Engineering geological exploration and evaluation method based on remote sensing technology

Abstract

The invention relates to an engineering geological exploration and evaluation method based on a remote sensing technology, and relates to the technical field of remote sensing, a laser radar and an unmanned aerial vehicle are used for aerial photography to obtain ground three-dimensional point cloud data, soil samples are collected through geological exploration to obtain geological data, and the obtained point cloud data and geological data are cleaned and denoised to obtain an evaluation result. According to the processed point cloud data, generating a digital elevation model to analyze topographic features, respectively performing feature extraction on the point cloud data and the geological data, establishing a landslide risk assessment model by using a machine learning method, and performing landslide risk assessment and grade division on a target area according to a result of the model. The point cloud data and the geological data are combined, fusion of multi-source data can be achieved, richer input is provided for a landslide risk assessment model, and the precision of landslide risk assessment is improved.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing technology, and more specifically, to an engineering geological survey and evaluation method based on remote sensing technology. Background Art

[0002] With the acceleration of global urbanization and the continuous expansion of infrastructure construction, engineering geological surveys are becoming increasingly important in the fields of civil engineering, environmental protection, and resource development. Although traditional engineering geological survey methods, such as on-site surveys and drilling, can provide relatively accurate geological data, their shortcomings are also very significant: on the one hand, these methods require a long time period and high economic investment; on the other hand, traditional methods mainly rely on point-like or limited area survey data, which makes it difficult to comprehensively and systematically reflect the geological characteristics of a large range, especially in areas with complex geological conditions and irregular terrain, where surveys are more difficult.

[0003] Remote sensing technology, as an efficient, fast and economical means of geological exploration, has attracted widespread attention because of its ability to cover a large range of surface information collection. In complex geological environments, remote sensing technology has higher acquisition efficiency and data processing capabilities, and can cover a variety of geological problems such as groundwater, soil pollution, landslides, and ground fissures. Summary of the invention

[0004] The present invention aims at the technical problems existing in the prior art and provides an engineering geological survey and evaluation method based on remote sensing technology to solve the problems raised in the above background technology.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: an engineering geological survey and evaluation method based on remote sensing technology, specifically comprising the following steps:

[0006] Step 101: Use laser radar and drone aerial photography to obtain ground three-dimensional point cloud data, collect soil samples through geological survey to obtain geological data, and clean and denoise the acquired point cloud data and geological data;

[0007] Step 102: Generate a digital elevation model based on the processed point cloud data to analyze terrain features;

[0008] Step 103: extracting features from the point cloud data and the geological data respectively, and establishing a landslide risk assessment model using a machine learning method;

[0009] Step 104: Evaluate and grade the landslide risk in the target area based on the model results, and display the landslide risk assessment results.

[0010] In a preferred embodiment, in step 101, laser radar and drone aerial photography are used to obtain ground three-dimensional point cloud data, soil samples are collected through geological surveys to obtain geological data, and the acquired point cloud data and geological data are cleaned and denoised. The specific steps are as follows:

[0011] Step A1, data collection: The laser radar scans the ground through laser pulses, and measures the three-dimensional coordinates of each point based on the time difference of the return signal, and obtains the point cloud data (x i ,y i ,z i ), represents the three-dimensional coordinates of the i-th point, where the coordinates of each point include ground height information, use a drone equipped with a camera to take aerial photos, record position and attitude information, generate images and ground control points, and collect soil samples through geological surveys to record soil type, water content, and density to obtain geological data;

[0012] Step A2, data processing: spatially aligning the LiDAR point cloud with the drone aerial image, processing the geological data for outliers, and converting them into a unified data format, improving the quality of the point cloud by removing isolated points that do not belong to the ground, further comprising the following steps:

[0013] Step A201, data registration: Use the iterative closest point algorithm to optimize the matching between the lidar point cloud and the drone image, where P is the point set in the lidar point cloud and Q is the point set in the drone image. The goal is to minimize the error function By minimizing the distance between the lidar point cloud P and the drone image point cloud Q under the rotation matrix and displacement vector, the two are aligned in space, where R is the rotation matrix, τ is the translation vector, and P i is the i-th point in the lidar point cloud, Q i is the corresponding point in the drone image, ||·|| represents the Euclidean norm of the vector;

[0014] Step A202, denoising: For the registered point cloud data, use the ground extraction algorithm to remove non-ground points, and set a height threshold h thershold , when the height of the point is greater than the threshold h thershold , the point is judged as a non-ground point, and all points above the threshold are deleted to ensure that the remaining point cloud contains only ground points.

[0015] In a preferred embodiment, in step 102, a digital elevation model is generated based on the processed point cloud data to analyze the terrain features, and the specific steps are as follows:

[0016] Step B1: Generate digital elevation model: For the extracted ground point P j (x j,y j ,z j ), whose height z j Used as height values ​​in digital surface models, point cloud data is converted into grid points (x g ,y g ), for each grid point, the ground point P extracted j (x j ,y j ,z j ) Calculate the elevation as The interpolation calculation is repeated until the elevation values ​​of all grid points have been calculated to generate a complete digital elevation model, where w j is based on the ground point j and the grid point (x g ,y g ) is the weight calculated by the distance, z g is the grid point (x g ,y g ) corresponds to the height value, indicating the elevation of the digital elevation model, z j is the height value of the extracted ground point, and m is the number of ground points involved in the interpolation;

[0017] Step B2: Analyze terrain features: Based on the generated digital elevation model, calculate the slope of each point in the digital elevation model to identify landslide-prone areas. g ,y g The calculation formula of the slope at ) is By calculating the slope of each point in the entire area, the slope value S is obtained, where S g is the grid point (x g ,y g ), z is the slope of the surface, and z is the elevation value of each point in the digital elevation model. is the midpoint of the digital elevation model (x g ,y g ) is the rate of change of elevation in the X direction, is the midpoint of the digital elevation model (x g ,y g ) is the rate of change of elevation in the Y direction.

[0018] In a preferred embodiment, in step 103, feature extraction is performed on the point cloud data and the geological data respectively, and a landslide risk assessment model is established using a machine learning method. The specific steps are as follows:

[0019] Step C1, feature extraction: extract terrain features and geological features from point cloud data and geological data respectively, and combine terrain features with geological features to form a feature matrix Among them, each row represents a sample and each column represents a feature. Respectively represent the slope value, soil type, water content of the sample, soil density of the sample, N is the total number of samples, and further includes the following steps:

[0020] Step C101, extracting terrain features: extracting the slope values ​​of all grid points from the point cloud data and the generated digital elevation model to form a terrain feature array [S1, S2, ..., S N ];

[0021] Step C102, extracting geological features: extracting soil type, water content, and density features from geological data, expressed as in, is the soil type of the sample, W k is the water content of the sample, D k is the soil density of the sample, k is the sample index;

[0022] Step C2, constructing a landslide risk assessment model: Use the random forest algorithm to perform landslide risk assessment. The random forest model is trained by the feature matrix X and the corresponding landslide risk label y, where y=1 indicates that a landslide has occurred and y=0 indicates that a landslide has not occurred. The random forest constructs a decision tree by calculating the splitting criteria of each tree. The specific calculation formula is: Among them, Gini(t) is the Gini impurity of node t, R is the number of categories, and p l is the proportion of category l in the node;

[0023] By building multiple decision trees, voting is performed on each test sample, and finally the label of the sample is predicted. After the model training is completed, the trained random forest model is used to predict new samples.

[0024] In a preferred embodiment, in step 104, the landslide risk of the target area is evaluated and graded according to the results of the model, and the landslide risk evaluation results are displayed. The specific steps are as follows:

[0025] Step D1, landslide risk assessment: Perform landslide risk assessment on each sample in the target area, and use T decision trees to make predictions. For each sample, calculate the probability of each tree predicting "landslide occurrence" as Among them, P(slip|X test ) represents the sample X test The probability of landslide occurrence in the trained model, λ t is the predicted output of the t-th tree, T is the number of decision trees;

[0026] Step D2: Risk level classification: Set the risk level classification threshold to P high = 0.5 and Plow =0.3, traverse all samples in the target area and assign them to high risk, medium risk and low risk levels according to the calculated probability. When P(slip)>0.5, the sample point is divided into the high risk area; when 0.3<P(slip)≤0.5, the sample point is divided into the medium risk area; when P(slip)≤0.3, the sample point is divided into the low risk area;

[0027] Step D3, generate risk map: use GIS software to color-code each sample point according to its risk level, marking high-risk areas as red, medium-risk areas as yellow, and low-risk areas as green.

[0028] The beneficial effects of the present invention are: using laser radar and unmanned aerial vehicle to obtain ground three-dimensional point cloud data, collecting soil samples through geological survey to obtain geological data, and cleaning and denoising the acquired point cloud data and geological data, generating a digital elevation model based on the processed point cloud data to analyze terrain characteristics, extracting features from the point cloud data and geological data respectively, improving the accuracy of the assessment model, and using machine learning methods to establish a landslide risk assessment model, assessing and grading the landslide risk of the target area based on the results of the model, and displaying the landslide risk assessment results. The present invention can quickly obtain geological data in a large area through remote sensing technology, reducing the dependence on on-site work in traditional surveys, and can achieve accurate landslide risk assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0031] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise clearly and specifically defined.

[0032] In the description of the present application, the term "for example" is used to mean "used as an example, illustration or description". Any embodiment described as "for example" in the present application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid unnecessary details to obscure the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0033] Example 1

[0034] This embodiment provides Figure 1 The engineering geological survey and evaluation method based on remote sensing technology is shown, which specifically includes the following steps:

[0035] Step 101: Use laser radar and drone aerial photography to obtain ground three-dimensional point cloud data, collect soil samples through geological survey to obtain geological data, and clean and denoise the acquired point cloud data and geological data;

[0036] Step 102: Generate a digital elevation model based on the processed point cloud data to analyze terrain features;

[0037] Step 103: extracting features from the point cloud data and the geological data respectively, and establishing a landslide risk assessment model using a machine learning method;

[0038] Step 104: Evaluate and grade the landslide risk in the target area based on the model results, and display the landslide risk assessment results.

[0039] Preferably, in step 101, laser radar and drone aerial photography are used to obtain ground three-dimensional point cloud data, soil samples are collected through geological survey to obtain geological data, and the acquired point cloud data and geological data are cleaned and denoised. The specific steps are as follows:

[0040] Step A1, data collection: The laser radar scans the ground through laser pulses, and measures the three-dimensional coordinates of each point based on the time difference of the return signal, and obtains the point cloud data (x i ,y i ,z i), represents the three-dimensional coordinates of the i-th point, where the coordinates of each point include ground height information, use a drone equipped with a camera to take aerial photos, record position and attitude information, generate images and ground control points, and collect soil samples through geological surveys to record soil type, water content, and density to obtain geological data;

[0041] Step A2, data processing: spatially aligning the LiDAR point cloud with the drone aerial image, processing the geological data for outliers, and converting them into a unified data format, improving the quality of the point cloud by removing isolated points that do not belong to the ground, further comprising the following steps:

[0042] Step A201, data registration: Use the iterative closest point algorithm to optimize the matching between the lidar point cloud and the drone image, where P is the point set in the lidar point cloud and Q is the point set in the drone image. The goal is to minimize the error function By minimizing the distance between the lidar point cloud P and the drone image point cloud Q under the rotation matrix and displacement vector, the two are aligned in space, where R is the rotation matrix used to describe the rotation of the point cloud, τ is the translation vector used to describe the translation of the point cloud, and P i is the i-th point in the lidar point cloud, Q i is the corresponding point in the drone image, ||·|| represents the Euclidean norm of the vector;

[0043] Step A202, denoising: For the registered point cloud data, use the ground extraction algorithm to remove non-ground points, and set a height threshold h thershold , when the height of the point is greater than the threshold h thershold , the point is judged as a non-ground point, and all points above the threshold are deleted to ensure that the remaining point cloud contains only ground points.

[0044] Preferably, in step 102, a digital elevation model is generated based on the processed point cloud data to analyze the terrain features, and the specific steps are as follows:

[0045] Step B1: Generate digital elevation model: For the extracted ground point P j (x j ,y j ,z j ), whose height z j Used as height values ​​in digital surface models, point cloud data is converted into grid points (x g ,y g ), for each grid point, the ground point P extracted j (x j ,y j ,z j ) Calculate the elevation as The interpolation calculation is repeated until the elevation values ​​of all grid points have been calculated to generate a complete digital elevation model, where w j is based on the ground point j and the grid point (x g ,y g ) is the weight calculated by the distance, z g is the grid point (x g ,y g ) corresponds to the height value, indicating the elevation of the digital elevation model, z j is the height value of the extracted ground point, and m is the number of ground points involved in the interpolation;

[0046] Step B2: Analyze terrain features: Based on the generated digital elevation model, calculate the slope of each point in the digital elevation model to identify landslide-prone areas. g ,y g The calculation formula of the slope at ) is By calculating the slope of each point in the entire area, the slope value S is obtained, where S g is the grid point (x g ,y g ), z is the slope of the surface, and z is the elevation value of each point in the digital elevation model. is the midpoint of the digital elevation model (x g ,y g ) is the rate of change of elevation in the X direction, is the midpoint of the digital elevation model (x g ,y g ) is the rate of change of elevation in the Y direction.

[0047] Preferably, in step 103, feature extraction is performed on the point cloud data and the geological data respectively, and a landslide risk assessment model is established using a machine learning method. The specific steps are as follows:

[0048] Step C1, feature extraction: extract terrain features and geological features from point cloud data and geological data respectively, and combine terrain features with geological features to form a feature matrix Among them, each row represents a sample and each column represents a feature. Respectively represent the slope value, soil type, water content of the sample, soil density of the sample, N is the total number of samples, and further includes the following steps:

[0049] Step C101, extracting terrain features: extracting the slope values ​​of all grid points from the point cloud data and the generated digital elevation model to form a terrain feature array [S1, S2, ..., S N ];

[0050] Step C102, extracting geological features: extracting soil type, water content, and density features from geological data, expressed as in, is the soil type of the sample, W k is the water content of the sample, D k is the soil density of the sample, k is the sample index;

[0051] Step C2, constructing a landslide risk assessment model: Use the random forest algorithm to perform landslide risk assessment. The random forest model is trained by the feature matrix X and the corresponding landslide risk label y, where y=1 indicates that a landslide has occurred and y=0 indicates that a landslide has not occurred. The random forest constructs a decision tree by calculating the splitting criteria of each tree. The specific calculation formula is: Among them, Gini(t) is the Gini impurity of node t, R is the number of categories, and p l is the proportion of category l in the node;

[0052] By building multiple decision trees, voting is performed on each test sample, and finally the label of the sample is predicted. After the model training is completed, the trained random forest model is used to predict new samples.

[0053] Preferably, in step 104, the landslide risk of the target area is evaluated and graded according to the results of the model, and the landslide risk evaluation results are displayed. The specific steps are as follows:

[0054] Step D1, landslide risk assessment: Perform landslide risk assessment on each sample in the target area, and use T decision trees to make predictions. For each sample, calculate the probability of each tree predicting "landslide occurrence" as Among them, P(slip|X test ) represents the sample X test The probability of landslide occurrence in the trained model, λ t is the predicted output of the t-th tree, T is the number of decision trees;

[0055] Step D2: Risk level classification: Set the risk level classification threshold to P high = 0.5 and P low =0.3, traverse all samples in the target area and assign them to high risk, medium risk and low risk levels according to the calculated probability. When P(slip)>0.5, the sample point is divided into the high risk area; when 0.3<P(slip)≤0.5, the sample point is divided into the medium risk area; when P(slip)≤0.3, the sample point is divided into the low risk area;

[0056] Step D3, generate risk map: use GIS software to color-code each sample point according to its risk level, marking high-risk areas as red, medium-risk areas as yellow, and low-risk areas as green.

[0057] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0058] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0059] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0060] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0061] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0062] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0063] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for engineering geological survey and assessment based on remote sensing technology, characterized in that: The specific steps include: Step 101: Use laser radar and drone aerial photography to obtain ground three-dimensional point cloud data, collect soil samples through geological survey to obtain geological data, and clean and denoise the acquired point cloud data and geological data; Step 102: Generate a digital elevation model based on the processed point cloud data to analyze terrain features; Step 103: extracting features from the point cloud data and the geological data respectively, and establishing a landslide risk assessment model using a machine learning method; Step 104: Evaluate and grade the landslide risk in the target area based on the model results, and display the landslide risk assessment results.

2. The engineering geological survey and assessment method based on remote sensing technology according to claim 1, characterized in that: In step 101, laser radar and drone aerial photography are used to obtain ground three-dimensional point cloud data, soil samples are collected through geological survey to obtain geological data, and the obtained point cloud data and geological data are cleaned and denoised. The specific steps are as follows: Step A1, data collection: The laser radar scans the ground through laser pulses, and measures the three-dimensional coordinates of each point based on the time difference of the return signal, and obtains the point cloud data (x i ,y i ,z i ), represents the three-dimensional coordinates of the i-th point, where the coordinates of each point include ground height information, use a drone equipped with a camera to take aerial photos, record position and attitude information, generate images and ground control points, and collect soil samples through geological surveys to record soil type, water content, and density to obtain geological data; Step A2, data processing: spatially align the lidar point cloud with the drone aerial image, process the geological data for outliers, and convert them into a unified data format to improve the quality of the point cloud by removing isolated points that do not belong to the ground.

3. The engineering geological survey and assessment method based on remote sensing technology according to claim 2 is characterized in that: In the data processing of step A2, the laser radar point cloud and the drone aerial image are spatially aligned, the geological data is processed for outliers, and converted into a unified data format, and the quality of the point cloud is improved by removing isolated points that do not belong to the ground, which further includes the following steps: Step A201, data registration: Use the iterative closest point algorithm to optimize the matching between the lidar point cloud and the drone image, where P is the point set in the lidar point cloud and Q is the point set in the drone image. The goal is to minimize the error function By minimizing the distance between the lidar point cloud P and the drone image point cloud Q under the rotation matrix and displacement vector, the two are aligned in space, where R is the rotation matrix, τ is the translation vector, and P i is the i-th point in the lidar point cloud, Q i is the corresponding point in the drone image, ||·|| represents the Euclidean norm of the vector; Step A202, denoising: For the registered point cloud data, use the ground extraction algorithm to remove non-ground points, and set a height threshold h thershold , when the height of the point is greater than the threshold h thershold , the point is judged as a non-ground point, and all points above the threshold are deleted to ensure that the remaining point cloud contains only ground points.

4. The engineering geological survey and assessment method based on remote sensing technology according to claim 1 is characterized in that: In step 102, a digital elevation model is generated based on the processed point cloud data to analyze the terrain features. The specific steps are as follows: Step B1: Generate digital elevation model: For the extracted ground point P j (x j ,y j ,z j ), whose height z j Used as height values ​​in digital surface models, point cloud data is converted into grid points (x g ,y g ), for each grid point, the ground point P extracted j (x j ,y j ,z j ) Calculate the elevation as The interpolation calculation is repeated until the elevation values ​​of all grid points have been calculated to generate a complete digital elevation model, where w j is based on the ground point j and the grid point (x g ,y g ) is the weight calculated by the distance, z g is the grid point (x g ,y g ) corresponds to the height value, indicating the elevation of the digital elevation model, z j is the height value of the extracted ground point, and m is the number of ground points involved in the interpolation; Step B2: Analyze terrain features: Based on the generated digital elevation model, calculate the slope of each point in the digital elevation model to identify landslide-prone areas. g ,y g The calculation formula of the slope at ) is By calculating the slope of each point in the entire area, the slope value S is obtained, where S g is the grid point (x g ,y g ), z is the slope of the surface, and z is the elevation value of each point in the digital elevation model. is the midpoint of the digital elevation model (x g ,y g ) is the rate of change of elevation in the X direction, is the midpoint of the digital elevation model (x g ,y g ) is the rate of change of elevation in the Y direction.

5. The engineering geological survey and assessment method based on remote sensing technology according to claim 1 is characterized in that: In step 103, feature extraction is performed on the point cloud data and the geological data respectively, and a landslide risk assessment model is established using a machine learning method. The specific steps are as follows: Step C1, feature extraction: extract terrain features and geological features from point cloud data and geological data respectively, and combine terrain features with geological features to form a feature matrix Among them, each row represents a sample and each column represents a feature. They represent the slope value, soil type, water content of the sample, and soil density of the sample, respectively, and N is the total number of samples; Step C2, constructing a landslide risk assessment model: Use the random forest algorithm to perform landslide risk assessment. The random forest model is trained by the feature matrix X and the corresponding landslide risk label y, where y=1 indicates that a landslide has occurred and y=0 indicates that a landslide has not occurred. The random forest constructs a decision tree by calculating the splitting criteria of each tree. The specific calculation formula is: Among them, Gini(t) is the Gini impurity of node t, R is the number of categories, and p l is the proportion of category l in the node; By building multiple decision trees, voting is performed on each test sample, and finally the label of the sample is predicted. After the model training is completed, the trained random forest model is used to predict new samples.

6. The engineering geological survey and assessment method based on remote sensing technology according to claim 1 is characterized in that: In the feature extraction step C1, terrain features and geological features are extracted from the point cloud data and geological data respectively, further comprising the following steps: Step C101, extracting terrain features: extracting the slope values ​​of all grid points from the point cloud data and the generated digital elevation model to form a terrain feature array [S1, S2, ..., S N ]; Step C102, extracting geological features: extracting soil type, water content, and density features from geological data, expressed as in, is the soil type of the sample, W k is the water content of the sample, D k is the soil density of the sample and k is the sample index.

7. The engineering geological survey and assessment method based on remote sensing technology according to claim 1 is characterized in that: In step 104, the landslide risk of the target area is evaluated and graded according to the results of the model, and the landslide risk evaluation results are displayed. The specific steps are as follows: Step D1, landslide risk assessment: Perform landslide risk assessment on each sample in the target area, use T decision trees to make predictions, and for each sample, calculate the probability of each tree predicting "landslide occurrence" as Among them, P(slip|X test ) represents the sample X test The probability of landslide occurrence in the trained model, λ t is the predicted output of the t-th tree, T is the number of decision trees; Step D2: Risk level classification: Set the risk level classification threshold to P high = 0.5 and P low =0.3, traverse all samples in the target area and assign them to high risk, medium risk and low risk levels according to the calculated probability. When P(slip)>0.5, the sample point is divided into the high risk area; when 0.3<P(slip)≤0.5, the sample point is divided into the medium risk area; when P(slip)≤0.3, the sample point is divided into the low risk area; Step D3, generate risk map: use GIS software to color-code each sample point according to its risk level, marking high-risk areas as red, medium-risk areas as yellow, and low-risk areas as green.

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