities / buildings, such as changes, weathering, deterioration, and damage, from the entire object to specific areas. The land status assessment device 100 performs observations using remote sensing C based on information classified in a land use map obtained by analyzing land use status in advance and observation information from remote sensing A and remote sensing B interpolated using sensor F. Remote sensing C has a narrower observation range than remote sensing B but can acquire information with higher accuracy, such as drone imaging. Through such observations, the land status assessment device 100 interpolates observation information obtained using a combination of sensor F, remote sensing A, remote sensing B, and remote sensing C. In this way, the land condition assessment device 100 is configured to use highly accurate sensors F and remote sensing C to comprehensively assess the situation and condition of land / rivers / harbors / facilities / buildings, such as changes, weathering, deterioration, and damage, from the entire object to localized areas.

[0080] <Space & Time Edition (Remote Sensing A + Remote Sensing B + Remote Sensing C + Sensor F) & Time> (Normal Operation) This observation is as follows. The land status assessment device 100 performs the following observations to comprehensively assess the status and condition of land / rivers / harbors / facilities / buildings, including changes, weathering, deterioration, and damage, from the entire object to specific areas, including changes over time. The land status assessment device 100 performs observations using remote sensing C based on information classified into a land use map obtained by prior analysis of land use status and observation information from remote sensing A and remote sensing B interpolated using sensor F. Remote sensing C has a narrower observation range than remote sensing B but is capable of acquiring information with higher accuracy and frequency, such as drone imaging. Through such observations, the land status assessment device 100 interpolates observation information obtained using a combination of sensor F, remote sensing A, remote sensing B, and remote sensing C. In this way, the land condition assessment device 100 is configured to use highly accurate sensors F and remote sensing C to comprehensively assess the situation and condition of land / rivers / harbors / facilities / buildings, such as changes, weathering, deterioration, and damage, from the entire object to localized areas, including changes over time.

[0081] <Modification 3> In this embodiment, the functions of the land use classification unit 110, the sensor data acquisition unit 120, the high-accuracy information generation unit 130, and the information display unit 140 are realized by software. As a modification, the functions of the land use classification unit 110, the sensor data acquisition unit 120, the high-accuracy information generation unit 130, and the information display unit 140 may be realized by hardware. Specifically, the land status assessment device 100 includes an electronic circuit 909 instead of the processor 910.

[0082] FIG. 11 is a diagram showing an example of the configuration of a land status assessment device 100 according to a modified example of this embodiment. The electronic circuit 909 is a dedicated electronic circuit that realizes the functions of the land use classification unit 110, the sensor data acquisition unit 120, the high-precision information generation unit 130, and the information display unit 140. Specifically, the electronic circuit 909 is a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, a logic IC, a GA, an ASIC, or an FPGA. GA is an abbreviation for Gate Array. ASIC is an abbreviation for Application Specific Integrated Circuit. FPGA is an abbreviation for Field-Programmable Gate Array.

[0083] The functions of the land use classification unit 110, the sensor data acquisition unit 120, the high-precision information generation unit 130, and the information display unit 140 may be realized by a single electronic circuit, or may be realized by distributing them across multiple electronic circuits.

[0084] As another modification, some of the functions of the land use classification unit 110, the sensor data acquisition unit 120, the high-accuracy information generation unit 130, and the information display unit 140 may be realized by electronic circuits, and the remaining functions may be realized by software. Also, some or all of the functions of the land use classification unit 110, the sensor data acquisition unit 120, the high-accuracy information generation unit 130, and the information display unit 140 may be realized by firmware.

[0085] Each of the processor and the electronic circuit is also called processing circuitry. That is, the functions of the land use classification unit 110, the sensor data acquisition unit 120, the high-accuracy information generation unit 130, and the information display unit 140 are realized by the processing circuitry.

[0086] <Variation 4> This section describes land cover information that the land status assessment device 100 grasps through observation using remote sensing images. Possible land cover information includes classifications such as buildings, water bodies, bare land, and soil and sand, which are determined based on the image characteristics of optical images and SAR images. Another example includes classifications such as water bodies, vegetation, clouds, snow cover, soil grain size, urban areas, and shadows, which are determined based on statistics of wavelength information in optical images. Yet another example includes classifications such as water bodies, fields, forests, urban areas, farmland, and snow cover, which are determined based on polarization information and coherence information in SAR images.

[0087] ***Explanation of Effects of the Present Embodiment*** The effects of the present embodiment will be described. As described above, the land status assessment device 100 according to the present embodiment provides a method for acquiring the damage status and changes in the damage state of specific areas, buildings, and houses during a disaster by combining the land use status of the earth's surface from satellite images with sensor data from buildings and houses. Furthermore, the land status assessment device 100 according to the present embodiment provides a method for acquiring the damage status and changes in the damage state by combining, in time series, sensor data acquired at different data acquisition timings with the land use status classification results. The land status assessment device 100 according to the present embodiment can provide a method for generating information combining the land use status of the earth's surface from satellite images with sensor data from buildings and houses over a wide area with high accuracy. Furthermore, the land status assessment device 100 according to the present embodiment can provide a method for generating information on the range, status, location, and changes in land use of specific areas, buildings, etc. over a wide area with high accuracy.

[0088] In the first embodiment described above, each unit of the land status assessment device has been described as an independent functional block. However, the configuration of the land status assessment device does not have to be the same as that of the above-described embodiment. The functional blocks of the land status assessment device may have any configuration as long as they can realize the functions described in the above-described embodiment. Furthermore, the land status assessment device may not be a single device, but may be a system composed of multiple devices. Furthermore, multiple parts of the first embodiment may be combined to implement the present invention. Alternatively, only one part of the first embodiment may be implemented. In addition, the present embodiment may be combined in any way, either as a whole or in part. In other words, in the first embodiment, the various embodiments may be freely combined, or any component of each embodiment may be modified, or any component of each embodiment may be omitted.

[0089] The above-described embodiments are essentially preferred examples and are not intended to limit the scope of the present disclosure, the scope of application of the present disclosure, or the scope of use of the present disclosure. The above-described embodiments can be modified in various ways as needed. For example, the procedures described using flow charts or sequence diagrams may be modified as appropriate.

[0090] 51 Land use map, 52 Remote sensing image, 53 Sensor data, 54 High-precision situation information, 55 Item correspondence information, 551 Item, 61 Ground sensor, 62 Target area, 100 Land situation assessment device, 110 Land use classification unit, 120 Sensor data acquisition unit, 130 High-precision information generation unit, 140 Information display unit, 150 Storage unit, 511 Label information, 909 Electronic circuit, 910 Processor, 921 Memory, 922 Auxiliary storage device, 930 Input interface, 940 Output interface, 950 Communication device.

Claims

1. A land use classification unit analyzes remote sensing images and sets label information representing the land cover status of the target area, A sensor data acquisition unit is provided in the target area and acquires sensor data from a ground sensor that detects the environmental conditions in the target area. A high-precision information generation unit analyzes the land cover and environmental conditions in the target area based on the remote sensing image and the sensor data, and generates high-precision condition information that represents the land conditions with higher accuracy than the label information. A land condition assessment device equipped with the following features.

2. The aforementioned land use classification section is: The land condition assessment device according to claim 1, which acquires the high-precision condition information and resets the label information based on the remote sensing image and the high-precision condition information.

3. The aforementioned land use classification section is: A land condition assessment device according to claim 1 or 2, which generates a map representing the target area to which the label information has been assigned as a land use map, and updates the land use map based on the remote sensing image and the high-precision condition information.

4. The high-precision information generation unit is A land condition assessment device according to claim 1 or claim 2, which generates high-precision condition information when an event occurs in the target area.

5. The high-precision information generation unit is A land condition assessment device according to claim 1 or claim 2, which generates the high-precision condition information when a disaster occurs in the target area.

6. The aforementioned sensor data acquisition unit is Sensor data is acquired from the ground sensors installed on the building located in the aforementioned target area. The high-precision information generation unit is A land condition assessment device according to claim 1 or claim 2, which analyzes the state of the building from the sensor data and estimates the land cover and environmental conditions based on the state of the building.

7. The aforementioned sensor data acquisition unit is Time-series sensor data is acquired from the ground sensors installed on the building located in the aforementioned target area. The high-precision information generation unit is The land condition assessment device according to claim 6, which analyzes changes in the state of the building from the aforementioned time-series sensor data and estimates changes in the land cover and environmental conditions based on the changes in the state of the building.

8. The aforementioned sensor data acquisition unit is Acquire multiple sensor data from multiple ground sensors, The high-precision information generation unit is A land condition assessment device according to claim 1 or 2, which estimates the land cover condition and the environmental condition in the region between the multiple ground sensors based on the data from the multiple sensors, and interpolates the high-precision condition information.

9. The aforementioned high-precision status information has multiple items, The aforementioned land condition assessment device, The land condition assessment device according to claim 1 or claim 2, further comprising an information display unit that displays the high-precision condition information, including items appropriate to each of the multiple users, for each of the multiple users, each of whom has different items that require display from among the multiple items.

10. The aforementioned land condition assessment device, The system includes item correspondence information in which items corresponding to each of the aforementioned multiple users are set, The aforementioned information display unit is The land condition assessment device according to claim 9, which displays the high-precision condition information including items corresponding to each of the multiple users based on the item correspondence information.

11. The aforementioned land use classification section is: By acquiring multiple remote sensing images with different granularities, The aforementioned sensor data acquisition unit is By acquiring multiple types of sensor data from multiple types of ground sensors, The high-precision information generation unit is The land condition assessment device according to claim 9, which selects at least one remote sensing image from the plurality of remote sensing images and at least one sensor data from the plurality of types of sensor data according to the items required by the user, and generates high-precision condition information for the required items based on the selected remote sensing image and the sensor data.

12. Computers By analyzing remote sensing images, label information representing the land cover of the target area is set. Sensor data is acquired from a ground sensor provided in the target area, which detects the environmental conditions in the target area. A method for understanding land conditions, which analyzes the land cover and environmental conditions in the target area based on the remote sensing image and the sensor data, and generates high-precision condition information that represents the land conditions with higher precision than the label information.

13. Land use classification processing involves analyzing remote sensing images and setting label information that represents the land cover status of the target area. A sensor data acquisition process that acquires sensor data from a ground sensor provided in the target area and which detects the environmental conditions in the target area, A high-precision information generation process analyzes the land cover and environmental conditions in the target area based on the remote sensing image and the sensor data, and generates high-precision condition information that represents the land conditions with higher accuracy than the label information. A land condition assessment program that is executed by a computer.