KR20230136289A - Disaster disaster area automatic detection and earthwork volume calculation system - Google Patents

Abstract

The system for automatically detecting disaster areas and calculating earthwork volume according to the present invention includes an unmanned aerial vehicle that transmits captured images; And when receiving the captured image from the unmanned aerial vehicle, obtain a cloud of 3D points from the captured image, divide the cloud of 3D points into grids of a predetermined size, and based on the average altitude of each grid. Determine whether a disaster or calamity has occurred, and if a disaster or calamity has occurred, extract error points where each 3D point in the damaged area where the disaster or calamity occurred is outside a predetermined range from the average altitude of the corresponding grid, and extract the error points It includes an earthwork volume calculation server that calculates the earthwork volume of the damaged area using the difference between the altitude of the points and the average altitude.

Description

Disaster disaster area automatic detection and earthwork volume calculation system}

The present invention relates to a system for automatically detecting disaster areas and calculating earthwork volume.

As modern society becomes more complex and advanced, the importance of geospatial information among various knowledge information is increasing day by day for efficient use and management of national space.

The field of using spatial information is an industry of global interest, and is widely used, such as Internet-based map services and 3D geographic information services. In particular, the acquisition and use of spatial geographic information data is increasing in order to utilize more accurate global coordinate locations. It is expanding.

Since the early 1990s, advanced cities have been building databases by steadily accumulating spatial geographic information data in preparation for changes in the urban planning environment through the acquisition and construction of spatial geographic information data.

Domestically, digital twin data construction and database creation of the Korean territory are in progress through the acquisition and data construction of spatial geographic information data using unmanned aerial vehicles, such as national-level geographic information system projects and the establishment of each city's urban planning information system.

In addition, recent rapid industrialization and urbanization have led to frequent changes in the national territory, and various technologies are being developed to provide the latest spatial information required for efficient management of the land and cities, spatial information-related industries, and social activities. Spatial information refers to location information about natural or artificial objects in space and various attribute information related thereto, and is creating more added value with the development of IT technology. Spatial information is the basis for creating next-generation industries, and is being used as open spatial information to make people's lives more convenient with the development of smart phones as well as at the national level, such as national land planning and disaster response. Unmanned aerial vehicles can quickly acquire spatial information of the target area and produce various results such as orthoimages and terrain models, so their utility in the field of spatial information construction is increasing recently.

Meanwhile, natural disasters due to global warming are rapidly increasing every year, and typhoons in particular have become more frequent and more powerful. As a result of the Korea Meteorological Administration's analysis of the intensity of typhoons that affected the Korean Peninsula for 10 years from 2009 to 2018, the frequency of occurrence of very strong typhoons with a maximum wind speed of 44 m/s or more accounts for more than 50%, and about 25% of these have a maximum wind speed of 44 m/s or more. It reaches an ultra-strong level of 55 m/s or more. Greenpeace, a global environmental group, predicts that if greenhouse gas emissions are maintained at the current level by 2030, typhoons and sea level rise will have a complex effect on Korea, causing 5% of the country to be submerged and 3.32 million people to be affected by flooding. do. A systematic management system is needed to respond to increasingly serious disasters due to climate change.

To this end, technology is required to automatically detect disaster areas using unmanned aerial vehicles in energy management, city control, and fire and disaster response, and to calculate the size of the damaged area in the disaster area.

The present invention was derived to solve the problems of the prior art, and the purpose of the present invention is to provide a system for automatically detecting disaster areas and calculating earthwork volume using an unmanned aerial vehicle.

Another purpose of the present invention is to provide a system for automatically detecting disaster areas and calculating earthwork volume, which can calculate the earthwork volume in disaster disaster areas required for data utilization such as energy management, city control, fire and disaster damage prediction, etc.

A system for automatically detecting disaster areas and calculating earthwork volume according to one aspect of the present invention for solving the above technical problem includes an unmanned aerial vehicle for transmitting captured images; And when receiving the captured image from the unmanned aerial vehicle, obtain a cloud of 3D points from the captured image, divide the cloud of 3D points into grids of a predetermined size, and based on the average altitude of each grid. Determine whether a disaster or calamity has occurred, and if a disaster or calamity has occurred, extract error points where each 3D point in the damaged area where the disaster or calamity occurred is outside a predetermined range from the average altitude of the corresponding grid, and extract the error points It includes an earthwork volume calculation server that calculates the earthwork volume of the damaged area using the difference between the altitude of the points and the average altitude.

The unmanned aerial vehicle may include a stereo camera that acquires the stereo image data; And it may include a thermal image camera that acquires the thermal image data.

The earthwork volume calculation server may perform clustering to extract the damaged area after extracting the error points, and extract the outline of the damaged area based on the clustering.

The earthwork volume calculation server extracts the outline of the damaged area, generates a two-dimensional grid shape based on error points, performs an intersection operation between the grid shape and the damaged area outline, and calculates the error points and the grid shape. The altitude difference of the average altitude is multiplied to create at least one polygonal solid, and the total amount of earthworks in the damaged area can be calculated by the sum of all polygonal solids.

The earthwork volume calculation server may generate a polygonal solid by integrating the altitude difference between the altitudes of error points within the damage area and the average altitude.

The earthwork volume calculation server extracts the outline of the damaged area, generates a two-dimensional grid based on error points, performs an intersection operation between the grid shape and the damaged area outline, and calculates the area of the grid shape based on the point and the corresponding The altitude of the error point and the altitude difference between the average altitude can be multiplied to calculate the earthwork volume of the grid shape based on the point, and the earthwork volume of the two-dimensional grid figures based on the error point can be added to calculate the total earthwork volume of the damaged area.

In addition, a method for automatically detecting a disaster area and calculating the amount of earthworks according to one aspect of the present invention includes the steps of transmitting an image captured by an unmanned aerial vehicle; Upon receiving the captured image from the unmanned aerial vehicle by an earthwork volume calculation server, obtaining a cloud of three-dimensional points from the captured image; dividing the cloud of three-dimensional points into grids of a predetermined size by the earthwork volume calculation server; determining whether a disaster or calamity has occurred based on the average altitude of each grid; When a disaster or calamity has occurred, extracting error points where each 3D point in the damaged area where the disaster or calamity occurred is outside a predetermined range from the average altitude of the corresponding grid; and calculating the earthwork volume of the damaged area using the difference between the altitude of the error points and the average altitude.

The step of extracting the error points includes extracting the error points by the earthwork volume calculation server and then performing clustering to extract the damaged area; And it may include extracting the outline of the damaged area based on the clustering by the earthwork volume calculation server.

The step of calculating the earthwork volume includes extracting an outline of the damaged area by the earthwork volume calculation server and then generating a two-dimensional grid figure based on error points; performing an intersection operation between the grid shape and the outline of the damaged area by the earthwork volume calculation server; generating at least one polygonal hexahedron by multiplying the error points and an altitude difference between the average altitude of the grid solid by the earthwork volume calculation server; And it may include calculating the total earthwork volume of the damaged area by the earthwork volume calculation server as the sum of all the polygons.

The step of calculating the earthwork volume includes extracting an outline of the damaged area by the earthwork volume calculation server and then generating a two-dimensional grid based on error points; performing an intersection operation between the grid shape and the outline of the damaged area by the earthwork volume calculation server; calculating the earthwork volume of the point-based grid shape by multiplying the area of the point-based grid shape by the altitude difference between the altitude of the corresponding error point and the average altitude by the earthwork quantity calculation server; and calculating the total earthwork volume of the damaged area by adding the earthwork volume of the two-dimensional grid figures based on the error point by the earthwork volume calculation server.

According to the automatic detection and earthwork volume calculation system of the disaster area of the present invention described above, the earthwork volume of the disaster disaster area can be calculated using an unmanned aerial vehicle.

In addition, according to the automatic detection of disaster area and earthwork volume calculation system of the present invention, it is possible to calculate the earthwork volume of disaster area required for data utilization such as energy management, city control, fire and disaster damage prediction, etc.

Figure 1 is a diagram schematically showing a system for automatically detecting disaster areas and calculating earthwork volume according to the present invention.
Figure 2 is a diagram showing the message flow in the automatic disaster area detection and earthwork volume calculation system according to an embodiment of the present invention.
Figure 3 is a flowchart showing the operation of the earthwork volume calculation server according to an embodiment of the present invention.
Figure 4 is a diagram showing an example of a point cloud generated according to an embodiment of the present invention.
Figure 5 is a diagram showing an example of a point cloud divided into grid units according to an embodiment of the present invention.
Figure 6 is a diagram showing an example of a point cloud reacquired according to an embodiment of the present invention.
Figure 7 is a diagram showing error points extracted based on the average altitude according to an embodiment of the present invention.
Figure 8 is a diagram illustrating error point data clustering according to an embodiment of the present invention.
Figure 9 is a diagram for explaining a method for calculating the amount of earthwork in a damaged area according to an embodiment of the present invention.

Specific structural or functional descriptions of the embodiments according to the concept of the present invention disclosed in this specification are merely illustrative for the purpose of explaining the embodiments according to the concept of the present invention, and the embodiments according to the concept of the present invention are It may be implemented in various forms and is not limited to the embodiments described herein.

Since the embodiments according to the concept of the present invention can make various changes and have various forms, the embodiments will be illustrated in the drawings and described in detail in this specification. However, this is not intended to limit the embodiments according to the concept of the present invention to specific disclosed forms, and includes all changes, equivalents, or substitutes included in the spirit and technical scope of the present invention.

The terms used in this specification are merely used to describe specific embodiments and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as “comprise” or “have” are intended to indicate the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in this specification, but are not intended to indicate the presence of one or more other features. It should be understood that this does not exclude in advance the possibility of the existence or addition of elements, numbers, steps, operations, components, parts, or combinations thereof.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

Figure 1 is a diagram schematically showing a system for automatically detecting disaster areas and calculating earthwork volume according to the present invention.

Referring to FIG. 1, the system for automatically detecting disaster areas and calculating earthwork volume according to this embodiment includes an unmanned aerial vehicle (UAV) 100, an earthwork volume calculation server 200, and WEB GIS (300). .

The unmanned aerial vehicle 100 may be, for example, a drone. The unmanned aerial vehicle 100 can acquire spatial data about a specific area while flying. For example, the unmanned aerial vehicle 100 can acquire or acquire stereo/thermal (eg, infrared) images in real time for a rapidly changing area of 0.25 km ² .

To this end, the unmanned aerial vehicle 100 may include, for example, a stereo camera, a thermal imaging camera, an inertial measurement unit (IMU), and a GPS device.

A stereo camera may also be referred to as a stereo vision camera. The stereo camera may include a left camera and a right camera. The left camera is located on the left side of the unmanned aerial vehicle (100) and captures a left image as seen by a person's left eye, and the right camera is located on the right side of the unmanned aerial vehicle (100) and captures a right image as seen by a person's right eye. can be filmed.

In the case of a stereo camera, the position of the camera is fixed, so the same matching process can be applied using stereo camera information. In addition, since each moment is an independent shot and the lighting conditions are the same through synchronization of left and right images, it has the advantage of being resistant to changes in weather and brightness. In addition, because point cloud information is generated for each shooting location point, 3D information can be constructed even with a small number of photos, and since point cloud information with absolute expression can be generated for each stereo image, there is no or little accumulation of errors. has.

The focal length of a stereo camera varies depending on the length of the baseline, which is the distance between the two cameras, so by adjusting the baseline, the equipment can be configured to be suitable for shooting within the range of 10M to 150M from the ground. Additionally, the stereo vision camera can be configured to acquire gray scale images to speed up data processing.

In this way, one depth image can be created by matching the left and right images captured through the two left and right cameras provided in the stereo camera.

Thermal imaging cameras capture thermal distribution images of an object (hereinafter referred to as “thermal imaging images”). When the thermal image of an object captured through this thermal imaging camera matches the depth image, a three-dimensional thermal image can be generated in which the thermal image is displayed on the depth image.

Additionally, in this embodiment, a stereo image by a stereo camera basically does not include a ground reference point. In the prior art, by installing a ground reference point on the ground, images taken at different flight altitudes, positions, or postures are matched to each other using meteorological reference points included in these images, or external facial expression elements are determined. Meanwhile, in this embodiment, the position and rotation angle of sensors such as a stereo camera are accurately determined using real-time kinematic (RTK) GPS and an inertial measurement unit (IMU) mounted on the unmanned aerial vehicle 100, thereby accurately determining external facial expression elements. The ground control point and ground control point surveying process for determining can be omitted.

Additionally, the unmanned aerial vehicle 100 may include a processor that calculates a posture vector using the 3-axis angular velocity data from the gyro sensor. Additionally, drones can be equipped with equipment such as a barometer to support stable flight.

The unmanned aerial vehicle 100 described above may include an inertial measurement unit (IMU) that generates attitude values and a global positioning system (GPS) device that generates location data.

An inertial measurement unit (IMU) is an electronic device that uses a combination of accelerometers, tachometers, and sometimes magnetometers to measure and report a body's specific forces, angular rates, and sometimes magnetic fields surrounding the body. Inertial measurement devices are commonly used to control aircraft, including unmanned aerial vehicles (UAVs) and spacecraft, including satellites and land-based vehicles. Recent developments allow the production of GPS devices that can use IMUs. The IMU allows GPS receivers to be used when GPS-signals are not available, such as in tunnels, inside buildings, or when there is electromagnetic interference.

The inertial measurement device can be replaced by an inertial navigation system (INS) and can be equipped with an acceleration sensor and a gyro sensor.

In the inertial measurement device, the 3-axis acceleration data (Ax, Ay, Az) of the acceleration sensor is converted into preset 3-dimensional acceleration data (An, Ae, Av) by the axis transform module, and the converted 3-dimensional acceleration Data (An, Ae, Av) can be converted into position data and velocity data and output. At this time, gyro torque signals corresponding to the position data and velocity data may be input to the posture vector calculation module that processes the three-axis angular velocity data (Wx, Wy, Wz) of the gyro sensor. The output of the posture vector calculation module may be input to the axis transformation module.

In this embodiment, the location data is generated by a GPS device, but it can be replaced by a GNSS (Global Navigation System). GNSS provides information about the location, speed, and velocity of objects on the ground using artificial satellites orbiting space. It can capture precise location information with a resolution of less than 1 m, and is widely applied not only to military purposes but also to the private sector, such as location guidance for transportation means such as aircraft, ships, and automobiles, and geodetic emergency rescue communication. GNSS consists of one or more satellites, a receiver capable of receiving signals, a ground-based monitoring station, and a system integrity monitoring system. This is a method of determining the location of the receiver by receiving radio waves transmitted from a satellite from the receiver and finding the distance.

The unmanned aerial vehicle 100 may be remotely controlled wirelessly by a remote control device (not shown). The remote control device can set the autonomous flight path of the unmanned aerial vehicle (100). The remote control device can perform First Person View (FPV) video monitoring. Additionally, the remote control device may monitor the status of the unmanned aerial vehicle 100 and gimbal.

The unmanned aerial vehicle 100 may transmit images captured by a stereo camera or a thermal imaging camera to the earthwork volume calculation server 200 through the communication network 10. According to another embodiment, the unmanned aerial vehicle 100 may transmit an image captured by a stereo camera or a thermal imaging camera to the earthwork volume calculation server 200 through a remote control device.

When the earthwork volume calculation server 200 receives an image captured by a stereo camera or a thermal imaging camera from the unmanned aerial vehicle 100 through the communication network 10, it calculates the earthwork volume of the disaster area based on the image.

The message flow between the unmanned aerial vehicle 100 and the earthwork volume calculation server 200 will be described with reference to FIG. 2.

Figure 2 is a diagram showing the message flow in the automatic disaster area detection and earthwork volume calculation system according to an embodiment of the present invention.

Referring to FIG. 2, first, the unmanned aerial vehicle 100 acquires or acquires an image of a specific area through a stereo camera or a thermal imaging camera. The image is preferably a three-dimensional image, such as a stereo image or a thermal image. The specific area may be an area monitored for disasters. Alternatively, the specific area may be an existing maintenance area. For example, at a construction site, problems may arise with buildings under construction, and these areas can become maintenance areas.

The unmanned aerial vehicle 100 can obtain or obtain images of a specific area by photographing a predetermined area using a stereo camera or a thermal imaging camera (S110). The unmanned aerial vehicle 100 transmits the acquired image to the earthwork volume calculation server 200 through the communication network 10 (S120). According to another embodiment, the unmanned aerial vehicle 100 may transmit an image captured by a stereo camera or a thermal imaging camera to the earthwork volume calculation server 200 through a remote control device.

When the earthwork volume calculation server 200 receives a captured image of an existing maintenance area from the unmanned aerial vehicle 100, it acquires the location of a specific area where the image was captured (S130). The location may be provided from the unmanned aerial vehicle 100 as GPS information.

After obtaining the location of the specific area, the earthwork volume calculation server 200 may request a Digital Surface Model (DSM) of the location from the Geographic Information System (GIS) (WEB GIS) on the web (S140). When the earthwork volume calculation server 200 receives the DSM at the location of the specific area from the WEB GIS 300 (S150), it can determine whether a disaster or calamity has occurred by comparing the DSM with the captured image (S160) ). For example, the earthwork volume calculation server 200 can determine whether a disaster or calamity has occurred by comparing the altitude of the specific area in the DSM with the altitude of the DSM at the corresponding location.

If a disaster or calamity has occurred, the earthwork volume calculation server 200 calculates the earthwork volume of the disaster or disaster location, that is, the damaged area (S170). Optionally, the earthwork volume calculation server 200 may transmit the calculated earthwork volume to WEB GIS (300).

Figure 3 is a flowchart showing the operation of the earthwork volume calculation server according to an embodiment of the present invention.

When the earthwork volume calculation server 200 receives an image from the unmanned aerial vehicle 100, it detects the disaster area from the received image and calculates the earthwork volume of the disaster area.

Specifically, referring to FIG. 2, the earthwork volume calculation server 200 may acquire a 3D point cloud from an image captured by the unmanned aerial vehicle 100 (S201). A 3D point cloud is a set of points spread out in 3D (three-dimensional) space. The image transmitted from the unmanned aerial vehicle 100 is an image captured by a stereo camera or a thermal imaging camera and includes three-dimensional spatial information.

Figure 4 is a diagram showing an example of a point cloud generated according to an embodiment of the present invention.

Referring to FIG. 4, the earthwork volume calculation server 200 may generate a point cloud including 3D points from the image received from the unmanned aerial vehicle 100.

For example, the earthwork volume calculation server 200 may generate an epipolar image pair from a captured image, generate a disparity map from the epipolar image pair, and then generate a 3D point cloud.

When the earthwork volume calculation server 200 acquires a cloud of 3D points, it divides the cloud of 3D points into grids of predetermined sizes and calculates the average altitude of each grid (S203).

Figure 5 is a diagram showing an example of a point cloud divided into grid units according to an embodiment of the present invention.

Referring to FIG. 5, the 3D point cloud is divided into grid units of a predetermined size by the earthwork volume calculation server 200. Additionally, the average elevation of each grid is calculated.

The earthwork volume calculation server 200 may determine whether a disaster or disaster has occurred in a shooting area, such as a maintenance area, based on the point cloud divided into grid units and the average altitude of each grid (S205). For example, the earthwork volume calculation server 200 may request the previous Digital Surface Model (DSM) of the corresponding area from the Geographic Information System (GIS) (WEB GIS) on the web. The earthwork volume calculation server 200 can determine whether a change has occurred in the area by comparing the average altitude of the current point cloud with the previous DSM (Digital Surface Model) of the area.

If a change has occurred, that is, if a disaster or disaster has occurred in the maintenance area, the earthwork volume calculation server 200 reacquires the 3D point cloud of the disaster site (S207).

Figure 6 is a diagram showing an example of a point cloud reacquired according to an embodiment of the present invention.

As shown in FIG. 6, the point cloud for the disaster area or damaged area can be reacquired. In Figure 6, the area colored in pink is the disaster area.

Next, the earthwork volume calculation server 200 extracts an error point by comparing the altitude of the disaster site point cloud and the average altitude (S209).

Figure 7 is a diagram showing error points extracted based on the average altitude according to an embodiment of the present invention.

Referring to FIG. 7, error points refer to points where the altitude of the cloud is outside a predetermined range compared to the average altitude. That is, the earthwork volume calculation server 200 compares the altitude of the 3D points at the disaster site and the average altitude of the grid, and determines whether the 3D point at the disaster site has an altitude that is outside a predetermined range from the average altitude of the grid. can do.

Accordingly, the earthwork volume calculation server 200 can detect error points where 3D points at the disaster site deviate from a predetermined range from the average altitude of the corresponding grid.

The earthwork volume calculation server 200 extracts error points and then performs clustering to extract the damaged area (S211). That is, the earthwork volume calculation server 200 determines whether each 3D point at the disaster site is outside a predetermined range from the average altitude of the grid, and sets an error point where the 3D point is outside the predetermined range from the average altitude of the grid. collect them.

Figure 8 is a diagram illustrating error point data clustering according to an embodiment of the present invention. In Figure 8, the number of clusters from which error points are collected is three. For example, error points where the distance between the positions of the error points is within a threshold may be clustered.

Next, the earthwork volume calculation server 200 extracts the outline of the damaged area based on the clustering (S213).

Referring to FIG. 8, the earthwork volume estimation server 200 may extract the outline of the damage area based on the positions of error points located at the edge of the cluster among the clustered error points.

Next, the earthwork volume calculation server 200 generates a two-dimensional grid figure based on the error point (S215).

Figure 9 is a diagram for explaining a method for calculating the amount of earthwork in a damaged area according to an embodiment of the present invention. Figure 9(a) shows an example of a two-dimensional grid shape. For example, the earthwork volume calculation server 200 may generate a point reference grid on the damaged area.

After generating a point-based grid, the earthwork volume calculation server 200 performs an intersection operation between the grid shape and the outline of the damaged area (S217).

Figure 9(b) shows an example of the intersection of a grid shape and the outline of a damaged area. As shown in FIG. 9(b), an intersection operation may be performed on the grid shape 30 and the outline 31 of the damaged area.

Subsequently, the earthwork volume calculation server 200 generates a polygonal solid by multiplying the error points and the altitude difference between the average altitude of the grid figure 30 (S219). A polygonal spheroid can be created by integrating (multiplying) the altitude difference between the altitudes of the error points within the damage area and the average altitude.

Alternatively, the earthwork volume calculation server 200 may multiply the area of the point-based grid figure by the difference between the altitude of the point and the average altitude, as shown in FIG. 9(c).

Figure 9(c) shows an example of multiplying the difference between the area of the point reference grid and the average altitude. As shown in FIG. 9(c), the amount of earthwork in the corresponding reference grid shape can be calculated by multiplying the area of the reference grid where each error point is located and the altitude difference between each error point and the average altitude.

Next, the earthwork volume calculation server 200 calculates the earthwork volume as the sum of all polygonal bodies (S221). Alternatively, the earthwork volume calculation server 200 may calculate the total earthwork volume by adding the earthwork volume in a plurality of reference grid shapes.

In this way, according to embodiments of the present invention, it is possible to calculate the amount of earthwork in a disaster area required for data utilization such as energy management, city control, fire and disaster damage prediction, etc.

Although the present invention has been described with reference to the drawings, it is merely illustrative, and it is obvious to those skilled in the art that various modifications and other equivalent embodiments can be made therefrom. Therefore, the true technical protection scope of the present invention should be determined by the technical spirit of the attached claims.

10: Communication network
100: drone
200: Earthwork volume calculation server
300: WEB GIS

Claims (10)

Unmanned aerial vehicles that transmit captured images; and
When receiving the captured image from the unmanned aerial vehicle, a cloud of 3D points is obtained from the captured image, the cloud of 3D points is divided into grids of a predetermined size, and disaster detection is performed based on the average altitude of each grid. Or, determine whether a disaster has occurred, and if a disaster or calamity has occurred, extract error points where each 3D point in the damaged area where the disaster or calamity occurred is outside a predetermined range from the average altitude of the corresponding grid, and determine the error point. A disaster area automatic detection and earthwork volume calculation system including an earthwork volume calculation server that calculates the earthwork volume of the damaged area using the difference between the altitude and the average altitude.
In claim 1,
The unmanned aerial vehicle may include a stereo camera that acquires the stereo image data; And a disaster area automatic detection and earthwork volume calculation system including a thermal imaging camera that acquires the thermal imaging image data.
In claim 1,
The earthwork volume calculation server extracts the error points and then performs clustering to extract the damaged area, and extracts the outline of the damaged area based on the clustering. A disaster area automatic detection and earthwork volume calculation system.
In claim 3,
The earthwork volume calculation server extracts the outline of the damaged area, generates a two-dimensional grid shape based on error points, performs an intersection operation between the grid shape and the damaged area outline, and calculates the error points and the grid shape. A disaster area automatic detection and earthwork volume calculation system that generates at least one polygonal solid by multiplying the elevation difference of the average altitude, and calculates the total earthwork volume in the damaged area by the sum of all polygonal solids.
In claim 4,
The earthwork volume calculation server is a disaster area automatic detection and earthwork volume calculation system in which the earthwork volume calculation server integrates the elevation difference between the altitudes of error points in the damaged area and the average altitude to generate a polygonal body.
In claim 3,
The earthwork volume calculation server extracts the outline of the damaged area, generates a two-dimensional grid based on error points, performs an intersection operation between the grid shape and the damaged area outline, and calculates the area of the grid shape based on the point and the corresponding Automatic disaster area disaster area that multiplies the altitude of the error point and the altitude difference between the average altitude to calculate the earthwork volume of the grid shape based on the point, and calculates the total earthwork volume of the damaged area by adding the earthwork volume of the two-dimensional grid figures based on the error point. Detection and earthwork volume estimation system.
In the method of automatically detecting disaster areas and calculating earthwork volume,
Steps for transmitting video captured by a drone
Upon receiving the captured image from the unmanned aerial vehicle by an earthwork volume calculation server, obtaining a cloud of three-dimensional points from the captured image;
dividing the cloud of three-dimensional points into grids of a predetermined size by the earthwork volume calculation server;
determining whether a disaster or calamity has occurred based on the average altitude of each grid;
When a disaster or calamity has occurred, extracting error points where each 3D point in the damaged area where the disaster or calamity occurred is outside a predetermined range from the average altitude of the corresponding grid; and
A method for automatically detecting a disaster area and calculating the amount of earthworks, comprising the step of calculating the amount of earthworks in the damaged area using the difference between the altitudes of the error points and the average altitude.
In claim 7,
The step of extracting the error points is
Extracting the error points by the earthwork volume calculation server and then performing clustering to extract the damaged area; and
A method for automatically detecting a disaster area and calculating the amount of earthworks, comprising the step of extracting an outline of the damaged area based on the clustering by the earthwork amount calculation server.
In claim 7,
The step of calculating the earthwork volume is
Extracting the outline of the damaged area by the earthwork volume calculation server and then generating a two-dimensional grid shape based on error points;
performing an intersection operation between the grid shape and the outline of the damaged area by the earthwork volume calculation server;
generating at least one polygonal hexahedron by multiplying the error points and an altitude difference between the average altitude of the grid solid by the earthwork volume calculation server; and
A method for automatically detecting a disaster area and calculating the earthwork volume, comprising the step of calculating the total earthwork volume of the damaged area by the earthwork volume calculation server as the sum of all the polygons.
In claim 7,
The step of calculating the earthwork volume is
Extracting the outline of the damaged area by the earthwork volume calculation server and then generating a two-dimensional grid based on error points;
performing an intersection operation between the grid shape and the outline of the damaged area by the earthwork volume calculation server;
calculating the earthwork volume of the point-based grid shape by multiplying the area of the point-based grid shape by the altitude difference between the altitude of the corresponding error point and the average altitude by the earthwork quantity calculation server; and
A method for automatically detecting a disaster area and calculating the earthwork volume, comprising the step of calculating the total earthwork volume of the damaged area by adding the earthwork volume of the two-dimensional grid figures based on the error point by the earthwork volume calculation server.