Landslide volume estimation method and device based on post-disaster remote sensing data and medium

Abstract

The invention discloses a landslide volume estimation method and device based on post-disaster remote sensing data and a medium, and relates to the technical field of remote sensing. According to the aerial image data of the unmanned aerial vehicle, the boundary range of the left and right sides of a landslide accumulation area, the boundary range of a landslide wall area and the boundary range of an area required for slope toe reconstruction are identified and delineated; according to the three-dimensional laser ground point cloud data in the area required by slope toe reconstruction, reconstructing an original slope toe terrain boundary before the occurrence of the landslide; determining the elevation data of the original slope toe before the occurrence of the landslide based on the original slope toe terrain boundary line before the occurrence of the reconstructed landslide; reconstructing a sliding interface based on the elevation data of the original slope toe before the occurrence of the landslide, and the three-dimensional laser ground point cloud data in the boundary range of the left and right sides of the landslide accumulation area and the boundary range of the landslide wall area; and determining the landslide volume according to the elevation data of the sliding interface and the elevation data of the landslide accumulation area. The landslide volume estimation precision can be improved.

Description

Technical Field

[0001] The present application relates to the field of remote sensing technology, and particularly to a landslide volume estimation method, device, and medium based on post-disaster remote sensing data. Background Technique

[0002] Landslide disasters are one of the most frequent types of geological disasters globally, causing great impacts on human society and the natural environment. Especially in mountainous areas, due to unique topographical and geomorphological features and increasingly severe extreme weather, the frequency and damage caused by landslide disasters are particularly serious.

[0003] The landslide volume is a key parameter for evaluating the scale of a landslide, assessing the scope and degree of disaster impact. In the emergency rescue after a landslide disaster and the engineering construction in the landslide accumulation area, landslide volume estimation is an extremely important task. Currently, landslide volume estimation mainly uses engineering methods such as geophysical exploration and geological drilling to generate a three-dimensional model of the landslide accumulation body and calculate the volume of the fitted landslide accumulation body model. However, such methods require personnel to reach the landslide site, consuming a large amount of manpower, material resources, and time; estimating the landslide volume can also be achieved by comparing the changes in elevation data before and after the landslide. However, if the landslide body was not continuously monitored before the disaster, it will be difficult to obtain high-precision elevation data before the landslide occurred; in addition, related technologies have proposed a method for estimating the fitted landslide volume by interpolating and fitting the sliding interface based on high-precision elevation data after the landslide occurred. However, this method ignores the importance of the original foot-of-slope topography in the fitting of the sliding interface, resulting in a large error in the landslide volume estimation. Summary of the Invention

[0004] The purpose of the present application is to provide a landslide volume estimation method, device, and medium based on post-disaster remote sensing data, which can solve the problem of inaccurate landslide volume estimation in related technologies.

[0005] To achieve the above object, the present application provides the following solutions.

[0006] In a first aspect, the present application provides a landslide volume estimation method based on post-disaster remote sensing data, including: obtaining post-disaster remote sensing data, where the post-disaster remote sensing data includes three-dimensional laser ground point cloud data and unmanned aerial vehicle (UAV) aerial image data of the landslide disaster area; the landslide disaster area includes a landslide accumulation area, a landslide wall area, and an area required for toe reconstruction; identifying and delineating the boundary ranges on the left and right sides of the landslide accumulation area, the boundary range of the landslide wall area, and the boundary range of the area required for toe reconstruction according to the UAV aerial image data; reconstructing the original toe terrain boundary before the landslide occurred based on the three-dimensional laser ground point cloud data within the area required for toe reconstruction; determining the elevation data of the original toe before the landslide occurred based on the reconstructed original toe terrain boundary before the landslide occurred; the elevation data is determined based on the values on the Z-axis of the three-dimensional laser ground point cloud data in a spatial rectangular coordinate system; the spatial rectangular coordinate system is established with the central meridian and the equator as the center, the projection of the equator as the X-axis, and the projection of the central meridian as the Y-axis; reconstructing the sliding interface based on the elevation data of the original toe before the landslide occurred, and the three-dimensional laser ground point cloud data within the boundary ranges on the left and right sides of the landslide accumulation area and the boundary range of the landslide wall area; determining the landslide volume according to the elevation data of the sliding interface and the elevation data of the landslide accumulation area.

[0007] In a second aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the landslide volume estimation method based on post-disaster remote sensing data described above.

[0008] In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the landslide volume estimation method based on post-disaster remote sensing data described above.

[0009] According to the specific embodiments provided by the present application, the following technical effects are disclosed in the present application.

[0010] This application reconstructs the original toe terrain boundary before the landslide occurred using the three-dimensional laser ground point cloud data within the area required for toe reconstruction in the landslide disaster area; based on the UAV aerial image data, it identifies and demarcates the boundary ranges on both sides of the landslide accumulation area, the boundary range of the landslide wall area, and the boundary range of the area required for toe reconstruction, ensuring the accuracy of the terrain boundaries in the landslide disaster area and providing the accuracy and reliability for subsequent landslide volume estimation results. Based on the reconstructed original toe terrain boundary before the landslide occurred, the elevation data of the original toe before the landslide occurred is determined, avoiding the problem in the prior art where inaccurate elevation data of the original toe before the landslide leads to inaccurate subsequent landslide volume estimation. Further, based on the elevation data of the original toe before the landslide occurred, and the three-dimensional laser ground point cloud data within the boundary ranges on both sides of the landslide accumulation area and the boundary range of the landslide wall area, the sliding interface is reconstructed. And based on the elevation data of the reconstructed sliding interface and the elevation data of the landslide accumulation area, high-precision landslide volume estimation is achieved. This application takes into account the importance of the original toe terrain in sliding interface fitting, so it reconstructs the original toe terrain boundary before the landslide occurred, and based on the reconstructed original toe terrain boundary before the landslide occurred, and the three-dimensional laser ground point cloud data within the boundary ranges on both sides of the landslide accumulation area and the boundary range of the landslide wall area, the sliding interface is reconstructed. It can significantly improve the estimation accuracy of the landslide volume and provide important data decision support for the emergency rescue and engineering construction of landslide disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0012] Figure 1 It is a schematic flow chart of a method for estimating landslide volume based on post-disaster remote sensing data provided in an embodiment of the present application.

[0013] Figure 2(a) is a schematic diagram of the UAV aerial image data of the landslide disaster area.

[0014] Figure 2(b) is a schematic diagram of the three-dimensional laser ground point cloud data of the landslide disaster area.

[0015] Figure 3(a) is a schematic diagram of the UAV aerial image data of each part of the landslide disaster area.

[0016] Figure 3(b) is a three-dimensional shaded diagram of the elevation data of each part of the landslide disaster area. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0018] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] Embodiment 1: As Figure 1 shown, the present application provides a landslide volume estimation method based on post-disaster remote sensing data, including steps 101 - 106.

[0020] Step 101: Obtain post-disaster remote sensing data, where the post-disaster remote sensing data includes three-dimensional laser ground point cloud data and unmanned aerial vehicle (UAV) aerial image data of the landslide disaster area; the landslide disaster area includes a landslide accumulation area, a landslide wall area, and an area required for slope foot reconstruction.

[0021] Step 102: According to the UAV aerial image data, identify and demarcate the boundary ranges on the left and right sides of the landslide accumulation area, the boundary range of the landslide wall area, and the boundary range of the area required for slope foot reconstruction.

[0022] Step 103: According to the three-dimensional laser ground point cloud data within the area required for slope foot reconstruction, reconstruct the original slope foot topographic boundary before the landslide occurred.

[0023] Step 104: Based on the reconstructed original slope foot topographic boundary before the landslide occurred, determine the elevation data of the original slope foot before the landslide occurred; the elevation data is determined based on the values on the Z-axis of the three-dimensional laser ground point cloud data in a spatial rectangular coordinate system; the spatial rectangular coordinate system is established with the central meridian and the equator as the center, the projection of the equator as the X-axis, and the projection of the central meridian as the Y-axis.

[0024] Among them, the UTM (Universal Transverse Mercator) projection is a widely used map projection method. Each longitude zone of the Universal Transverse Mercator projection is defined by the following coordinate system: Center (origin): The intersection of the central meridian and the equator. X-axis: Based on the projection of the equator, representing the east-west direction (eastward is positive). Y-axis: Based on the projection of the central meridian, representing the north-south direction (northward is positive).

[0025] Step 105: Reconstruct the sliding interface based on the elevation data of the original slope foot before the landslide occurs, and the three-dimensional laser ground point cloud data within the boundary range of the left and right sides of the landslide accumulation area and the boundary range of the landslide wall area.

[0026] Step 106: Determine the volume of the landslide according to the elevation data of the sliding interface and the elevation data of the landslide accumulation area.

[0027] In some embodiments, step 103 specifically includes: reconstructing the original slope foot terrain boundary before the landslide occurs using a spatial sphere interpolation method based on the three-dimensional laser ground point cloud data in the required area for slope foot reconstruction; the calculation formula of the spatial sphere interpolation method is as follows.

[0028] .

[0029] in,( , , ) is the first The values ​​of the three-dimensional laser ground point cloud data on the X-axis, Y-axis, and Z-axis in the spatial rectangular coordinate system; =1,2,..., ; is the total number of 3D laser ground point cloud data; is the coordinate value of the center of the sphere in the spatial rectangular coordinate system used by the spatial spherical interpolation method; is the radius of the sphere.

[0030] In some embodiments, step 105 specifically includes: reconstructing the sliding interface based on the gradient descent Kriging interpolation method; the calculation formula of the gradient descent Kriging interpolation method is: .

[0031] in, To reconstruct any point selected on the sliding interface o Corresponding elevation data; is a point randomly selected from the boundary range of the left and right sides of the landslide accumulation area, the boundary range of the landslide wall area, and the boundary range of the reconstructed original slope foot. Elevation data on the Z axis of the spatial rectangular coordinate system; n is the number of points randomly selected from the boundary ranges of the left and right sides of the landslide accumulation area, the boundary range of the landslide wall area, and the boundary range of the area required for slope foot reconstruction; For point Weight coefficient.

[0032] in, , for example, , As a pointo The true value of n is unknown. Assume that o has a value of 3, and interpolation can only be performed through the 3 points around point , and . That is, the values of are known, and is the weight of , and the sum of the values of is 1. In this way, the estimated value of is calculated. For example, is 1200, is 1260, is 1300; is 0.2, is 0.5, is 0.3, + + = 1. Then = 1200×0.2 + 1260×0.5 + 1300×0.3 = 1260.

[0033] In some embodiments, before step 106, step 201 is further included.

[0034] Step 201: With the goal of minimizing the value of the error function, update the elevation data corresponding to each point on the reconstructed sliding interface.

[0035] In some embodiments, step 201 specifically includes: with the goal of minimizing the value of the error function, according to , update to determine the weight coefficient of the updated point ; where is the weight coefficient of the updated point ; α is the learning rate; ; and j are respectively two points arbitrarily selected from the boundary ranges on the left and right sides of the landslide accumulation area, the boundary range of the landslide wall area, and the boundary range of the reconstructed original toe of the slope; is the semi - variance of the elevation data corresponding to point and point j respectively; is the planar distance between point and point j ; w is the slope; is the intercept; according to the updated weight coefficient and the calculation formula of the Kriging interpolation method of gradient descent, determine the updated ; Use the updated as the elevation data corresponding to the points on the final reconstructed sliding interface o .

[0036] In some embodiments, the calculation formula of the error function is: .

[0037] Wherein, J is the error function; ; is the semi-variance of the elevation data of the left and right boundaries of the landslide accumulation area, the landslide wall area, and the inner points of the original toe of the reconstructed slope and the points j ; is the semi-variance of the elevation data corresponding to the point and the point o respectively; r oo is the semi-variance of the elevation data corresponding to the point o ; λ j is the weight coefficient; is the planar distance between the point and the point j ; w is the slope; b is the intercept.

[0038] In some embodiments, according to the elevation data of the sliding interface and the elevation data of the landslide accumulation area, the landslide volume is determined, specifically including: randomly selecting multiple points on the reconstructed sliding interface; using the original elevation data corresponding to each point on the landslide accumulation area as the elevation data of the landslide accumulation area; using the elevation data corresponding to each point on the reconstructed sliding interface as the elevation data of the sliding interface; determining the landslide volume according to the pixel area, the original elevation data corresponding to each point on the landslide accumulation area, and the elevation data corresponding to each point on the reconstructed sliding interface.

[0039] In some embodiments, the calculation formula of the landslide volume is: .

[0040] Wherein, V is the landslide volume; M is the landslide disaster area; is the original elevation data corresponding to the point o on the landslide accumulation area; is the elevation data corresponding to the point o on the reconstructed sliding interface; S is the pixel area.

[0041] Among them, grid division is performed on the reconstructed sliding interface to determine the pixel area of each grid. For example, if the reconstructed sliding interface is an area of 100 meters × 100 meters, it can be divided into 100 rows × 100 columns, for a total of 10,000 grids, and the area of each grid is 1 square meter, so the total area of all grids is 10,000 square meters.

[0042] This application considers the case where the landslide accumulation body covers the shear outlet. Reconstructing the landslide shear outlet marks the starting point where the landslide material begins to break away from its original position and move downward along the slope, that is, the reconstructed original toe-of-slope topography. And the elevation data of the reconstructed original toe-of-slope topography is crucial for reconstructing the sliding interface. Therefore, after reconstructing the original toe-of-slope topography using the spatial spherical interpolation method in this application, the Kriging interpolation method based on gradient descent is used to reconstruct the sliding interface, which can significantly improve the estimation accuracy of the landslide volume and provide important data decision support for the emergency rescue and engineering construction of landslide disasters.

[0043] Example 2: Taking the estimation of a certain landslide volume as an example, the specific step process is as follows.

[0044] S1: Obtain the three-dimensional laser ground point cloud data and UAV aerial image data of the landslide and its surrounding areas, as shown in Figures 2(a) and 2(b). Among them, the landslide area is the landslide accumulation area. The surrounding area of the landslide is the landslide wall area and the area required for toe-of-slope reconstruction.

[0045] S2: According to the UAV aerial images, identify and delineate the boundary ranges of the landslide accumulation area, the landslide wall area, and the area required for toe-of-slope reconstruction, as shown in Figures 3(a) and 3(b).

[0046] S3: According to the three-dimensional laser ground point cloud data within the area required for toe-of-slope reconstruction, reconstruct the original toe-of-slope topographic boundary before the landslide occurred.

[0047] Among them, the spatial spherical interpolation method used for reconstruction is: .

[0048] Among them, ( x 1 , y 1 , z 1 ), ( x 2 , y 2 , z 2 )... ( x 20 , y 20 , z 20are the values of the 20 three-dimensional laser ground points selected within the area required for toe reconstruction on the X-axis, Y-axis, and Z-axis in the 3-degree 34-zone spatial coordinate system; are the coordinate values of the center of the sphere used in the spatial sphere interpolation method in the spatial coordinate system. Here, solve respectively = 511965, b = 3276733, c = 1277; r is the radius of the sphere. Among them, it can be solved that r = 21697.

[0049] S4: Combine the fitting data of the original toe before the landslide occurred and the three-dimensional laser point cloud data on the left and right boundaries of the landslide accumulation area and within the landslide wall area to reconstruct the sliding interface. Among them, the fitting data is the elevation data, and the elevation value is the fitting value. The fitting data includes multiple fitting values, and the elevation data includes multiple elevation values.

[0050] Among them, the Kriging interpolation method based on gradient descent used for reconstructing the sliding interface is: .

[0051] Among them, is the elevation value of any point o on the Z-axis in the spatial rectangular coordinate system of the reconstructed sliding interface; are the points selected within the left and right boundaries of the landslide accumulation area, the landslide wall area, and the reconstructed original toe on the Z-axis in the spatial rectangular coordinate system; n is the number of points selected within the left and right boundaries of the landslide accumulation area, the landslide wall area, and the reconstructed original toe, n can be 30; is the weight coefficient, specifically as follows.

[0052] .

[0053] .

[0054] .

[0055] Among them, is the semi-variance of the elevation values of any point and point j within the left and right boundaries of the landslide accumulation area, the landslide wall area, and the reconstructed original toe; is the semi-variance of the elevation values corresponding to point and point o ; r oo is the semi-variance of the elevation value of point o itself; λj is the weight coefficient; For point and Point j The plane distance; w is the slope; b is the intercept, here w =1.2654, b =287.36; J is the error function, which needs to be iterated continuously to J Take the minimum value, specifically: . is the point after iteration The weight coefficient of is the point (before update) The weight coefficient of α is the learning rate, usually set to 0.001.

[0056] Among them, the error function J The minimum value can be 1796.62, corresponding to the updated weight coefficient , exemplarily, as shown in Table 1.

[0057] Table 1 Updated weight coefficient table

[0058] At this point, any point selected on the reconstructed sliding interface can be obtained o Fitting value on the Z axis of the spatial rectangular coordinate system .

[0059] S5: The landslide volume is obtained by integrating the difference between the reconstructed sliding interface elevation data and the surface elevation data of the accumulation area. The specific expression of the landslide volume is: .

[0060] in, For the landslide hazard area, illustratively, the landslide hazard area is divided into 87,500 grids; Z o It is the elevation value of any point selected in the landslide hazard area on the Z axis of the spatial coordinate system, which is directly read from the digital elevation model; is the pixel area, preferably a 2m×2m grid; is the volume of the landslide, and the calculated result is 875810m 3 .

[0061] Based on the spatial spherical interpolation method to reconstruct the original topography at the foot of the slope, the application uses the gradient descent Kriging interpolation method to reconstruct the landslide sliding interface, which can improve the accuracy of sliding interface fitting and improve the accuracy of landslide volume estimation.

[0062] In an exemplary embodiment, a computer device is further provided, which includes a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the above method is implemented.

[0063] In an exemplary embodiment, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the above method is implemented.

[0064] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0065] Those of ordinary skill in the art can understand that all or part of the processes in implementing the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memories can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0066] In each of the embodiments provided in this application, the database involved may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., and is not limited thereto. In each of the embodiments provided in this application, the processor may be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., and is not limited thereto.

[0067] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0068] Specific examples are used in this article to elaborate on the principles and implementation manners of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A landslide volume estimation method based on post-disaster remote sensing data, characterized in that: include: Acquire post-disaster remote sensing data, wherein the post-disaster remote sensing data includes three-dimensional laser ground point cloud data and drone aerial image data of the landslide disaster area; the landslide disaster area includes the landslide accumulation area, the landslide wall area, and the area required for slope foot reconstruction; According to the drone aerial image data, identify and delineate the boundary ranges of the left and right sides of the landslide accumulation area, the boundary range of the landslide wall area, and the boundary range of the area required for slope foot reconstruction; Reconstructing the original slope foot terrain boundary before the landslide occurs based on the three-dimensional laser ground point cloud data in the area required for slope foot reconstruction; Based on the reconstructed original slope foot terrain boundary before the landslide occurred, the elevation data of the original slope foot before the landslide occurred is determined; the elevation data is determined based on the value of the three-dimensional laser ground point cloud data on the Z axis of the spatial rectangular coordinate system; the spatial rectangular coordinate system is established with the central meridian and the equator as the center of the circle, the projection of the equator as the X axis, and the central meridian as the Y axis; Reconstructing the sliding interface based on the elevation data of the original slope foot before the landslide occurred, and the three-dimensional laser ground point cloud data within the boundary range of the left and right sides of the landslide accumulation area and the boundary range of the landslide wall area; The volume of the landslide is determined based on the elevation data of the sliding interface and the elevation data of the landslide accumulation area.

2. The landslide volume estimation method based on post-disaster remote sensing data according to claim 1 is characterized in that: Reconstruct the original slope foot terrain boundary before the landslide occurs based on the three-dimensional laser ground point cloud data in the area required for slope foot reconstruction, specifically including: Based on the three-dimensional laser ground point cloud data in the area required for the slope foot reconstruction, the original slope foot terrain boundary before the landslide occurred is reconstructed using a spatial spherical interpolation method; The calculation formula of the spatial sphere interpolation method is: ； in,( , , ) is the first The values ​​of the three-dimensional laser ground point cloud data on the X-axis, Y-axis, and Z-axis in the spatial rectangular coordinate system; =1,2,..., ; is the total number of 3D laser ground point cloud data; is the coordinate value of the center of the sphere in the spatial rectangular coordinate system used by the spatial spherical interpolation method; is the radius of the sphere.

3. The landslide volume estimation method based on post-disaster remote sensing data according to claim 1 is characterized in that: Based on the elevation data of the original slope foot before the landslide occurred, and the three-dimensional laser ground point cloud data within the boundary range of the left and right sides of the landslide accumulation area and the boundary range of the landslide wall area, the sliding interface is reconstructed, specifically including: The sliding interface is reconstructed based on the gradient descent Kriging interpolation method; the calculation formula of the gradient descent Kriging interpolation method is: ； in, To reconstruct any point selected on the sliding interface o Corresponding elevation data; is a point randomly selected from the boundary range of the left and right sides of the landslide accumulation area, the boundary range of the landslide wall area, and the boundary range of the reconstructed original slope foot. Elevation data on the Z axis of the spatial rectangular coordinate system; is a point randomly selected from the boundary ranges of the left and right sides of the landslide accumulation area, the boundary range of the landslide wall area, and the boundary range of the area required for slope foot reconstruction. the number of For the selected point The weight coefficient of .

4. The landslide volume estimation method based on post-disaster remote sensing data according to claim 3 is characterized in that: Before determining the landslide volume based on the elevation data of the sliding interface and the elevation data of the landslide accumulation area, the following are also included: With the goal of minimizing the value of the error function, the elevation data corresponding to each point on the reconstructed sliding interface is updated.

5. The method for estimating landslide volume based on post-disaster remote sensing data according to claim 4, characterized in that: With the goal of minimizing the value of the error function, updating the elevation data corresponding to each point on the reconstructed sliding interface specifically includes: The goal is to minimize the value of the error function, according to ,right Update and determine the updated point The weight coefficient of ; where The updated point The weight coefficient of α is the learning rate; ; and j are two points randomly selected from the boundary ranges of the left and right sides of the landslide accumulation area, the boundary range of the landslide wall area, and the boundary range of the reconstructed original slope foot; For point and Point j The semivariance of the corresponding elevation data; For point and Point j The plane distance; is the slope; is the intercept; According to the updated weight coefficient and the calculation formula of the gradient descent Kriging interpolation method, the updated ; The updated As the final point on the reconstructed sliding interface o The corresponding elevation data.

6. The method for estimating landslide volume based on post-disaster remote sensing data according to claim 4, characterized in that: The calculation formula of the error function is: ； in, J is the error function; ; The left and right boundaries of the landslide accumulation area, the landslide wall area, and the reconstructed original slope foot inner point and Point j Semivariance of elevation data; For point and Point o The semivariance of the corresponding elevation data; r oo For point o The corresponding semivariance of the elevation data; For point j The weight coefficient of For point and Point j The plane distance; is the slope; is the intercept.

7. The method for estimating landslide volume based on post-disaster remote sensing data according to claim 1, characterized in that: The volume of the landslide is determined based on the elevation data of the sliding interface and the elevation data of the landslide accumulation area, including: Multiple points randomly selected from the reconstructed sliding interface; Using the original elevation data corresponding to each point on the landslide accumulation area as the elevation data of the landslide accumulation area; Reconstructing the elevation data corresponding to each point on the sliding interface as the elevation data of the sliding interface; The volume of the landslide is determined according to the pixel area, the original elevation data corresponding to each point on the landslide accumulation area and the elevation data corresponding to each point on the reconstructed sliding interface.

8. The landslide volume estimation method based on post-disaster remote sensing data according to claim 1 is characterized in that: The calculation formula of the landslide volume is: ； in, is the landslide volume; It is a landslide hazard area; Point on the landslide accumulation area o The corresponding original elevation data; To reconstruct the sliding boundary point o Corresponding elevation data; is the pixel area.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the landslide volume estimation method based on post-disaster remote sensing data as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for estimating the landslide volume based on post-disaster remote sensing data described in any one of claims 1 to 8 is implemented.

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