JP6694212B2 - Traffic obstacle estimation system - Google Patents

Description

  The present invention relates to a traffic fault estimation system suitable for estimating a fault in a transportation network such as a road or a railroad immediately after a disaster occurs.

  As a technique for grasping the topography, a technique is known in which a laser is emitted from an aircraft or the like toward the surface of the earth to obtain height information of the ground surface.

For example, in Patent Document 1 (Japanese Patent Publication No. 2014-35232), a laser scanner is mounted on an aircraft, and while the aircraft is flying, the laser scanner irradiates a laser beam on the ground and a time difference from the laser beam reflected from the ground. A technique relating to aviation laser surveying is disclosed, in which the altitude to the ground and the shape of the terrain are precisely determined from the distance to the ground obtained more and the position information of the aircraft obtained from the GPS surveying instrument and IMU (inertial measurement device).
Japanese Patent No. 2014-35232

  When an earthquake or a natural disaster such as a landslide, a debris flow, or an avalanche occurs, a traffic network such as a road or a railroad may be damaged, which may cause a vehicle to be difficult to pass through. These often occur over a wide area and at the same time, and it is difficult to comprehensively understand them.

  Therefore, if a traffic network such as a road or railroad is damaged due to an earthquake, slope disaster, avalanche, or other natural disaster, use the conventional technology described above to identify the topography and verify the existence of any obstacles to the traffic network. It is possible to do it.

  However, the aerial laser survey from an aircraft has good measurement accuracy, but requires enormous preparations, is expensive, and takes a long time to obtain a measurement result. There is a problem that it is not possible to determine whether it is a topography or another feature, and it is not possible to easily grasp the obstacles of the transportation network such as roads and railways immediately after the disaster occurs.

  If the state of the transportation network such as roads and railroads immediately after the occurrence of a disaster could be easily and quickly grasped, it would be useful for recovery, but in the past this was not possible and there was a problem.

The present invention is to solve the problems as described above, and a traffic obstacle estimation system according to the present invention provides a first coordinate-calculated three-dimensional image based on a plurality of aerial photographs acquired in a first period. While acquiring point cloud data, second coordinate-calculated three-dimensional point cloud data is acquired based on a plurality of aerial photographs acquired at a second time different from the first time, and the first coordinate calculation is performed. Topography change data acquisition step of acquiring topography change data based on the difference between the completed three-dimensional point cloud data and the second coordinate-calculated three-dimensional point cloud data, and obtained from the traffic network data included in the map data. Preparing the buffer zone data, obtaining the terrain change amount in the zone of interest of the buffer zone data from the terrain change data, and when the terrain change amount is a predetermined value or more Estimated that the traffic failure, when said terrain variation is less than the predetermined value, and to execute the steps of presumed traffic hazard has not occurred, the.

  Further, the traffic obstacle estimation system according to the present invention is characterized in that the predetermined value differs depending on the ground resolution.

Further, the traffic obstacle estimation system according to the present invention is characterized in that the buffer zone data is obtained from point data and line data used in a GIS (Geographic Information System) .

  Further, the traffic obstacle estimation system according to the present invention is characterized in that the aerial photographs are a plurality of aerial photographs obtained by taking topography from different positions.

  Further, the traffic obstacle estimation system according to the present invention is characterized in that the Structure from Motion technology and the Multi-view Stereo technology are used to prepare the coordinate-calculated three-dimensional point cloud data.

  Since the traffic obstacle estimation system according to the present invention estimates the occurrence of a traffic obstacle from the data based on the topographic change data of the terrain based on the aerial photograph and the data based on the buffer zone data, the traffic obstacle related to the present invention as described above. According to the failure estimation system, it is possible to easily understand the failure of the transportation network such as the road and the railroad immediately after the occurrence of the disaster, and it is possible to use the acquired information for recovery after the disaster.

It is a figure explaining the composition of the air vehicle 101 which acquires the aerial photograph used with the traffic obstacle estimation system which concerns on embodiment of this invention. It is a figure showing an example of composition of a computer which realizes a traffic obstacle presumption system based on a traffic obstacle presumption method concerning an embodiment of the present invention. It is a figure which shows the flowchart of the analysis process performed with a surface change analysis system. It is a figure which shows the subroutine which acquires the coordinate-calculated three-dimensional point cloud data of a ground change analysis system. It is a figure which shows the data structure example of traffic network data. It is a figure which shows the flowchart of the buffer zone data acquisition process performed with the traffic obstacle estimation system which concerns on this invention. It is a figure which shows the flowchart of the traffic obstacle estimation process in the traffic obstacle estimation system which concerns on this invention. It is an example of the map data in which the location determined to have a traffic obstacle by the traffic obstacle estimation system according to the present invention is displayed in different colors.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. First, an example of a method for taking an aerial photograph used in the traffic obstacle estimation system according to the present invention will be described. FIG. 1 is a diagram illustrating a configuration of an aircraft 101 that acquires an aerial photograph used in a traffic obstacle estimation system according to an embodiment of the present invention, showing a state in which an aerial photograph of the ground surface is taken by the aircraft 101. There is.

  In the traffic obstacle estimation system according to the present invention, it is assumed that the aerial photograph is taken by the flying object 101 after an earthquake or a natural disaster such as landslide, debris flow, or avalanche occurs. In the traffic obstacle estimation system, steps such as estimation are executed from aerial photographs after the occurrence of natural disasters.

  In the following, a configuration for taking an aerial photograph will be described, but the aerial photograph taking method with such a configuration is merely an example of the aerial photograph taking method, and the traffic obstacle estimation system according to the present invention is It is also possible to use an aerial photograph acquired by another photographing method.

  In FIG. 1, reference numeral 101 denotes a flying body capable of autonomous flight. Although an unmanned aerial vehicle is assumed here as the flying object 101, an aerial photograph may be taken using a manned aerial vehicle.

  A control device (not shown) is installed on the ground, and the control device is capable of data communication with the flying object 101, and controls flight of the flying object 101, sets and changes flight plan, and changes the flying object 101. The information collected by can be saved and managed.

  As the flying body 101, for example, a helicopter as a small flying body that autonomously flies can be used. The air vehicle 101 (helicopter) is operated by remote control from the control device, or a flight plan is set in the control device of the air vehicle 101 (helicopter) by the control device so that the air vehicle 101 can fly autonomously according to the flight plan. Is becoming

  The flying body 101 (helicopter) has an airframe 103 and a required number of propellers provided on the airframe 103, for example, front and rear, left and right, and a total of four sets of propellers 104, 105, 106, 107. The propellers 104, 105, 106 and 107 are individually connected to a first motor, a second motor, a third motor, and a fourth motor (all not shown). Further, the drive of each of the first motor, the second motor, the third motor, and the fourth motor is independently controlled. The propellers 104, 105, 106, 107, the first motor, the second motor, the third motor, the fourth motor, and the like constitute the navigation means of the flying body.

  An image pickup device 113 and a flight device control device (not shown) are provided on the airframe 103 of the flight device 101 (helicopter). The image pickup device 113 acquires digital image data. The image pickup device 113 may be a camera that picks up still images at predetermined time intervals, or may be a video camera that continuously picks up images. The imaging device 113 is provided on the lower surface of the machine body 103. Further, the image pickup device 113 has CCD and CMOS sensors, which are a group of pixels, as an image pickup element, and can acquire digital image data (aerial photograph) from the air by this image pickup element. You can do it.

  The aerial photograph data acquired by the imaging device 113 may be wirelessly transmitted to a control device on the ground, or may be stored in a medium (not shown) or the like, and the air vehicle 101 (helicopter) may be stored. After landing, the media may be retrieved from this media.

  It should be noted that acquiring an aerial photograph with the image pickup device 113 mounted on the flying object 101 (helicopter) as described above does not require large-scale preparation, and the aerial photograph can be taken relatively easily and inexpensively. Although it is an important matter in realizing the traffic obstacle estimation system according to the present invention, the traffic obstacle estimation system according to the present invention does not necessarily need to use an aerial photograph using the flying body as described above. There is no. That is, the traffic obstacle estimation system according to the present invention can be realized even by using an aerial photograph or the like acquired by a conventional manned airplane or the like, or an aircraft such as an artificial satellite.

  Next, based on the aerial photograph acquired by the imaging device 113 of the flying object 101 (helicopter) and the vector data generally used in GIS (Geographic Information System), The configuration of a computer that executes the traffic obstacle estimation system according to the present invention for estimating the obstacle situation of a traffic network will be described.

  FIG. 2 is a diagram showing an example of the configuration of a computer that realizes the traffic obstacle estimation system based on the traffic obstacle estimation method according to the embodiment of the present invention. In FIG. 2, 10 is a system bus, 11 is a CPU (Central Processing Unit), 12 is a RAM (Random Access Memory), 13 is a ROM (Read Only Memory), and 14 is a communication control unit that controls communication with an external information device. Reference numeral 15 is an input control unit such as a keyboard controller, 16 is an output control unit, 17 is an external storage device control unit, 18 is an input unit including input devices such as a keyboard, pointing device, and mouse, 19 is an output unit such as a printing device, Reference numeral 20 denotes an external storage device such as an HDD (Hard Disk Drive), 21 denotes a graphic control unit, and 22 denotes a display device.

  In FIG. 2, the CPU 11 retrieves / acquires data by communicating with an external device in accordance with a program ROM stored in the ROM 13 or a program stored in the large-capacity external storage device 20, or the like. It is for performing arithmetic processing such as processing of output data in which graphics, images, characters, tables and the like are mixed, and further management of a database stored in the external storage device 20.

  Further, the CPU 11 centrally controls each device connected to the system bus 10. The program ROM in the ROM 13 or the external storage device 20 stores an operating system program (hereinafter referred to as OS), which is a basic program for controlling the CPU 11.

  Further, the ROM 13 or the external storage device 20 stores various data used when performing output data processing and the like. The RAM 12, which is the main memory, functions as the main memory, work area, etc. of the CPU 11.

  The input control unit 15 controls the input unit 18 from a keyboard or a pointing device (not shown). The output control unit 16 also controls the output of the output unit 19 such as a printer.

  The external storage device controller 17 is an external storage device 20 such as a hard disk drive (HDD) that stores a boot program, various applications, font data, user files, edit files, printer drivers, or the like, or a floppy disk (FD). Control access to. The system program that realizes the traffic obstacle estimation method of the present invention is stored in the external storage device 20 as described above. Further, the graphic control unit 21 is a component for drawing the information displayed on the display device 22.

  Further, the communication control unit 14 controls communication with an external device via a network, and thereby acquires data required by the system from a database held by an external device on the Internet or an intranet, It is configured to be able to send information to an external device.

  In the external storage device 20, in addition to an operating system program (hereinafter referred to as OS) which is a control program for the CPU 11, a system program for operating the traffic obstacle estimation system of the present invention on the CPU 11 and data used by the system program are installed. Saved and stored.

  As the data used in the system program that realizes the traffic obstacle estimation method of the present invention, it is basically assumed that the data is stored in the external storage device 20. It is also possible to configure to acquire from an external device on the Internet or an intranet via the communication control unit 14. Further, the data used in the system program that realizes the traffic obstacle estimation method of the present invention can be configured to be acquired from various media such as a USB memory, a CD, and a DVD.

  Further, it is assumed that the external storage device 20 stores various data referred to when the processing steps of the traffic obstacle estimation method according to the present invention are executed, such as aerial photograph data and map data described later. Has been done.

  In the traffic obstacle estimation system according to the present invention, the presence or absence of the traffic obstacle is estimated by using the topographic change data obtained by the ground change analysis method. Therefore, a ground change analysis method used in the present invention that can be executed by a computer having the above system configuration will be described below.

  As a general outline, the ground surface change analysis system in which the ground surface change analysis method is executed analyzes the ground surface change between the first period and the second period that is different from the first period. One example of such an analysis result output method is to visualize this and display it on the display device 22 or the like. In the present invention, this result is used for a traffic obstacle estimation system.

  Here, in the analysis processing of the surface change analysis system, the ground surface in the case where there is a significant change in the ground surface between the aerial photograph related to the first period and the aerial photograph related to the second period after the first period. It is supposed to be analyzed. Further, the second time is assumed to be a time after a disaster such as landslide, debris flow, heavy snowfall or avalanche, and the first time is assumed to be a time before the disaster.

  In addition, in the surface change analysis system, the aerial photograph prepares what can be processed as digital data. However, when only the analog photograph can be prepared as the aerial photograph related to the first period before the disaster occurs. , This can be scanned and made into digital data. With respect to the aerial photograph relating to the second period, if the aerial photograph is acquired based on the aerial photograph acquisition method described above by the flying object 101 (helicopter), an aerial photograph with appropriate digital data can be obtained.

  Hereinafter, the analysis processing in the surface change analysis system used in the present invention will be described. FIG. 3 is a diagram showing a flowchart of an analysis process executed by the surface change analysis system.

  In FIG. 3, when the analysis process is started in step S100, subsequently, in step S101, a step of preparing a plurality of aerial photographs acquired in the first period is executed. In this step, it is assumed that multiple aerial photographs will be prepared before a disaster such as a landslide, debris flow, heavy snowfall or avalanche that causes a change in the ground surface. Here, hereinafter, when preparing an aerial photograph, it is required to prepare a plurality of aerial photographs of the ground surface to be analyzed, taken from different positions at the same time. Furthermore, the location information (longitude / latitude / height) of the camera that took the picture is required. In addition to this, when camera posture information (yaw, pitch, roll; magnetic azimuth, elevation / depression angle, rotation angle) and lens distortion information can be obtained, it contributes to improvement in accuracy of the three-dimensional point cloud data, which is preferable. Is not mandatory. This is the same in the case of the step of preparing the aerial photograph for the second period, which will be described later.

  Subsequently, in step S102, a subroutine for acquiring coordinate-calculated three-dimensional point cloud data is executed based on the aerial photograph of the first period prepared in the previous step S101.

  Hereinafter, such a subroutine will be described. FIG. 4 is a diagram showing a subroutine for acquiring coordinate-computed three-dimensional point cloud data of the surface change analysis system.

  In FIG. 4, when the coordinate-calculated three-dimensional point cloud data acquisition subroutine is started in step S200, a plurality of aerial photographs at corresponding times are referred to in step S201.

  In step S202, if the prepared aerial photograph is for the first period, shooting point information (latitude / longitude / height) is set. If the prepared aerial photograph is one other than the one in the first period, no special process may be performed in this step. However, the aerial photographs of the 1st and 2nd periods are images taken by the camera for digital aerial survey, and the posture information of the camera (yaw, pitch, roll; magnetic direction, elevation angle, rotation angle) When it is desired to measure highly accurate three-dimensional point cloud data, such as when lens distortion information is obtained, the information may be used.

  Next, in step S203, three-dimensional distribution data is calculated from a plurality of aerial photographs. In such a three-dimensional distribution data calculating step of step S203, it is preferable to use the Structure from Motion technology. Alternatively, it may be obtained by conventional photogrammetry. Alternatively, three-dimensional point cloud data obtained by laser survey may be acquired or used.

  The Structure from Motion technology is a technology for extracting three-dimensional distribution data of the ground surface by extracting feature points in an aerial photograph and associating a plurality of aerial photographs with each other. As such a three-dimensional distribution data calculation step, a conventionally known algorithm described in JP2013-120133A, JP2014-120079A, or the like can be used.

  Next, in step S204, based on the three-dimensional distribution data calculated in step S203, three-dimensional point cloud data calculation for calculating three-dimensional point cloud data of the ground surface having a higher density than the three-dimensional distribution data of the ground surface Perform the step. In this three-dimensional point cloud data calculation step, it is preferable to use the Multi-view Stereo technique.

  Software such as SfM-MVS software or photogrammetric software can be used for the three-dimensional distribution data calculation step of step S203 and the three-dimensional point cloud data calculation step of step S204.

  Subsequently, in step S205, the position coordinates of all the point groups are calculated based on the three-dimensional point cloud data calculation step obtained as described above and the reference coordinates (GCP; ground control point), and coordinate calculation is performed. Completed 3D point cloud data is acquired.

  Here, the setting of the coordinate position of the reference coordinates (GCP; ground control point) will be described. The aerial photographs themselves are image information, not geographic information. Therefore, work is performed to set reference coordinates (GCP; ground control points) at locations that are easy to identify in the aerial photograph.

  It is easy to set a target in the aerial photographs prepared in the 1st period, and a similar ground object reflected in the aerial photographs prepared in the 2nd period is manually set as the reference coordinates (GCP; ground control point). To decide.

  Since the aerial photographs prepared in the first period have shooting location information (latitude / longitude / height), the position coordinates of the reference coordinates (GCP; ground control point) are based on these. calculate. An existing algorithm can be used for such calculation.

  The reference coordinates (GCP; ground control point) calculated in this manner may have an error of about ± 5 m when true geographic coordinates are used as a reference. The topographic change data, which is the difference between the coordinate-calculated 3D point cloud data for the first period and the coordinate-calculated 3D point cloud data for the second period, is important. If the change is relatively correct, the above error in absolute geospatial position does not cause a big problem.

  Regarding the position coordinates of the reference coordinates (GCP; ground control point) calculated based on the aerial photographs prepared in the first period, the coordinate-calculated three-dimensional point cloud is based on the aerial photographs prepared in the second period. It is also used when acquiring data.

  A conventionally known algorithm can be used as an algorithm for acquiring the coordinate-calculated three-dimensional point cloud data from the three-dimensional point cloud data calculation step and the reference coordinates (GCP; ground control point).

  It should be noted that the aerial photographs of the first and second periods are images taken by a camera for digital aerial survey, and the posture information of the camera (yaw, pitch, roll; magnetic direction, elevation angle, rotation angle) When it is desired to measure highly accurate 3D point cloud data, such as when lens distortion information is obtained, or when laser surveying 3D point cloud data can be used in the first and second periods. May use the three-dimensional point cloud data having a small error obtained by them and proceed with the subsequent processing.

  In addition, the coordinate-calculated three-dimensional point cloud data acquired based on the aerial photograph of the first period will be referred to as first coordinate-calculated three-dimensional point cloud data.

  In step S206, the process returns to the original routine.

  Returning from the subroutine to the main routine, subsequently, in step 103 of FIG. 3, a step of preparing a plurality of aerial photographs acquired in the second period is executed. In this step, it is assumed that multiple aerial photographs will be prepared after a disaster such as a landslide, debris flow, heavy snowfall or avalanche that causes surface changes. To obtain an aerial photograph after the occurrence of a disaster, the use of the flight vehicle 101 (helicopter) as described above makes it possible to take an aerial photograph relatively easily and inexpensively.

  Subsequently, in step S104, a subroutine for acquiring coordinate-calculated three-dimensional point cloud data is executed based on the aerial photograph of the second period prepared in the previous step S103.

  In step S205, it has been described that the reference coordinates (GCP; ground control point) are referred to in the aerial photograph. Here, the reference coordinates (GCP; ground control points) set in the plurality of aerial photographs acquired in the first period and the reference coordinates (GCP; ground control points) set in the plurality of aerial photographs obtained in the second period. ) Is the same as.

  Now, when the subroutine completes the acquisition of the coordinate-calculated three-dimensional point cloud data obtained based on the empty vehicle photograph of the second period, the process returns to the main routine.

  The coordinate-calculated three-dimensional point cloud data acquired based on the aerial photograph at the second time will be referred to as second coordinate-calculated three-dimensional point cloud data.

  Now, returning to the main routine of FIG. 3, the first coordinate-calculated three-dimensional point cloud data acquired based on the plurality of aerial photographs acquired in the first time period and the plurality of data acquired in the second time period are acquired. The difference from the second coordinate-calculated three-dimensional point cloud data acquired based on the aerial photograph is calculated.

  In step S106, topographic change data is created according to the magnitude of the difference. If the difference in height between the second time and the first time at a certain point (x, y) is Δz, the topographic change data at the certain point can be expressed as (x, y, Δz).

  As an algorithm for obtaining the topographic change data which is the difference between the first period and the second period three-dimensional point cloud data, a conventionally well-known one such as GIS (Geographic Information System) can be used. ..

  The traffic obstacle estimation system according to the present invention uses such topographical change data. Further, the topographical change data obtained by the ground surface change analysis system can be color-coded and displayed on the display device 22 or output from the output unit 19, for example. In order to visualize the magnitude of the difference, not only the color-coded display but also the gray-scale display can be adopted. Further, any other method can be used as long as the magnitude of the difference can be visualized.

  The analysis process ends in step S107.

  The above surface change analysis system makes it possible to quickly grasp the surface changes immediately after a disaster such as a landslide, debris flow, heavy snowfall or avalanche, and to quickly obtain analysis results for disaster prevention and search. It can be used for support. Further, in the traffic obstacle estimation system according to the present invention, the landform change data obtained from the ground surface change analysis system as described above is used to simplify the obstacles of the traffic network such as roads and railways immediately after the occurrence of the disaster, which will be described below. Therefore, it is possible to use the obtained information for recovery after a disaster.

  Next, a traffic obstacle estimation method according to the present invention, which can be executed by a computer having the above system configuration, will be described below.

  The traffic obstacle estimation system according to the present invention is, as an outline, for example, an aerial photograph acquired after occurrence of a natural disaster such as a landslide, debris flow, heavy snowfall or avalanche, and a vector used in GIS (Geographic Information System). Based on the data, the obstacle condition of the transportation network on the ground surface is estimated, and this is visualized and displayed by the display device 22 or the like. Here, hereinafter, in the embodiments, the road on which an automobile or the like travels is assumed as the transportation network, but the transportation network also includes a railway on which a train or the like travels.

  Vector data used in GIS is 1) point data (data consisting only of nodes), 2) line data (or arc data. Data consisting of two or more nodes and links connecting the nodes), 3) ) Polygon data (three or more nodes and data configured as a closed area in which nodes are sequentially connected by links) is composed of three types of data.

  Here, the traffic network data can be represented by line data. Also, the buffer zone data can be represented as polygon data.

  A process of acquiring buffer zone data (polygon data of vector data) from traffic network data (line data of vector data) in map data will be described.

  The map data mainly includes traffic network data such as roads. Furthermore, this transportation network data is mainly composed of point data indicating the position of an intersection on a road, and line data in which the point data are connected to each other.

  FIG. 5 is a diagram showing a data structure example of traffic network data. In FIG. 5, FIG. 5A shows traffic network data itself such as roads. Such traffic network data includes data such as intersections and roads.

  Further, FIG. 5B shows point data Px and line data Ly respectively corresponding to the above intersections and roads. The concept of such point data and line data is well known in the related art, and data adopted in GIS (Geographic Information System) can be used as appropriate.

Further, FIG. 5C shows buffer zone data (one-dot chain line) acquired from the line data. The buffer zone data is composed of polygon data indicating a surface. FIG. 6 is a flowchart showing a buffer zone data acquisition process executed by the traffic obstacle estimation system according to the present invention.

  In FIG. 6, when the calculation processing of the buffer zone data is started in step S300, the process proceeds to step S301, and the point data in the traffic network data in the range to be estimated by the traffic obstacle estimation system according to the present invention is calculated. Get line data. Here, the range to be estimated is the same range as the range of the terrain acquired by the aerial photograph.

  Then, in step S302, buffer zone data is created from the point data and the line data. The buffer zone data can be created from the point data and the line data according to a conventionally known concept. The buffer zone data is essentially polygon data of a transportation network having a constant width (for example, 20 m in actual size). However, if the terrestrial resolution (actual size in the real space represented by 1 pixel, unit meter) is not sufficient due to the small scale of aerial photography used, etc., consider the measurement error of the 3D point cloud data. Alternatively, the width of the generated buffer zone data may be made significantly wider than the actual road width (a few times wider than the actual road width). This is to prevent a mistake in which the topographic change portion is out of the buffer zone data and should be presumed to be a traffic obstacle, but not.

  In the subsequent step S303, the buffer zone data acquisition process is terminated.

  Next, from the topographical change data based on the aerial photograph obtained by the surface change analysis system and the buffer zone data based on the traffic network data which is a kind of map data used in the geographical information system, the traffic obstacle estimation system according to the present invention. Will explain the algorithm for estimating the occurrence status of traffic obstacles due to natural disasters.

  FIG. 7 is a diagram showing a flowchart of traffic obstacle estimation processing in the traffic obstacle estimation system according to the present invention.

  In FIG. 7, when the traffic obstacle estimation process is started in step S400, the process proceeds to step S401 to prepare the terrain change data of the estimation target range, and to prepare the buffer zone data as the estimation target range in step S402. ..

  In the subsequent step S403, the zone of interest in the buffer zone data is searched for topographic change data that overlaps with the map and geographical data. Succeedingly, in a step S404, a topographical change amount in the focused zone is acquired.

  Succeedingly, in a step S405, it is determined whether or not the topographic change amount calculated in the step S404 is equal to or more than a predetermined value.

  When the determination in step S405 is YES, it can be determined that some abnormality has occurred, so the flow proceeds to step S406, and it is estimated that a traffic obstacle has occurred. On the other hand, if the determination in step S405 is NO, it can be determined that there is no abnormality, so the flow proceeds to step S407 and it is estimated that no traffic obstacle has occurred.

  Here, it is preferable that the predetermined value of the landform change amount serving as the threshold value used for the determination in step S405 be changed according to the ground resolution of the aerial photographs of the first time period and the second time period used. Here, the terrestrial resolution refers to the actual size (unit: meter) in the real space of one pixel of the aerial photograph. If an aerial photograph is taken using a lens having the same total number of pixels and the same angle of view, the aerial photograph taken from a high altitude has a low ground resolution (the actual size of one pixel in a real space is large). On the contrary, aerial photographs taken from low altitude have high ground resolution (actual size in real space of 1 pixel is small). When an aerial photograph with low ground resolution is used, the predetermined value is set to be large, and when an aerial photograph with high ground resolution is used, the predetermined value is also set to be small.

  In step S408, it is determined whether or not the determination / estimation has been made for all the zones of interest. When the determination in step S408 is no, the process proceeds to step S410, the next target zone is focused, and the process returns to step S404.

  On the other hand, when the determination in step S408 is YES, the process proceeds to step S409, and the display device 22 displays the map data in which the locations between the interpolation points where the traffic obstacle is determined to have been color-coded and displayed. The map data displayed in different colors may be output to the output unit 19 such as a printing device. FIG. 8 is a diagram showing an example of map data in which a portion determined to have a traffic obstacle is color-coded and displayed.

  In step S411, the traffic obstacle estimation process ends.

  As described above, the traffic obstacle estimation system according to the present invention estimates the presence or absence of a traffic obstacle from the data based on the terrain change data of the terrain based on the aerial photograph and the data based on the buffer zone data. According to the traffic obstacle estimation system, it is possible to easily grasp the obstacle of the transportation network such as the road and the railroad immediately after the occurrence of the disaster, and it is possible to utilize the grasped information for the recovery after the disaster.

10 ... System Bus 11 ... CPU (Central Processing Unit)
12 ... RAM (Random Access Memory)
13 ... ROM (Read Only Memory)
14 ... Communication control unit 15 ... Input control unit 16 ... Output control unit 17 ... External storage device control unit 18 ... Input unit 19 ... Output unit 20 ... External storage device 21・ ・ ・ Graphic control unit 22 ・ ・ ・ Display device 101 ・ ・ ・ Aircraft 103 ・ ・ ・ Airframes 104, 105, 106, 107 ・ ・ ・ Propeller 113 ・ ・ ・ Imaging device

Claims (5)

Based on the plurality of aerial photographs acquired in the first period, the first coordinate-calculated three-dimensional point cloud data is acquired, and
The second coordinate-calculated three-dimensional point cloud data is acquired based on a plurality of aerial photographs acquired at a second time different from the first time,
Topography change data acquisition step of acquiring topography change data based on a difference between the first coordinate-calculated three-dimensional point cloud data and the second coordinate-calculated three-dimensional point cloud data,
Preparing buffer zone data obtained from traffic network data included in map data,
Acquiring a topographic change amount in the zone of interest of the buffer zone data from the topographic change data;
When the topography change amount is equal to or greater than a predetermined value, it is estimated that a traffic obstacle has occurred, and when the topography change amount is not equal to or greater than the predetermined value, a step of estimating that a traffic obstacle has not occurred,
A traffic obstacle estimation system characterized by executing. The traffic obstacle estimation system according to claim 1, wherein the predetermined value is different depending on the ground resolution. The traffic obstacle estimation system according to claim 1 or 2, wherein the buffer zone data is obtained from point data and line data used in a GIS (Geographic Information System) . The traffic obstacle estimation system according to any one of claims 1 to 3, wherein the aerial photograph is a plurality of aerial photographs obtained by photographing topography from different positions. The structure of the coordinate-calculated three-dimensional point cloud is prepared by using the Structure from Motion technology and the Multi-view Stereo technology. The traffic according to any one of claims 1 to 4, wherein: Failure estimation system.

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