Economical loss monitoring apparatus based on synthetic aperture radar image analysis

Abstract

To provide an apparatus which can calculate economical losses due to disasters in real time.SOLUTION: An economical loss monitoring apparatus acquires ground observation data by a synthetic aperture radar (SAR) mounted on an artificial satellite in observation data acquisition processing 2. The apparatus specifies a submerged area based on the change of the observation data in submerged area specification processing 4. Further, the apparatus acquires topographic data (such as three-dimensional point group data) around the submerged area in topographic data acquisition processing 6. The apparatus overlays the submerged area on this topographic data, and estimates the submergence depths in respective points (areas) within the submerged areas. The apparatus executes the estimation of the amount of damage based on the submergence depths in the respective areas in economical loss calculation processing 10.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] This invention relates to a technique for estimating economic losses due to flooding, landslides, and the like. [Background technology]

[0002] Economic losses due to disasters such as tsunamis are estimated for the purpose of decision-making regarding countermeasures in the event of a disaster by the state, local governments, businesses, etc. For example, Patent Document 1 discloses a method in which the amount of damage for each area according to the type of disaster is recorded in advance, and economic losses are calculated based on this.

[0003] This allows decisions on countermeasures to be made based on predictions of economic losses.

[0004] Furthermore, in relation to disasters, efforts are being made to efficiently obtain the inundation depth in the event of a tsunami, flood, etc. For example, Patent Document 2 discloses a method for estimating the inundation depth at unmeasured points based on the inundation depth at multiple points measured in a field survey. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent Publication 2018-147495 [Patent Document 2] Patent Publication No. 2008-310036 Summary of the Invention [Problem to be solved by the invention]

[0006] However, in conventional technologies such as that described in Patent Document 1, the operator must input the fact of the occurrence of a disaster and the type of disaster, making it difficult to calculate economic losses in real time when a disaster occurs.

[0007] Furthermore, in conventional techniques such as those in Patent Document 2, on-site surveys were required to calculate the flood depth, which was time-consuming and cumbersome.

[0008] An object of the present invention is to provide an apparatus that can solve any of the above problems and calculate economic loss in real time or that can quickly estimate flood depth. [Means for solving the problem]

[0009] Independently applicable features of the present invention are listed below.

[0010] (1)(2)(3) The economic loss monitoring device of this invention acquires ground observation data using a synthetic aperture radar, identifies areas submerged due to a disaster based on changes in the observation data over time, acquires topographical data under normal conditions around the submerged areas, estimates the submergence depth of the submerged areas identified based on the observation data using the synthetic aperture radar using the edge of the submerged areas as a reference and by referring to the topographical data, and calculates economic loss based on the estimated submergence depth.

[0011] Therefore, the amount of damage caused by flooding can be estimated accurately in real time.

[0012] (4) The economic loss monitoring device according to the present invention is characterized in that the topographical data is three-dimensional point cloud data or a digital topographical model.

[0013] Therefore, the amount of damage caused by flooding can be estimated with high accuracy based on this data.

[0014] (5) The economic loss monitoring device of the present invention is characterized in that the economic loss is direct damage in the flooded area or indirect damage to locations related to the flooded area, or both.

[0015] Therefore, both direct and indirect damages can be calculated.

[0016] (6) The economic loss monitoring device according to the present invention is characterized in that the estimation of the submergence depth is performed for each of a plurality of sections into which the flooded area is divided.

[0017] Therefore, the amount of damages for each category can be calculated.

[0018] (7) The economic loss monitoring device according to the present invention is characterized in that it is constructed as a server device accessible from a terminal device via a network.

[0019] Therefore, the amount of damage can be known by accessing from a terminal device.

[0020] (8) The economic loss monitoring device according to the present invention is characterized in that the observation data is backscattered reflection intensity.

[0021] Therefore, the flooded area can be estimated based on the change in reflection intensity.

[0022] (9) The economic loss monitoring device of the present invention is characterized in that if an area adjacent to a submerged area identified based on observation data has an elevation lower than that of the submerged area, the adjacent area is also considered to be a submerged area.

[0023] Therefore, the submerged area can be identified more accurately.

[0024] (10)(11)(12) The submergence degree estimation device of the present invention acquires ground observation data by a synthetic aperture radar, identifies areas submerged due to a disaster based on changes in the observation data over time, acquires topographical data under normal conditions in the vicinity of the submerged areas, and estimates the submergence depth of the submerged areas identified based on the observation data by the synthetic aperture radar by using the edges of the submerged areas as a reference and referring to the topographical data.

[0025] Therefore, the degree of submersion can be estimated with high accuracy.

[0026] (13) The submergence degree estimation device according to the present invention is characterized in that the topographical data is three-dimensional point cloud data or a digital topographical model.

[0027] Therefore, the degree of submersion can be estimated with high accuracy based on this data.

[0028] (14) The submersion estimation device according to the present invention is characterized in that the observation data is backscattered reflection intensity.

[0029] Therefore, the flooded area can be estimated based on the change in reflection intensity.

[0030] (15)(16)(17) The economic loss monitoring device of this invention acquires ground observation data by a synthetic aperture radar, identifies a change area based on changes in the observation data over time, acquires map data at least in the vicinity of the change area, estimates the extent of damage to the change area identified based on the observation data by the synthetic aperture radar by referring to the map data, and calculates economic loss based on the estimated extent of damage.

[0031] Therefore, the amount of damage caused by a disaster can be estimated accurately in real time.

[0032] (18) The economic loss monitoring device according to the present invention is characterized in that the map data is information that distinguishes at least buildings, roads, and railroads.

[0033] Therefore, the amount of damage caused by a disaster can be estimated with high accuracy based on this data.

[0034] (19) The economic loss monitoring device according to the present invention is characterized in that the economic loss is direct damage in a disaster area or indirect damage in an area related to the disaster area, or both.

[0035] Therefore, both direct and indirect damages can be calculated.

[0036] (20) The economic loss monitoring device according to the present invention is characterized in that the extent of the disaster is estimated for each of a plurality of sections into which the disaster area is divided.

[0037] Therefore, the amount of damages for each category can be calculated.

[0038] (21) The economic loss monitoring device according to the present invention is characterized in that it is constructed as a server device accessible from a terminal device via a network.

[0039] Therefore, the amount of damage can be known by accessing from a terminal device.

[0040] (22) The economic loss monitoring device of the present invention is characterized in that the disaster area is a submerged area and the extent of the disaster is the submerged depth.

[0041] Therefore, the amount of damage caused by flooding can be estimated.

[0042] (23) The economic loss monitoring device according to the present invention is characterized in that the observation data is backscattered reflection intensity.

[0043] Therefore, the flooded area can be estimated based on the change in reflection intensity.

[0044] The term "apparatus" is a concept that includes not only one consisting of one computer, but also one consisting of multiple computers connected via a network, etc. Therefore, when the means of the present invention (or a part of the means) is distributed among multiple computers, these multiple computers correspond to the apparatus.

[0045] The term "program" is a concept that includes not only programs that can be executed directly by a CPU, but also programs in source format, compressed programs, encrypted programs, programs that work in conjunction with an operating system to perform their functions, and the like. [Brief description of the drawings]

[0046] [Figure 1] 1 is a functional configuration of an economic loss monitoring device according to an embodiment of the present invention. [Diagram 2] 1 shows the system configuration of an economic loss monitoring system. [Diagram 3] 1 shows the hardware configuration of an economic loss monitoring server device S. [Figure 4] This shows the hardware configuration of terminal device T. [Diagram 5] 1 is a flow chart of the economic loss monitoring program. [Figure 6] Figure 6A is a backscattering intensity image in normal times, Figure 6B is a backscattering intensity image in the event of a disaster, and Figure 6C is a difference image. [Figure 7A] FIG. 2 is a diagram for explaining forward scattering and backward scattering. [Figure 7B] FIG. 1 is a diagram showing the difference in backscattering between a forest, a grassland, and a lake surface. [Figure 8] FIG. 1 illustrates the identification of a flooded area 70 and its edge 72. [Figure 9] FIG. 13 is a diagram showing a method for calculating flood depth using three-dimensional point cloud data. [Figure 10] FIG. 1 is a diagram showing a flooded area 70 divided into sections. [Figure 11] 13 is a graph showing the relationship between flood depth and number of days of business interruption. [Figure 12] 1 is a map showing estimated damages by region. [Figure 13] This is a map including indirect damage amounts over a wide area. [Figure 14] FIG. 13 is a diagram showing the relationship between flood depth and damage rate. [Figure 15]FIG. 13 is a diagram showing the relationship between flood depth and damage rate. [Figure 16] FIG. 13 is a diagram showing the relationship between flood depth and damage rate. [Figure 17] FIG. 13 is a diagram showing the relationship between flood depth and damage rate. [Figure 18] FIG. 13 is a diagram showing the relationship between flood depth and damage rate. [Figure 19] 13 is a functional configuration of an economic loss monitoring device according to a second embodiment. [Figure 20] 1 is a flow chart of the economic loss monitoring program. [Figure 21] FIG. 13 is a diagram showing a schematic diagram of calculation of a displacement change by differential interferometry. [Figure 22] FIG. 13 is a diagram showing a method for calculating the magnitude of displacement due to an accumulation of phase differences. [Diagram 23] 11A and 11B are diagrams for explaining a process for expanding a submerged area. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0047] 1. First embodiment 1.1 Overall structure Fig. 1 shows the functional configuration of an economic loss monitoring device according to an embodiment of the present invention. In observation data acquisition process 2, the economic loss monitoring device acquires the intensity of backscattering observed on the ground by a synthetic aperture radar (SAR) mounted on an artificial satellite. In submerged area identification process 4, a submerged area is identified based on the change in the intensity of backscattering.

[0048] Furthermore, topographical data (such as three-dimensional point cloud data) of the surroundings of the submerged area is acquired in a topographical data acquisition process 6. The submerged area is superimposed on this topographical data, and the submerged depth at each point (area) within the submerged area is estimated.

[0049] In the economic loss calculation process 10, the amount of damage is estimated based on the submerged depth at each point, etc.

[0050] As described above, the submergence depth is estimated based on the observation data from the synthetic aperture radar and the topographical data, and the economic loss is estimated based on this. Therefore, the economic loss due to submergence can be calculated in real time.

[0051] 1.2 System configuration and hardware configuration FIG. 2 shows the configuration of an economic loss monitoring system in which an economic loss monitoring device is constructed as a server device S.

[0052] The economic loss monitoring server device S is provided so as to be able to communicate with the SAR data server device 20 and the three-dimensional point cloud data server device 22 via the Internet. The terminal devices T1, T2, ..., Tn are connected so as to be able to communicate with the economic loss monitoring server device S via the Internet. Thus, a user can access the economic loss monitoring server device S from the terminal devices T1, T2, ..., Tn and obtain an estimate of economic loss due to submergence.

[0053] 3 shows the hardware configuration of the economic loss monitoring server device S. A memory 32, a communication circuit 34, an SSD 36, and a DVD-ROM drive 38 are connected to a CPU 30. The communication circuit 34 is for connecting to the Internet.

[0054] The SSD 36 stores an operating system 40 and an economic loss monitoring program 42. The economic loss monitoring program 42 performs its functions in cooperation with the operating system 40. These programs were recorded on a DVD-ROM 44 and installed in the SSD 36 via the DVD-ROM drive 38.

[0055] 4 shows the hardware configuration of the terminal device T. Connected to the CPU 50 are a memory 52, a communication circuit 54, an SSD 56, a DVD-ROM drive 58, a keyboard / mouse 60, and a display 62. The communication circuit 54 is for connecting to the Internet.

[0056] An operating system 64 and a terminal program 66 are recorded in the SSD 56. The terminal program 66 performs its functions in cooperation with the operating system 64. These programs were recorded on a DVD-ROM 68 and installed in the SSD 56 via the DVD-ROM drive 58.

[0057] 1.3 Economic loss monitoring process 5 shows a flowchart of the economic loss monitoring program 42. The CPU 30 of the economic loss monitoring server device S (hereinafter sometimes abbreviated as server device S) acquires meteorological information (step S1). For example, meteorological information related to submergence, such as information on the amount of rainfall in each region over the past few days (e.g., information from AMeDAS) and information on tsunamis caused by earthquakes, is acquired. Based on the acquired meteorological information, the server device S determines an area to monitor for submergence (step S2). In this embodiment, the monitoring area is determined based on an area divided into meshes (e.g., an area of ​​1 km square).

[0058] The server device S acquires SAR data for the determined area from the SAR data server device 20 (step S3). The SAR data is data (including coordinate information in the satellite coordinate system) obtained by observing the ground using a synthetic aperture radar (SAR) mounted on a satellite. The server device S uses backscattering intensity data from the acquired SAR data. Here, the backscattering intensity data is data indicating the intensity of the reflected waves from the earth's surface of the microwaves irradiated by the SAR. The SAR orbits the earth while shifting its orbit, and will observe the same area several days to one week later, for example.

[0059] Figure 7A shows a schematic diagram of radio waves emitted from the SAR and the reflected waves. The SAR emits radio waves diagonally downward to the side of the direction of travel. These radio waves are scattered on the ground, and some of them are received by the SAR as backscatter (scattering that returns to the SAR). Therefore, if the ground is rough, backscattering occurs and a certain degree of intensity is obtained. On the other hand, if the ground is smooth like the surface of water, most of the emitted radio waves are forward scattered, and the intensity of the backscattering is significantly reduced. Therefore, by obtaining the time-dependent change in backscattering intensity at the same point, it is possible to know the changes on the ground.

[0060] The server device S combines the backscattering intensity data during normal times that has been recorded in advance with the acquired backscattering intensity data, and identifies a fluctuation region where the backscattering intensity has decreased (step S4).

[0061] FIG. 6A shows backscattering intensity data under normal conditions. The image is shaded according to the reflection intensity, with the higher the reflection intensity, the whiter it is, and the lower the reflection intensity, the blacker it is. Backscattering data under normal conditions can be selected from past data that is reliably under normal conditions and recorded. Alternatively, past backscattering data for a predetermined number of times can be confirmed to have not changed, and this can be used as backscattering data under normal conditions.

[0062] FIG. 6B shows the acquired backscattering intensity data. The server device S superimposes three images: an image in which the backscattering intensity data under normal conditions in FIG. 6A is colored red, an image in which the acquired backscattering intensity data in FIG. 6B is colored blue, and an image in which the acquired backscattering intensity data in FIG. 6B is colored green. In areas where there is no change in intensity, the red, blue, and green are combined, and the area tends to be white when the intensity is high and black when the intensity is low. However, areas where an intensity change has occurred will have a color depending on whether the change is positive or negative.

[0063] For example, if there is a submerged area, the backscattering intensity will be reduced, resulting in a red area in the image, as shown in Figure 6C. Conversely, areas with increased backscattering intensity will be shown as blue areas.

[0064] The server device S extracts the region where the backscattering intensity is reduced as a variable region.

[0065] However, the fluctuating region identified as described above includes not only the region where the backscattering intensity has fluctuated due to submersion, but also the region where the backscattering intensity has fluctuated due to water being filled in rice paddies, etc. Also, if the lake surface changes due to rising water levels, the size of the region where the backscattering intensity has fluctuated will change.

[0066] Therefore, the server device S refers to related information about the variable area (such as map information about rice paddies, lakes, etc.) and identifies the submerged area from the variable area. For example, in rice paddies, the time of flooding for each area is recorded in advance, and the occurrence of a variable area in that area at that time is determined to not be submerged. Also, when submergence occurs due to a rise in the water level of a lake, this can be determined based on the size of the variable area with reduced backscattering intensity, and it can be determined to be a submerged area.

[0067] The satellite coordinate system is used to calculate the change area, but this is converted to latitude and longitude to align the coordinate system with related information in the map information.

[0068] When the server device S determines that there is a submerged area (step S6), it specifies the extent of the submerged area. For example, as shown in FIG.

[0069] Next, the server device S acquires three-dimensional point cloud data (including latitude and longitude information) including the submerged area 70 from the three-dimensional point cloud data server device 22 based on the position data of the submerged area 70 (step S7). The three-dimensional point cloud data is data measured under normal circumstances using a radar distance measuring device mounted on an automobile, drone, etc. It is an image that represents the appearance of the ground surface and buildings and structures as three-dimensional point cloud data.

[0070] Next, the server device S overlays the boundary line (edge) 72 of the submerged area 70 onto the three-dimensional point cloud data, and estimates the flood depth of each point based on the three-dimensional point cloud data, using the boundary line 70 as a reference (0 m).

[0071] For example, Fig. 9 shows a cross section of a terrain represented by three-dimensional point cloud data. Using edge 72 of submerged area 70 as a reference, flood depth 76 at point A can be calculated. Server device S divides submerged area 70 into a number of mesh areas as shown in Fig. 10, and calculates the flood depth for each divided area (step S8). Note that the flood depth is calculated as the average depth in the division. Alternatively, it may be the maximum depth.

[0072] Next, the server S calculates the direct damage for each division based on the submergence depth (step S9). In this embodiment, the number of days of business suspension for each division is calculated based on data showing the relationship between the submergence depth and the number of days of business suspension shown in Fig. 11 (see "Flood Control Economic Manual" by the Water Management and Disaster Management Bureau of the Ministry of Land, Infrastructure, Transport and Tourism).

[0073] Furthermore, information on factories and stores in each section is obtained from map data. The map data may be recorded in advance in the SSD 36, or may be obtained from a server device. The amount of damage caused by business interruption is calculated based on normal sales statistics (which can be obtained from an external server device, etc.) of the factories and stores in each section.

[0074] This makes it possible to create and record a map of damage amounts by category, as shown in FIG.

[0075] The server device S repeatedly executes the above process, thereby making it possible to calculate and record the estimated amount of damage caused by flooding in various locations in real time.

[0076] If one wishes to know the estimated amount of damages due to flooding, one can access the server device S from the terminal device T via the Internet, specify the area, and obtain the estimated amount of damages.

[0077] 1.4 Modifications and Others (1) In the above embodiment, the amount of damage is estimated in the server device S. However, the amount of damage may be estimated by installing an economic loss monitoring program in a stand-alone PC.

[0078] (2) In the above embodiment, three-dimensional point cloud data is used as the topographical data. However, DEM (digital terrain model) data may be used instead.

[0079] (3) In the above embodiment, the indirect damages are calculated based on the number of days business was suspended, and this is the amount of damages. However, the economic impact on the suppliers and customers of each flooded company may be calculated as damages.

[0080] This makes it possible to display damage in areas other than the flooded areas, as shown in Figure 13. In other words, it is possible to estimate not only the economic loss in the affected area, but also the extent to which the economic loss has spread to other areas via the supply chain, etc.

[0081] In addition, the calculation of the damage amount may take into account economic losses caused by traffic disruptions due to flooding of roads and railway tracks.

[0082] (4) In the above embodiment, the server device S acquires meteorological information and determines the area to be monitored based on the information. However, the area to be monitored may be designated from the terminal device T, and the server device S may perform monitoring in accordance with the designation.

[0083] (5) In the above embodiment, the edge of the flooded area is identified based on the intensity image data, and the flood depth is estimated based on this edge by referring to the topographical data. However, the flood depth may be estimated by other methods, such as repeatedly tracking the phase of backscattering and tracking changes in altitude, based on SAR observation data under normal conditions.

[0084] (6) In the above embodiment, the amount of direct damages to business activities is calculated based on the calculated flood depth. However, the flood depth may also be used for other purposes, such as estimating human damage to aid in rescue efforts.

[0085] Alternatively, the amount of direct damage may be calculated as follows. First, the total assets such as houses within a mesh area are calculated. For example, statistical data is used to calculate the amount using the following formula (see the Flood Control Economic Survey Manual (Draft) (hereinafter referred to as the Manual) published by the Ministry of Land, Infrastructure, Transport and Tourism in April 2020).

[0086] a) house The total floor area of ​​the houses existing within a mesh area is multiplied by the assessed value per square meter of the house (it is preferable to use statistical data; for example, Table 1 in the supplementary reference material to the manual cited above) to calculate the value of the houses in that mesh area.

[0087] Total floor area x 1m of house by prefecture 2 Per valuation The amount of damage to houses is calculated by multiplying the house assets of the mesh area calculated in this way by the damage rate by flood depth shown in Figure 14.

[0088] b) Household items The number of households in a mesh area is multiplied by the assessed value of household goods per household (it is preferable to use statistical data; for example, Table 2 in the supplementary reference material to the manual cited above) to calculate the value of household goods assets in that mesh area.

[0089] Household goods assets = Household goods assets (household goods excluding automobiles) + Household goods assets (automobiles) Household asset value (household goods excluding automobiles) = number of households x assessed value of household goods per household (household goods excluding automobiles) Household asset value (automobiles) = number of households x assessed value of household assets per household (automobiles) The amount of damage to household goods is calculated by multiplying the household goods assets in the mesh area calculated in this way by the damage rate by flood depth shown in Figure 15.

[0090] c) Depreciation and inventory assets at business establishments For each industrial classification, multiply the number of employees within a mesh area (you can use the number of employees by industrial classification from the Economic Census, etc.) by the per capita depreciable assets and inventory asset valuation (it is preferable to use statistical data; for example, Table 3 in the supplementary reference materials for the Manual) to calculate the establishment depreciable assets and inventory assets.

[0091] Number of employees x Depreciable asset valuation and inventory asset valuation per employee The business depreciation and inventory asset damage amount is calculated by multiplying the business depreciation and inventory asset damage rate for the mesh area calculated in this way by the damage rate for each flood depth shown in Figure 16.

[0092] d) Depreciation and inventory assets for agricultural and fishing businesses The number of farming and fishing households within a mesh area (regional mesh statistics related to the national census can be used) is multiplied by the depreciable asset valuation amount and inventory asset valuation amount per household (it is preferable to use statistical data; for example, Table 4 in the supplementary reference material to the manual cited above) to calculate the amount of farming and fishing household depreciable assets and inventory assets.

[0093] Number of farm households x Depreciable asset valuation and inventory asset valuation per household The damage amount for agricultural and fishing household depreciation and inventory assets is calculated by multiplying the damage rate by flood depth shown in Figure 17 for the agricultural and fishing household depreciation and inventory assets for the mesh area calculated in this way.

[0094] e) Crops The value of agricultural assets is calculated by multiplying the area of ​​paddy fields and field areas within a mesh region by the average annual yield per unit area (it is preferable to use statistical data. For example, for paddy fields, see Table 5 in the supplementary reference material to the Manual cited above, and for field areas, it is preferable to use statistical data on agricultural crop prices per unit yield. For example, Table 6 in the supplementary reference material to the Manual cited above).

[0095] Paddy field / field area x average yield x crop price The agricultural product damage value is calculated by multiplying the agricultural product asset value of the mesh area calculated in this way by the damage rate by flood depth shown in Figure 18.

[0096] In this way, damage amounts for each type of property or asset can be calculated, which can then be combined to calculate the total direct damage amount.

[0097] (7) In the above embodiment, the area where the backscattering intensity has changed is estimated using image processing. However, the difference in backscattering intensity may be calculated as a numerical value, and an area where the numerical value has changed by a predetermined value or more may be estimated as a changed area.

[0098] (8) In the above embodiment, the submerged area, which is the damaged area, is estimated based on the decrease in backscattering intensity. However, the collapse of a bridge pier over a river or lake can also be estimated in a similar manner.

[0099] Furthermore, in order to identify an area buried under sand as a damaged area, an area where the backscattering intensity has increased may be identified as a sand-buried area, because the backscattering intensity becomes higher than normal due to increased unevenness caused by sand.

[0100] In this case, the amount of accumulated sediment is calculated by comparing the 3D point cloud data measured under normal circumstances with the 3D point cloud data measured after the occurrence of the abnormality (which can be measured by mounting a measuring device on a drone or the like). Based on this amount of accumulated sediment, the amount of damage to each property and asset shown in (6) above can be calculated. The amount of damage is calculated based on the data in Figures 14 to 18, which show the relationship between the amount of accumulated sediment (accumulation height) and the damage rate.

[0101] (9) In the above embodiment, the affected area is identified based on the change in backscattering intensity. However, the damaged area may be estimated based on the lack of correlation between the normal phase and the acquisition phase (reduction in coherence).

[0102] (10) In the above embodiment, the change amount area is specified based on the change in backscattering intensity, and this is combined with map information such as DEM to estimate the submerged area. For each mesh of the submerged area obtained in this way (for example, a mesh of 10 m square), the DEM data is referenced and the altitude is calculated as the target mesh. Next, the altitudes of the eight meshes surrounding the target mesh are also calculated.

[0103] Among the eight surrounding meshes, meshes that do not belong to the submerged region and have an elevation lower than the mesh of interest are added as submerged regions. This is because meshes with an elevation lower than the mesh of interest are likely to be submerged.

[0104] For example, assume that the elevations of the meshes around the mesh of interest FM are as shown in Fig. 23A. In this figure, the meshes with lanes drawn on them are submerged areas.

[0105] As shown in Figure 23B, among the meshes around the target mesh FM, SM2 and SM3, which have low altitudes, are newly changed to submerged regions. Mesh SM1 has a high altitude, so it is not changed to a submerged region. SM4 also has a low altitude, but it is originally a submerged region, so it remains as it is.

[0106] The final submerged area may be determined in the manner described above. Note that the mesh of the DEM data is often smaller than the mesh of the SAR data. In such a case, when calculating the elevation of the mesh of the SAR data, the average elevation (or the maximum or minimum value) of the mesh of the DEM data included in the mesh may be used.

[0107] Moreover, the above-mentioned submerged area expansion process may be performed not for all meshes in the submerged area, but by expanding the area surrounded by the cliff line in the DEM data by a processing distance (for example, 200 m) outward from the cliff line, and then performing the process only on the overlapping area between this expanded area and the submerged area (and further, on the overlapping area that is equal to or larger than a predetermined area). This makes it possible to speed up the process while preventing a decrease in accuracy.

[0108] (10) The above-described embodiments and modifications may be implemented in combination with each other. Also, the above-described embodiments and modifications may be implemented in combination with other embodiments and modifications.

[0109] 2. Second embodiment 2.1 Overall configuration Fig. 19 shows the functional configuration of an economic loss monitoring device according to an embodiment of the present invention. In the observation data acquisition process 12, the economic loss monitoring device acquires observation data obtained by observing the ground using a synthetic aperture radar (SAR) mounted on an artificial satellite. In the change area identification process 14, a change area is identified based on the change in the observation data.

[0110] Furthermore, map data (data showing the types and sizes of buildings, features, etc.) of the surrounding area of ​​the changed area is acquired in map data acquisition process 16. In disaster extent estimation process 18, the extent of the disaster in the changed area is estimated based on this map data.

[0111] In the economic loss calculation process 10, the amount of damage is estimated based on the extent of the disaster at each location.

[0112] As described above, the extent of the disaster is estimated based on the observation data from the synthetic aperture radar and the map data, and the economic loss is further estimated based on this. Therefore, the economic loss due to the disaster can be calculated in real time.

[0113] 2.2 System configuration and hardware configuration The system configuration and hardware configuration are similar to those in the first embodiment shown in FIGS.

[0114] 2.3 Economic loss monitoring process 20 shows a flowchart of the economic loss monitoring program 42. The server device S acquires disaster-related information (weather information, tsunami information, earthquake information, accident information, flood information, etc.) (step S11). For example, disaster information for the past few days in each region is acquired. The server device S determines an area to be monitored based on the acquired disaster information (step S12).

[0115] The server device S acquires the SAR data of the determined area from the SAR data server device 20 (step S13). The server device S calculates the displacement on the ground (displacement in the direction from the satellite to the observation point) by differential interferometry using the backscattering phase and the normal phase of the acquired SAR data.

[0116] Figure 21 shows a schematic diagram of how displacement is calculated using differential interferometry. The phase difference between the reflected waves observed by SAR during normal times and during measurement varies depending on the height H (topography) of surface point P and the inter-satellite distance B (the deviation in the satellite's position between normal times and the current measurement). The phase also varies due to fluctuations caused by atmospheric conditions, scattering, and noise. Therefore, based on the phase difference, the fluctuations caused by these factors are cancelled out and the displacement in the direction of radio wave irradiation is calculated.

[0117] However, if a phase difference of more than one period of the irradiated radio waves occurs, it is not possible to measure accurate changes in displacement. Therefore, as shown in Figure 22, accurate changes in height are calculated by accumulating phase differences from points where there is no change. This type of calculation is possible if the change is gradual. If the change in height is sudden (if there is a drastic change that is no longer related to the original topography), this method cannot be used and only the fact that a change in height has occurred can be detected.

[0118] The change area includes not only the area caused by the disaster, but also the area where the height has changed due to the change of vegetation, the harvesting and growth of crops, etc. Therefore, the server device S distinguishes between the disaster area and the non-disaster area as follows.

[0119] The server device S acquires map data of the changed area and obtains the attributes of the changed area. That is, it determines whether the changed area is a mountainous area, a road, a railroad, a field, a river, a building, etc. For example, if the changed area is a field, etc., and the amount of change is constant over a wide range, it is determined to be a crop harvest and is not a disaster area. On the other hand, if the amount of change is large and not uniform in roads, rivers, railroads, buildings, etc., it is determined to be a disaster area (step S18).

[0120] The server device S estimates the extent of the disaster for the area determined to be a disaster area by referring to the information (attributes) of the map data. For example, if the disaster area of ​​a road, bridge, railroad, etc. spreads beyond a certain range (presuming a landslide, etc.), it determines that the road, bridge, railroad, etc. is not accessible. At the same time, it estimates the number of days until recovery based on the spread of the disaster area.

[0121] In this case, if the amount of change (displacement) can be calculated accurately, the extent of damage may be calculated based on this amount of change. For example, in the case of a road, if the change in height due to earth and sand exceeds a predetermined value, it is determined that the road is impassable.

[0122] For buildings, the extent of the disaster area around the building is used to estimate whether the building is partially or completely destroyed. For example, a comparison is made between normal times and times of disaster, and the proportion of the floor area of ​​the building where displacement has occurred is calculated, and whether the building is partially or completely destroyed is determined based on that proportion. In this case, the number of days until recovery is estimated based on the extent of the disaster area. At the same time, information such as whether the building is a factory, a residence, an apartment building, a detached house, or a shop is obtained from map data.

[0123] The attributes of the roads, rivers, railroad tracks, buildings, and the like described above may be derived by estimation based on three-dimensional point cloud data.

[0124] Next, the server device S calculates the direct damage based on the estimated extent of the disaster. For example, if a road is impassable, the amount of damage is calculated based on the traffic volume of the road. Alternatively, the amount of damage is calculated based on the type and amount of goods transported on the road. In other words, the amount of damage is calculated by multiplying the damage amount per day by the estimated number of days for recovery.

[0125] For a factory, the damage amount is calculated by multiplying the daily shipping amount by the estimated number of days it will take to recover.

[0126] Direct damages can be calculated in this manner.

[0127] The server device S is configured to repeatedly execute the above process, thereby making it possible to calculate and record in real time the estimated damage amounts due to disasters in various locations.

[0128] When a user wishes to know the estimated amount of damages due to a disaster, the user can access the server device S from the terminal device T via the Internet, specify the area, and obtain the estimated amount of damages for that area.

[0129] 2.4 Modifications and Others (1) In the above embodiment, the amount of damage is estimated in the server device S. However, the amount of damage may be estimated by installing an economic loss monitoring program in a stand-alone PC.

[0130] (2) In the above embodiment, direct damages are calculated and used as the amount of damages. However, the economic impact on the suppliers and customers of the collapsed company may be calculated as indirect damages. Of course, both direct and indirect damages may be calculated.

[0131] (3) In the above embodiment, the ground surface displacement is calculated based on the phase difference of the backscattering to estimate the disaster area. However, instead of directly estimating the disaster area, it is also possible to detect the uplift of the road slope and the like to detect the signs of the disaster.

[0132] (4) In the above embodiment, the ground displacement is calculated based on the phase difference, and the damaged area is estimated based on this. However, the damaged area may be estimated based on the lack of correlation between the normal phase and the acquired phase (reduction in coherence).

[0133] (5) The above-described embodiments and modifications may be implemented in combination with each other or in combination with other embodiments and modifications.

Claims

1. Acquire ground observation data using synthetic aperture radar, Identifying flooded areas caused by disasters based on changes in the observation data over time; Obtaining observation data in normal times in the vicinity of the submerged area; For a submerged area identified based on the observation data by the synthetic aperture radar, estimating a submerged depth by using an edge of the submerged area as a reference and referring to the topographical data; An economic loss monitoring device that calculates economic loss based on the estimated submergence depth.

2. At least one processor; a memory storing at least one program configured to be executed by said at least one processor; An economic loss monitoring device comprising: The at least one program comprises: Acquire ground observation data using synthetic aperture radar, Identifying flooded areas caused by disasters based on changes in the observation data over time; Acquire topographical data in a normal state in the vicinity of the submerged area; For a submerged area identified based on the observation data by the synthetic aperture radar, estimating a submerged depth by using an edge of the submerged area as a reference and referring to the topographical data; An economic loss monitoring device having instructions for calculating economic losses based on the estimated submergence depth.

3. An economic loss monitoring program for implementing an economic loss monitoring device by a computer, Acquire ground observation data using synthetic aperture radar, Identifying flooded areas caused by disasters based on changes in the observation data over time; Acquire topographical data in a normal state in the vicinity of the submerged area; For a submerged area identified based on the observation data by the synthetic aperture radar, estimating a submerged depth by using an edge of the submerged area as a reference and referring to the topographical data; and an economic loss monitoring program having instructions for calculating economic losses based on the estimated submergence depth.

4. In any one of the devices or programs according to claims 1 to 3, The device or program, wherein the topographical data is three-dimensional point cloud data or a digital topographical model.

5. In any one of the devices or programs according to claims 1 to 3, The device or program, wherein the economic loss is direct damage in a flooded area or indirect damage to a location related to the flooded area, or both.

6. In any one of the devices or programs according to claims 1 to 3, A device or program characterized in that the estimation of the submergence depth is performed for each of a plurality of sections into which the flooded area is divided.

7. In any one of the devices or programs according to claims 1 to 3, The economic loss monitoring device is constructed as a server device accessible from a terminal device via a network.

8. In any one of the devices or programs according to claims 1 to 3, The device or program, wherein the observation data is backscattered reflection intensity.

9. In any one of the devices or programs according to claims 1 to 3, An apparatus or program characterized in that, if an area adjacent to a submerged area identified based on the observation data has an elevation lower than that of the submerged area, the adjacent area is also designated as a submerged area.

10. Acquire ground observation data using synthetic aperture radar, Identifying flooded areas caused by disasters based on changes in the observation data over time; Acquire topographical data in a normal state in the vicinity of the submerged area; A submergence degree estimation device that estimates the submergence depth of a submerged area identified based on the observation data by a synthetic aperture radar, using the edge of the submerged area as a reference and referring to the topographical data.

11. At least one processor; a memory storing at least one program configured to be executed by said at least one processor; A submersion degree estimation device comprising: The at least one program comprises: Acquire ground observation data using synthetic aperture radar, Identifying flooded areas caused by disasters based on changes in the observation data over time; Acquire topographical data in a normal state in the vicinity of the submerged area; A submergence degree estimation device that is equipped with an instruction to estimate the submergence depth of a submerged area identified based on the observation data by a synthetic aperture radar, using the edge of the submerged area as a reference and referring to the topographical data.

12. A submersion degree estimation program for implementing a submersion degree estimation device by a computer, Acquire ground observation data using synthetic aperture radar, Identifying flooded areas caused by disasters based on changes in the observation data over time; Acquire topographical data in a normal state in the vicinity of the submerged area; A submergence degree estimation program having an instruction to estimate the submergence depth of a submerged area identified based on the observation data by a synthetic aperture radar, using the edge of the submerged area as a reference and referring to the topographical data.

13. In any one of the devices or programs according to claims 10 to 12, The device or program, wherein the topographical data is three-dimensional point cloud data or a digital topographical model.

14. In any one of the devices or programs according to claims 10 to 12, The device or program, wherein the observation data is backscattered reflection intensity.

15. Acquire ground observation data using synthetic aperture radar, Identifying a region of change based on changes in the observation data over time; acquiring map data at least in the vicinity of the variable region; estimating the extent of the disaster with reference to the map data for the affected area identified based on the observation data by the synthetic aperture radar; An economic loss monitoring device that calculates economic loss based on the estimated extent of the disaster.

16. At least one processor; a memory storing at least one program configured to be executed by said at least one processor; An economic loss monitoring device comprising: The at least one program comprises: Acquire ground observation data using synthetic aperture radar, Identifying a region of change based on changes in the observation data over time; acquiring map data at least in the vicinity of the variable region; estimating the extent of the disaster with reference to the map data for the affected area identified based on the observation data by the synthetic aperture radar; An economic loss monitoring device having instructions for calculating economic losses based on the estimated extent of the disaster.

17. An economic loss monitoring program for implementing an economic loss monitoring device by a computer, Acquire ground observation data using synthetic aperture radar, Identifying a region of change based on changes in the observation data over time; acquiring map data at least in the vicinity of the variable region; estimating the extent of the disaster with reference to the map data for the affected area identified based on the observation data by the synthetic aperture radar; and an economic loss monitoring program having instructions for calculating economic losses based on the estimated extent of the disaster.

18. In any one of the devices or programs according to claims 15 to 17, The device or program, wherein the map data is information for distinguishing at least buildings, roads, and railroads.

19. In any one of the devices or programs according to claims 15 to 17, The device or program, wherein the economic loss is direct damage in a disaster area or indirect damage in an area related to the disaster area, or both.

20. In any one of the devices or programs according to claims 15 to 17, A device or program, characterized in that the estimation of the extent of the disaster is performed for each of a plurality of sections into which the disaster area is divided.

21. In any one of the devices or programs according to claims 15 to 17, The economic loss monitoring device is constructed as a server device accessible from a terminal device via a network.

22. In any one of the devices or programs according to claims 15 to 17, The disaster area is a submerged area, The extent of the disaster is a submersion depth.

23. In any one of the devices or programs according to claims 15 to 17, The device or program, wherein the observation data is backscattered reflection intensity.

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