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

Abstract

A land status ascertaining device (100) is provided with a land use classifying unit (110) that analyzes a remote sensing image (52) and sets label information (511) for a survey region. Furthermore, on the basis of log information (31) transmitted from an IoT appliance (61), a local information estimating unit (120) estimates, as local status information (53), the condition and status of land at a survey point where the IoT appliance (61) is present. An accuracy enhancing unit (140) estimates, on the basis of the remote sensing image (52) and the local status information (53), the condition and status of land at points in the survey region other than the survey point, and generates region condition information (55) indicating the condition and status of the land in the survey region with a higher accuracy 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 determining the investigation area by extracting candidate areas to be investigated from sensor information for ground surface information acquired from remote sensing images at the time of the disaster occurrence.

[0003] International Publication No. 2023 / 017612

[0004] The technology of Patent Document 1 only determines the survey area, and is unable to grasp the state and condition of the land in the survey area with high accuracy.

[0005] The present disclosure aims to grasp the land condition and situation of a survey area with higher accuracy.

[0006] The land status assessment device according to the present disclosure includes a land use classification unit that analyzes remote sensing images and sets label information representing the land coverage status of a survey area; a local information estimation unit that receives log information transmitted from IoT devices (Internet of Things devices) installed in the survey area and estimates the land status and status of a survey point where the IoT device is located as local status information based on the log information; and a precision improvement unit that estimates the land status and status at points other than the survey point in the survey area based on the remote sensing images and the local status information, and generates area status information that represents the land status and status of the survey area with higher accuracy than the label information.

[0007] In the land status assessment device according to the present disclosure, a local information estimation unit receives log information transmitted from IoT devices installed in a survey area. The local information estimation unit estimates the land condition and status of the survey point where the IoT device is located as local status information based on the log information. The precision improvement unit estimates the land condition and status of points other than the points in the survey area based on the remote sensing image and the local status information, and generates area status information that represents the land condition and status of the survey area with higher precision than the label information. Therefore, the land status assessment device according to the present disclosure has the effect of being able to assess the land condition and status of the survey area with higher precision.

[0008] FIG. 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 schematic diagram showing an example of the flow of land status assessment processing according to embodiment 1. FIG. 5 is a schematic diagram showing example 1 of land status assessment processing in a survey area according to embodiment 1. FIG. 6 is a diagram showing an example in which interpolation using sensor data F is performed in example 1 of land status assessment processing according to embodiment 1. FIG. 7 is a schematic diagram showing example 2 of land status assessment processing in a survey area according to embodiment 1. FIG. 8 is a schematic diagram showing example 3 of land status assessment processing in a survey area according to embodiment 1. FIG. 9 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 device that assesses the state and situation of land in a survey area using remote sensing images 52 and log information 31 and sensor data 32 from IoT devices 61. In this embodiment, the land status assessment device 100 is also referred to as a wide-area activity assessment device that assesses the state and situation of land, including the activity of things in the survey area.

[0011] 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 the other hardware.

[0012] The land status grasping device 100 includes, as functional elements, a land use classification unit 110, a local information estimation unit 120, a dynamic analysis unit 130, a precision improvement unit 140, and a memory unit 150. The memory unit 150 stores a land use map 51, a remote sensing image 52, local situation information 53, local dynamic information 54, and area state information 55. The land use map 51 includes label information 511 that indicates the land coverage status.

[0013] The functions of the land use classification unit 110, the local information estimation unit 120, the dynamic analysis unit 130, and the accuracy improvement 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.

[0014] 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 local information estimation unit 120, the dynamic analysis unit 130, and the accuracy improvement 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.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] The "unit" of each of the land use classification unit 110, the local information estimation unit 120, the dynamic analysis unit 130, and the accuracy improvement unit 140 may be interpreted as a "circuit," "step," "procedure," "process," or "circuitry." The land situation assessment program causes a computer to execute a land use classification process, a local information estimation process, a dynamic analysis process, and an accuracy improvement process. The "processes" of the land use classification process, the local information estimation process, the dynamic analysis process, and the accuracy improvement process may be interpreted as a "program" or a "program product." Alternatively, the "processes" of the land use classification process, the local information estimation process, the dynamic analysis process, and the accuracy improvement process may be interpreted as a "computer-readable storage medium storing a program." Alternatively, the "processes" of the land use classification process, the local information estimation process, the dynamic analysis process, and the accuracy improvement process may be interpreted as a "computer-readable recording medium recording a program." Furthermore, the land situation assessment method is a method performed by the land situation assessment device 100 executing the land situation assessment program. The land situation assessment program may be provided stored in a computer-readable recording medium. The land status assessment program may also be provided as a program product.

[0023] ***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.

[0024] <Land use classification process: step S101> The land use classification unit 110 analyzes the remote sensing image 52 to set label information 511 that represents the land coverage status of the survey area. The survey area is the area that is surveyed by the land status assessment device 100, i.e., the area for which the land use status is to be classified and identified. The land use status includes the classification indicated by the land label information, and the state and status of the land, including the dynamics of things. The land label information represents the land coverage status. Specifically, it is as follows:

[0025] 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 data acquired by an IoT device 61. The land use classification unit 110 acquires the remote sensing image 52.

[0026] 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.

[0027] 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 cover status of the survey area. The label information 511 may be associated with the land use map 51. The label information 511 that represents the land cover status includes at least classifications such as buildings, water bodies, bare land, and soil and sand. The land cover status includes statuses that indicate what the land is used for, the condition of the land, or how the land is changing.

[0028] <Local Information Estimation Process: Step S102> The local information estimation unit 120 receives log information 31 transmitted from the IoT devices 61 installed in the survey area. Based on the log information 31, the local information estimation unit 120 estimates the state and situation of the land at the survey point where the IoT devices 61 are located as local situation information 53. The state and situation of the land include not only land use information classified by label information, but also the surrounding state or situation, such as whether the surrounding area of ​​the survey point is in a normal or abnormal state. The local information estimation unit 120 receives log information 31 corresponding to each IoT device in a device group consisting of one or more IoT devices 61 located on the ground. The log information 31 indicates, for example, whether the IoT devices 61 are normal or abnormal. Based on the log information 31, the local information estimation unit 120 estimates whether each IoT device in the device group is normal or abnormal at the survey point where each IoT device is located.

[0029] For example, the IoT device 61 is at least one of an IoT home appliance and an elevator. The IoT home appliance includes, for example, an air conditioner, a refrigerator, a television, or other home appliances connected to the Internet. The IoT device 61 may be any device that is connected to the Internet and can transmit information to the land condition assessment device 100.

[0030] The following devices may be used as the IoT device 61. The IoT device 61 may be a fixed surveillance camera, a mobile surveillance camera (lidar / radar), a camera mounted on a moving object, or a PMV perimeter monitoring sensor. The IoT device 61 may also be a high-precision locator or the like in addition to a camera. The IoT device 61 may also be a sensor installed in a facility, a drive recorder, a publicly installed sensor, a sensor on a mobile terminal, or a sensor used for access control. As such, the IoT device 61 may be mounted on a device such as a home appliance, mounted on a moving object, mounted on a facility, or located in the air. The IoT device 61 may be any sensor that can estimate the health or safety of a building or house based on its log information.

[0031] If the IoT device 61 is an IoT home appliance, information such as an air conditioner on / off signal, a refrigerator open / close signal, or a television on / off signal becomes the log information 31. From such log information 31, the local information estimation unit 120 estimates the state and situation of the land at the survey point where the IoT device 61 is located as local situation information 53. Specifically, the local situation information 53 includes information such as whether a building exists at the survey point where the IoT device 61 is located and whether the survey point is normal or abnormal.

[0032] If the IoT device 61 is an elevator, for example, information such as whether the elevator is ascending or descending, whether or not there is flooding detected by a flood sensor, or whether water on the floor is detected by a thermal camera becomes the log information 31. The local information estimation unit 120 estimates the state and situation of the land at the survey point where the IoT device 61 is located from such log information 31 as local situation information 53. Specifically, the local situation information 53 includes information such as whether there is flooding or a fire at the survey point where the IoT device 61 is located, and whether the elevator is operating normally. The local situation information 53 includes information such as whether the survey point is normal or abnormal.

[0033] Log information 31 is "device operation and operating information collected by a device equipped with a built-in wireless LAN module and capable of connecting to a network, as well as external information collected by sensors installed in the device." For example, log information 31 is information transmitted from IoT device 61 and is source or source information for estimating the state and situation of the land at the survey location where IoT device 61 is located as local situation information. For example, log information 31 is information indicating the normal or abnormal state of IoT device 61. Furthermore, log information 31 is information indicating the operating state of IoT device 61 (on / off signals, signals indicating normal operation, etc.) or the state and behavior of its components (open / close signals, signals indicating rising or falling, etc.). Furthermore, log information 31 is information indicating the physical state or material detection state detected by sensors in IoT device 61 (such as information indicating the presence or absence of flooding detected by a flood sensor or information indicating the detection of water on the floor by a thermal camera).

[0034] <Movement Analysis Processing: Step S103> The movement analysis unit 130 analyzes the log information 31 and generates the movement of things at the survey point where the IoT device 61 is located as local movement information 54. The movement analysis unit 130 may acquire sensor data 32 from the IoT device 61, analyze the log information 31 and the sensor data 32, and generate the local movement information 54. If the IoT device 61 is an elevator, monitoring video transmitted from a monitoring camera attached to the elevator becomes the sensor data 32. If the IoT device 61 is an elevator, door opening / closing signals and the like become the log information 31, and the monitoring video becomes the sensor data 32. If the IoT device 61 is an air conditioner, an infrared image transmitted from an infrared camera attached to the air conditioner becomes the sensor data 32. If the IoT device 61 is an air conditioner, on / off signals and the like become the log information 31, and the infrared image becomes the sensor data 32.

[0035] The activity analysis unit 130 analyzes the presence or absence of people at the survey location where the IoT device 61 is located from log information 31, such as on / off signals of an air conditioner, an open / close signal of a refrigerator, or an on / off signal of a television. The activity analysis unit 130 also analyzes the activity of people, such as what they are doing, from surveillance camera footage. Alternatively, the activity analysis unit 130 may analyze the temperature around people from infrared images. The activity analysis unit 130 generates local activity information 54 that indicates the status or activity of people in the vicinity of the survey location where the IoT device 61 is located by combining and analyzing the log information 31 and the sensor data 32.

[0036] In this embodiment, a person has been described as an example of an object whose behavior is to be analyzed, but the behavior of an object other than a person may also be analyzed. For example, the behavior of an animal such as a pet may be analyzed. Alternatively, the behavior of a machine in operation may be analyzed. In addition, the behavior of any object whose behavior can be analyzed may be analyzed.

[0037] Furthermore, the activity analysis unit 130 may transmit the activity of the person included in the local activity information 54 to the user as monitoring information 541. Here, the user who receives the monitoring information 541 is a user who has been pre-registered as a user who will receive the monitoring information 541. Specifically, the monitoring information 541 including the local activity information 54 of a family member living in a building at the survey location is transmitted to a pre-registered member of the family. More specifically, the monitoring information 541 including the local activity information 54 of a child, elderly person, pet, or the like is transmitted to the guardian, manager, or the like of the family member. Alternatively, the monitoring information 541 including the local activity information 54 of a user who uses a facility is transmitted to a pre-registered manager, or the like of the facility.

[0038] The local information estimation unit 120 acquires multiple pieces of log information 31 from multiple IoT devices 61 installed at multiple investigation points. The local information estimation unit 120 also acquires multiple types of log information 31 from multiple types of IoT devices 61. The dynamic analysis unit 130 also acquires multiple pieces of sensor data 32 from multiple IoT devices 61 installed at multiple investigation points. The dynamic analysis unit 130 also acquires multiple types of sensor data 32 from multiple types of IoT devices 61. In this embodiment, the local information estimation unit 120 and the dynamic analysis unit 130 are described as separate components. However, the functions of the local information estimation unit 120 and the dynamic analysis unit 130 may be realized by a single component.

[0039] As shown in FIG. 3 , specific examples of the log information 31 and the sensor data 32 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 a local area. (5) Building sensing data is data acquired by a building condition sensor installed inside a building. For example, the 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 a sensor installed in a home appliance 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.

[0040] <High Accuracy Processing: Steps S104 to S108> In step S104, the high accuracy unit 140 determines whether or not a disaster has been detected in the survey area. If a disaster has not been detected, the process proceeds to step S105. If a disaster has been detected, the process proceeds to step S106.

[0041] The occurrence of a disaster may be detected by the land use classification unit 110 from the remote sensing image 52. Alternatively, the remote sensing image 52 may be assigned an indication of whether it is an image taken at the time of a disaster or an image taken during normal times. Alternatively, the land use classification unit 110 may determine whether a disaster has occurred based on whether the source of the remote sensing image 52 is a cloud taken during a disaster or a cloud taken during normal times. Any other method may be used to detect the occurrence of a disaster in the survey area.

[0042] <<High-Accuracy Processing in Peacetime>> Step S105 is a processing performed when no disaster is detected in the survey area, i.e., during peacetime. In step S105, the accuracy improvement unit 140 estimates the land conditions and situations at points other than the survey point in the survey area based on the remote sensing image 52 and the local situation information 53, and generates area condition information 55. The area condition information 55 represents the land conditions and situations in the survey area with greater accuracy than the label information 511. The accuracy improvement unit 140 may also generate area condition information 55 including the movements of people at the survey point based on the remote sensing image 52, the local situation information 53, and the local activity information 54. The accuracy improvement unit 140 may transmit the area condition information 55 to a user pre-registered as a user who will receive the area condition information 55. Note that if no disaster is detected, the accuracy improvement unit 140 does not need to transmit the area condition information 55.

[0043] The precision improving unit 140 re-estimates the state and situation of the land at points other than the survey points where each of the IoT devices 61 is located, based on the remote sensing image 52, the local situation information 53, and the local activity information 54. In this way, the precision improving unit 140 can interpolate the state and situation of the land at points other than the survey points where each of the IoT devices 61 is located, based on the remote sensing image 52, the local situation information 53, and the local activity information 54, and generate highly accurate area state information 55.

[0044] <<High Accuracy Processing in the Event of a Disaster>> Steps S106 to S108 are processing performed when a disaster is detected in the survey area, i.e., when a disaster occurs. In step S106, the accuracy improving unit 140 generates area state information 55 based on the remote sensing image 52, the local situation information 53, and the local activity information 54. The generation of the area state information 55 is similar to step S104. Here, the accuracy improving unit 140 generates area state information 55 including the activity of people at the survey point based on the remote sensing image 52, the local situation information 53, and the local activity information 54. At this time, the accuracy improving unit 140 generates area state information 55 including delayed evacuation information 551 indicating whether anyone was unable to escape at the survey point.

[0045] The precision improvement unit 140 transmits the area state information 55 to a user who has been registered in advance as a user who will receive the area state information 55. Here, the user who has been registered in advance as a user who will receive the area state information 55 is, for example, a government, a local government, a rescue team such as a fire department, or a police department.

[0046] In step S107, the accuracy improving unit 140 notifies the movement analysis unit 130 that the late-escape information 551 has been generated. In step S108, the movement analysis unit 130 receives notification from the accuracy improving unit 140 that the late-escape information 551 has been generated. The movement analysis unit 130 acquires the late-escape information 551 and adds the late-escape information 551 to the watching information 541. The movement analysis unit 130 transmits the watching information 541 with the late-escape information 551 added to it to the user. Here, the user to whom the watching information 541 with the late-escape information 551 added is sent is a user who has been registered in advance as a user who will receive the watching information 541. In other words, this is a user such as a family member, guardian, or administrator of the person being watched over.

[0047] The accuracy improving unit 140 may generate the area status information 55 when some event occurs in the survey area. Specifically, the accuracy improving unit 140 may generate the area status information 55 when a disaster occurs in the survey area, i.e., when a disaster is detected. Alternatively, the accuracy improving unit 140 may generate the area status information 55 periodically or irregularly. Furthermore, the accuracy improving unit 140 may generate the area status information 55 in response to a request from a user.

[0048] The precision improving unit 140 generates area status information 55 that accurately represents the state and condition of the land by combining the land coverage status obtained from the remote sensing image 52 with the log information 31 and sensor data 32 transmitted from the IoT device 61. The area status information 55 includes information indicating the land coverage status and the status and condition of buildings or houses in the survey area. The area status information 55 also includes information indicating the movement of monitored objects, such as people, animals, or operating machines, in the survey area. The area status information 55 also includes information indicating changes in the land status or human movement, which is obtained by combining, in chronological order, the classification results of the land status and condition with the log information 31 and sensor data 32 acquired at a different data acquisition timing than the time of classification.

[0049] The land use classification unit 110 may acquire the area status information 55 and reset the label information 511 based on the remote sensing image 52 and the area status information 55. The reset label information 511 further subdivides and details classifications such as buildings, water bodies, bare land, and sediment with higher 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 from 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 from statistics of wavelength information of optical images. Yet another example includes classifications such as water bodies, fields, forests, urban areas, farmland, and snow cover determined from polarization information and coherence information of SAR images.

[0050] <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. 4 to Fig. 8. In Fig. 4 to Fig. 8, remote sensing images may be abbreviated as remote sensing or remote sensing.

[0051] 4 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.

[0052] In Fig. 4, a remote sensing image 52 is shown in the upper row, and log information 31 and sensor data 32 are shown in the lower row. In Fig. 4, 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. In addition, sensor devices provided in the IoT device 61 include sensor D, which is a drive recorder, sensor E, which is a security camera, and sensor F, which is an IoT home appliance. In other words, sensors D, E, and F are examples of IoT devices 61.

[0053] <<Spatial Interpolation of Observations of the Target Area Using Sensor Data>> In each of processes 1 to 6 in FIG. 4 , the land status assessment device 100 observes the survey area 62, 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 the 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 or 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 the 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 the SAR images. In this case, the land status assessment device 100 acquires log information and sensor data from IoT devices within the observation range of the remote sensing images. The log information and sensor data from the IoT devices are locally accurate information, although the range is narrow in the vicinity of the survey point 63. The land status assessment device 100 uses the log information and sensor data to interpolate the observation information of the remote sensing image. Specifically, the land status assessment device 100 uses the log information and sensor data to estimate the state and situation of the land in the survey area 62 other than the survey point 63. This allows the land status assessment device 100 to generate area status information 55 that reflects the highly accurate log information and sensor data, thereby expanding the observation range with high accuracy.

[0054] <<Spatial and Temporal Interpolation of Observations of the Target Area Using Sensor Data>> In each of processes 1 to 6 in FIG. 4 , the land status assessment device 100 combines the damage situation or damage state with log information and sensor data in a chronological order. Specifically, the land status assessment device 100 chronologically combines the damage situation or damage state of specific areas, buildings, and houses with log information and sensor data acquired at different data acquisition timings than the land status and situation classification results. In this way, the land status assessment device 100 acquires changes in the damage situation or damage state. In this process, the land status assessment device 100 uses log information and sensor data from IoT devices within the observation range of the remote sensing image. Although the log information and sensor data from the IoT devices are narrow in range, they have high local accuracy and can acquire information more frequently than remote sensing images. The land status assessment device 100 uses this log information and 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 area status information 55 that reflects highly accurate and frequent log information and sensor data information, thereby enabling the observation range to be expanded with high accuracy.

[0055] Note that processes 1 to 6, which are labeled "label update" in Fig. 5, are executed, for example, by the precision improvement unit of the land status assessment device. In each of processes 1 to 6, the precision improvement 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 precision improvement 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.

[0056] An example of a proactive approach is an automated case where the process is completed within the land condition assessment device. The land condition assessment device pre-specifies a specific location or state and monitors the situation or state. If there is a change in the monitored information, the land condition assessment device will acquire the situation, state, or information of the location using a more granular remote sensor or sensor.

[0057] 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, or 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.

[0058] For example, remote sensors B and C or sensors D, E, and F may be selected depending on the purpose of use of the area status information 55. Specific examples of purposes of use include planning evacuation routes from disaster areas, predicting changes in disaster conditions, and notifying disaster victims of these changes. In addition, users such as the government, local governments, fire departments, and police may plan rescue team dispatch routes or determine rescue urgency levels.

[0059] An example of the land status grasping process will be described in more detail below. Note that in Figures 5 to 8 described below, sensor F, an IoT home appliance, is shown as an example of the sensor that acquires log information and sensor data. This is just one example, and the present embodiment can be applied even if sensor F is replaced with sensor D or sensor E.

[0060] <Example 1 of Land Status Grasping Processing> FIG. 5 is a schematic diagram showing Example 1 of land statusgrasping processing in a survey area according to this embodiment. In Example 1 of land statusgrasping 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 local information estimation unit 120 acquires log information from sensor F and generates local status information. The activity analysis unit 130 acquires log information and sensor data from sensor F and generates local activity information. The accuracy improvement unit 140 generates area state information 55 using remote sensing image A, the local status information, and the local activity information. The land use classification unit 110 may update the label information using the area state information 55.

[0061] 6 is a diagram showing a process in which interpolation is performed using log information and sensor data from sensor F in example 1 of the land status assessment process according to this embodiment. The precision improving unit 140 estimates the state and condition of the land at points other than the survey point from local situation information and local activity information based on the log information and sensor data from sensor F, and generates area status information 55. Alternatively, the precision improving unit 140 may estimate the land coverage and environmental state in an area between multiple ground sensors based on multiple sensor data, and generate area status information 55.

[0062] The land status assessment device 100 interpolates the observation information of remote sensing A using log information and sensor data from sensor F 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 from sensor F.

[0063] Furthermore, the land status assessment device 100 continuously interpolates the observation information of remote sensing A spatially and temporally within the observation range of remote sensing A using log information and sensor data from sensor F. This enables the land status assessment device 100 to expand the observation range based on the highly accurate information from sensor F while updating the log information and sensor data from sensor F.

[0064] <<Spatial Edition (Remote Sensing A + Sensor F)>> (Disaster Response) This section describes an example in which, during a disaster, the land situation assessment device 100 uses log information and sensor data from Sensor F to spatially interpolate area status information 55 with high accuracy. The land situation assessment device 100 combines the log information and sensor data from Sensor F with the low-accuracy information from Remote Sensing A, based on information previously analyzed and classified into a land use map of the land's state and condition, and the observation information from Remote Sensing A. This interpolates the observation information from Remote Sensing A. This allows the land situation assessment device 100 to comprehensively assess a wide range of disaster situations, including building damage, fires, flooding, and landslides, early in the event of a disaster. The land situation assessment device 100 can also generate information on people who are late in evacuating, contributing to rescue efforts and other activities.

[0065] <<Space & Time Edition (Remote Sensing A + Sensor F) & Time>> (Disaster Response) This section describes an example in which, during a disaster, the land status assessment device 100 uses log information and sensor data from Sensor F to spatially and temporally interpolate area status information 55 with high accuracy. The land status assessment device 100 continuously acquires log information and sensor data from Sensor F in relation to information previously analyzed for land status and conditions and classified into a land use map, as well as observation information from Remote Sensing A. The land status assessment device 100 then combines the continuously acquired log information and sensor data from Sensor F with the less accurate 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 conditions, including building damage, fires, flooding, and landslides, and their changes, early in the course of a disaster. The land status assessment device 100 can also generate information on people who are late in evacuating, contributing to rescue efforts and other activities.

[0066] 7 is a schematic diagram showing Example 2 of the land status assessment process for a survey area according to this embodiment. Here, an example will be described in which area status information 55 is interpolated with high accuracy using remote sensing images A and B and sensor F.

[0067] The land status assessment device 100 performs observation by remote sensing B based on the observation information of remote sensing A interpolated with log information and sensor data from 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 log information and sensor data from sensor F and remote sensing B can improve the accuracy of the observation range.

[0068] <<Spatial Edition (Remote Sensing A + Remote Sensing B + Sensor F)>> (Disaster Response) This section describes an example in which, during a disaster, the land situation assessment device 100 uses log information and sensor data from Sensor F to spatially interpolate area status information 55 with high accuracy. In order to identify areas with high priority for evacuation and rescue of 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 in a land use map and observation information from Remote Sensing A interpolated using log information and sensor data from Sensor F. Remote Sensing B has a narrower observation range than Remote Sensing A but can acquire information with higher accuracy, such as aerial 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 grasp areas with high priority for evacuation and rescue of victims, as well as the type of damage and damage situation, in various disaster situations such as building damage, fire, flooding, landslide, etc., using a highly accurate sensor F and remote sensing B. The land situation assessment device 100 can also generate information on people who are late in evacuating, thereby contributing to rescue activities, etc.

[0069] <<Space & Time Edition (Remote Sensing A + Remote Sensing B + Sensor F) & Time>> (Disaster Response) This section describes an example in which, during a disaster, the land situation assessment device 100 uses log information and sensor data from Sensor F to spatially and temporally interpolate area status information 55 with high accuracy. In order to consistently identify areas with high priority for evacuation and rescue of victims and the type of damage from a comprehensive assessment of the damage situation early on in the disaster, 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 in a land use map and observation information from Remote Sensing A interpolated using log information and sensor data from 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 in the damage situation in a variety of disaster situations such as building damage, fire, flooding, landslides, etc., using a highly accurate sensor F and remote sensing B. The land situation assessment device 100 can also generate information on people who are late in evacuating, thereby contributing to rescue activities, etc.

[0070] 8 is a schematic diagram showing Example 3 of the land status assessment process for a survey area according to this embodiment. Here, an example will be described in which area status information 55 is interpolated with high accuracy using remote sensing images A, B, and C and sensor F.

[0071] 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 by 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 make it possible to increase the accuracy of the observation range.

[0072] 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.

[0073] <<Spatial Edition (Remote Sensing A + Remote Sensing B + Remote Sensing C + Sensor F)>> (Disaster Response) This section describes an example in which, during a disaster, the land situation assessment device 100 uses log information and sensor data from Sensor F to spatially interpolate area status information 55 with high accuracy. 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 in advance based on an analysis of the state and condition of the land, and on 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 can acquire information with higher accuracy, such as drone imaging. Through such observations, the land situation assessment device 100 interpolates observation information obtained by combining 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.

[0074] <<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 log information and sensor data from Sensor F to spatially and temporally interpolate area status information 55 with high accuracy. During a disaster, if highly accurate observation information is continuously required to grasp specific information necessary for the movement and rescue operations of rescue teams from police and fire departments heading to the disaster site based on the damage 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 the state and condition of the land in advance, and the 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.

[0075] <Example 4 of Land Status Assessment Processing> <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 log information and sensor data from sensor F. The land status assessment device 100 improves observation accuracy and expands the observation range by combining remote sensing images and log information and sensor data from sensor F according to the use, purpose, situation, etc. This makes it possible to update land use labels with high accuracy and reliability based on a land use map that can be used for adaptively assessing the disaster situation and for disaster response.

[0076] <Space & Time Edition> The land status assessment device 100 updates labels based on time-series remote sensing images and time-series log information and sensor data from sensor F. The land status assessment device 100 combines remote sensing images and log information and sensor data from sensor F 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.

[0077] ***Other Configurations*** <Variation 1> 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 survey area. The label information 511 may be associated with the land use map 51.

[0078] In a first modification of this embodiment, the land use classification unit 110 may update the land use map 511 based on the remote sensing image 52 and the area status information 55. In a first modification of this embodiment, the land use classification unit 110 may execute a map update process to update the land use map 511 based on the remote sensing image 52 and the area status information 55. Other processes are the same as those in FIG. 2 .

[0079] 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 area status information 55, reset the label information 511 based on the remote sensing image 52 and the area status information 55, and update the land use map 511.

[0080] <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 the response in disasters. The land status ascertainment process in peacetime may differ from that in disasters 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.

[0081] The land status assessment process corresponding to Figures 5 and 6 can also be applied in peacetime. Within 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 local accuracy, 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. At this time, the precision improvement unit 140 generates area status information 55 including the movement of people at the survey location based on the log information and sensor data from remote sensing A and sensor F. In addition, the movement analysis unit 130 transmits the movement of people included in the local movement information obtained from the log information and sensor data from sensor F as monitoring information to a user, such as the guardian of the person being monitored.

[0082] <Spatial Edition (Remote Sensing A + Sensor F)> (Normal Operation) This observation is as follows. The land status assessment device 100 interpolates the observation information of remote sensing A by combining the log information and sensor data from Sensor F with the low-accuracy information from Remote Sensing A, in relation to the information classified in the land use map and the observation information of Remote Sensing A. Sensor F is, for example, an IoT home appliance. 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, such as changes, weathering, deterioration, and damage. The land status assessment device 100 is also configured to transmit the movement of people included in the local movement information as monitoring information to a user, such as the guardian of the person being monitored.

[0083] <Space & Time Edition (Remote Sensing A + Sensor F) & Time> (Normal Operation) The land status assessment device 100 constantly acquires log information and sensor data from Sensor F in response to information classified in a land use map and observation information from Remote Sensing A. The land status assessment device 100 then combines the information with low-precision information from Remote Sensing A and continuously interpolates and updates the observation information from Remote Sensing A spatially and temporally. This allows the land status assessment device 100 to comprehensively assess the status and state of land, rivers, ports, facilities, and buildings, including changes, weathering, deterioration, and damage, as well as their changes. The land status assessment device 100 also continuously transmits monitoring information on the movements of people included in the local movement information to users, such as the guardians of those being monitored.

[0084] The land situation grasping process corresponding to FIG. 7 can also be applied in normal times.

[0085] <Spatial Edition (Remote Sensing A + Remote Sensing B + Sensor F)> (Normal Response) The land status assessment device 100 performs observations using remote sensing B based on the observation information of remote sensing A interpolated using information classified in a land use map, log information from sensor F, and sensor data. Through such observations, the land status assessment device 100 interpolates the observation information obtained by combining sensor F with remote sensing A and remote sensing B. In this way, the land status assessment device 100 is configured to assess 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. The land status assessment device 100 is also configured to transmit the movement of people included in the local movement information as monitoring information to a user, such as the guardian of the person being monitored.

[0086] <Space & Time Edition (Remote Sensing A + Remote Sensing B + Sensor F) & Time> (Normal Operation) The land status assessment device 100 performs observations using remote sensing B based on information classified in a land use map, log information from Sensor F, and observation information from Remote Sensing A interpolated with sensor data. Through such observations, the land status assessment device 100 performs spatial and temporal interpolation of observation information obtained by combining Sensor F with Remote Sensing A and Remote Sensing B. The land status assessment device 100 is configured to adaptively assess the status and state of land, rivers, ports, facilities, and buildings, including changes, weathering, deterioration, and damage, as well as changes therein, using highly accurate Sensor F and Remote Sensing B. The land status assessment device 100 is also configured to continuously transmit the movements of people included in the local movement information to users, such as the guardians of the people being monitored, as monitoring information.

[0087] The land situation grasping process corresponding to FIG. 8 can also be applied in normal times.

[0088] <Spatial Edition (Remote Sensing A + Remote Sensing B + Remote Sensing C + Sensor F)> (Normal Operation) This observation is as follows. The land status assessment device 100 performs observation using remote sensing C based on observation information from remote sensing A and remote sensing B, which is interpolated using information classified in a land use map, log information from sensor F, and sensor data. Through this observation, the land status assessment device 100 interpolates observation information obtained by combining sensor F, remote sensing A, remote sensing B, and remote sensing C. In this way, 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, from the entire object to specific areas, using highly accurate sensor F and remote sensing C. The land status assessment device 100 is also configured to transmit the movement of people included in the local movement information as monitoring information to a user, such as the guardian of the person being monitored.

[0089] <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 observation using remote sensing C based on the observation information from remote sensing A and remote sensing B, which are interpolated using information classified in a land use map, log information from sensor F, and sensor data. Through this observation, 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. In this way, 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, from the entire object to specific areas, including changes over time, using highly accurate sensor F and remote sensing C. The land status assessment device 100 is also configured to continuously transmit the human behavior included in the local behavior information to a user, such as the guardian of the person being monitored, as monitoring information.

[0090] <Modification 3> In this embodiment, the functions of the land use classification unit 110, the local information estimation unit 120, the dynamic analysis unit 130, and the accuracy improvement unit 140 are realized by software. As a modification, the functions of the land use classification unit 110, the local information estimation unit 120, the dynamic analysis unit 130, and the accuracy improvement unit 140 may be realized by hardware. Specifically, the land status assessment device 100 includes an electronic circuit 909 instead of the processor 910.

[0091] FIG. 9 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 local information estimation unit 120, the dynamic analysis unit 130, and the accuracy improvement 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.

[0092] The functions of the land use classification unit 110, the local information estimation unit 120, the dynamic analysis unit 130, and the precision improvement unit 140 may be realized by a single electronic circuit, or may be realized by distributing them across multiple electronic circuits.

[0093] As another modification, some of the functions of the land use classification unit 110, the local information estimation unit 120, the dynamic analysis unit 130, and the accuracy improvement 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 local information estimation unit 120, the dynamic analysis unit 130, and the accuracy improvement unit 140 may be realized by firmware.

[0094] 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 local information estimation unit 120, the dynamic analysis unit 130, and the accuracy improvement unit 140 are realized by the processing circuitry.

[0095] <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.

[0096] ***Description of Effects of the Present Embodiment*** The effects of the present embodiment will be described. The land status assessment device 100 according to this embodiment combines a land use map obtained from the analysis results of remote sensing images with log information and sensor data from multiple IoT devices to grasp the status and changes of land cover over a wide area and in detail. The local information estimation unit estimates the normality or abnormality of each device in a device group consisting of one or more IoT devices present at a survey location based on log signals indicating normality or abnormality corresponding to each device in the device group. The accuracy improvement unit estimates information indicating land use information for locations other than the survey location using local land use information at the survey location and land use information corresponding to the survey area acquired by remote sensing. The accuracy improvement unit then re-estimates land use information for the survey area with high accuracy based on the local land use information at the survey location, land use information corresponding to the survey area acquired by remote sensing, and information indicating land use information for locations other than the survey location. As described above, the land status determination device 100 according to this embodiment can estimate information other than the survey point and improve the accuracy of the survey area by linking the log information and sensor data of the survey point with the remote sensing data. The land status determination device 100 according to this embodiment can obtain highly accurate land use information by linking and aggregating the log information and sensor data of the survey point with the remote sensing data.

[0097] 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.

[0098] 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.

[0099] 31 Log information, 32 Sensor data, 51 Land use map, 52 Remote sensing image, 53 Local situation information, 54 Local activity information, 55 Area status information, 61 IoT device, 62 Survey area, 63 Survey point, 100 Land situation assessment device, 110 Land use classification unit, 120 Local information estimation unit, 130 Activity analysis unit, 140 High accuracy improvement unit, 150 Memory unit, 511 Label information, 541 Monitoring information, 551 Late escape 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 situation assessment device comprising: a land use classification unit that analyzes remote sensing images and sets label information that represents the land coverage status of a survey area; a local information estimation unit that receives log information transmitted from IoT devices (Internet of Things devices) installed in the survey area and estimates the land condition and status of the survey point where the IoT device is located as local situation information based on the log information; and a high accuracy improvement unit that estimates the land condition and status at points other than the survey point in the survey area based on the remote sensing images and the local situation information, and generates area condition information that represents the land condition and status of the survey area with higher accuracy than the label information.

2. The land situation assessment device of claim 1, further comprising a movement analysis unit that analyzes the log information and generates local movement information on the movements of people at the survey location, and the high accuracy improvement unit generates the area status information including the movements of people at the survey location based on the remote sensing image, the local situation information, and the local movement information.

3. The land situation assessment device of claim 2, wherein the high-precision unit, upon detecting a disaster in the survey area, generates the area status information including information on people who were unable to escape in time based on the remote sensing image, the local situation information, and the local activity information.

4. The land status grasping device according to claim 3, wherein the high accuracy unit transmits the area status information to a user who has been pre-registered as a user who will receive the area status information.

5. A land situation assessment device as described in claim 3 or claim 4, wherein the movement analysis unit transmits the movement of people contained in the local movement information as monitoring information to a user who has been pre-registered as a user to receive the monitoring information.

6. The land situation assessment device described in claim 5, wherein the dynamic analysis unit acquires the delayed escape information, adds the delayed escape information to the monitoring information, and transmits the monitoring information to a user who has been pre-registered as a user who will receive the monitoring information.

7. A land status assessment device as described in any one of claims 2 to 6, wherein the dynamic analysis unit acquires sensor data from a sensor device provided in the IoT device, analyzes the log information and the sensor data, and generates the local dynamic information.

8. A land condition assessment device according to any one of claims 1 to 7, wherein the IoT device is at least one of an IoT home appliance and an elevator.

9. A method for understanding land conditions, in which a computer analyzes remote sensing images to set label information representing the land coverage status of a survey area, the computer receives log information transmitted from IoT devices (Internet of Things devices) installed in the survey area, and estimates the state and status of the land at the survey point where the IoT device is located as local situation information based on the log information, and the computer estimates the state and status of the land at points other than the survey point in the survey area based on the remote sensing images and the local situation information, and generates area condition information representing the state and status of the land in the survey area with higher accuracy than the label information.

10. A land situation assessment program that causes a computer to perform the following steps: a land use classification process that analyzes remote sensing images and sets label information that represents the land coverage status of a survey area; a local information estimation process that receives log information transmitted from IoT devices (Internet of Things devices) installed in the survey area and estimates the land status and status of the survey point where the IoT device is located as local situation information based on the log information; and a high-precision process that estimates the land status and status at points in the survey area other than the survey point based on the remote sensing images and the local situation information, and generates area status information that represents the land status and status of the survey area with higher precision than the label information.

Images