Slope rill width automatic extraction and grading method and system based on point cloud data

Abstract

The invention provides an automatic slope rill width extracting and grading method and system based on point cloud data, and relates to the technical field of soil erosion. The method comprises the following steps: firstly, acquiring three-dimensional point cloud data covering a rill region to be detected, and establishing a complete slope point cloud and a corresponding space coordinate system through control point registration, denoising and cutting; constructing a digital elevation model as a rill boundary verification reference according to the slope point cloud; in the slope surface point cloud, calculating the coordinate difference of adjacent points in the cross slope direction and the vertical direction to obtain a first error threshold value for determining point pairs in the same cross section; for the point pairs meeting the first error threshold value, calculating the coordinate difference in the down-slope direction, comparing the coordinate difference with a preset rill width threshold value, and extracting rill boundary points; and based on the extracted rill boundary points, verifying the spatial consistency with the digital elevation model, calculating the average width of each rill, and outputting a width grading result. According to the invention, errors generated during manual measurement can be greatly reduced; and the measurement precision of the width of the rill is improved.

Description

Technical Field

[0001] This invention relates to the field of soil erosion technology, and in particular to an automated method and system for extracting and classifying the width of fine gullies on slopes based on point cloud data. Background Technology

[0002] Soil erosion refers to the destruction, separation, transportation, and deposition of soil and its parent material under the influence of various natural and anthropogenic factors. Soil erosion leads to increased river sediment, reduced soil fertility, and exacerbates floods, making it one of the main causes of ecological disasters.

[0003] Studying soil erosion processes is of great significance for controlling soil erosion, improving the human living environment, and especially for the sustainable development of impoverished areas. As a basic landscape unit of mountains, hills, and fractured plateaus, slopes play a crucial role in studying the patterns of soil erosion and revealing the mechanisms of soil erosion. Slope gully erosion, a vital link in soil erosion on the Loess Plateau, is the main source of sand production on Loess Plateau slopes, accounting for over 70% of slope erosion. Slope erosion also exhibits significant differences at different stages. In the early stages of rainfall, splash erosion, sheet erosion, and patch erosion are dominant, followed by the gradual emergence and maturation of gully erosion, eventually evolving into gully erosion.

[0004] Trough erosion significantly contributes to soil erosion on slopes, and its morphology is the most direct reflection of the degree of erosion. The morphology of slope troughs is one of the important factors influencing slope erosion and sediment yield. Generally, troughs formed by rainwater erosion do not exceed 30 cm in depth and 50 cm in width. Previous studies mainly used rulers to measure the cross-sectional characteristics of troughs. However, due to the irregularity of trough cross-sections, manual measurement often introduces significant human error, potentially distorting the results. Furthermore, the unevenness of the overall trough morphology makes it difficult to define representative measurement points, hindering the scientific measurement of trough width. Summary of the Invention

[0005] To overcome the shortcomings of the prior art, the purpose of this invention is to provide an automated extraction and grading method and system for slope ditch width based on point cloud data, which significantly reduces the errors caused by manual measurement and improves the measurement accuracy of ditch width.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] An automated method for extracting and classifying the width of fine ditches on slopes based on point cloud data includes:

[0008] Acquire three-dimensional point cloud data covering the area of ​​the ditch to be measured, and establish a complete slope point cloud and corresponding spatial coordinate system through control point registration, noise reduction and cropping;

[0009] A digital elevation model is constructed based on the slope point cloud and used as a benchmark for verifying the ditch boundary.

[0010] In the slope point cloud, the coordinate difference between adjacent points in the transverse and vertical directions is calculated to obtain a first error threshold, which is used to determine point pairs that are in the same cross section.

[0011] For point pairs that meet the first error threshold, calculate the slope-direction coordinate difference and compare it with the preset ditch width threshold to extract the ditch boundary points;

[0012] Based on the extracted ditch boundary points, the spatial consistency with the digital elevation model is verified, the average width of each ditch is calculated, and the width classification results are output.

[0013] Preferably, three-dimensional point cloud data covering the area of ​​the ditch to be measured is acquired, and a complete slope point cloud and corresponding spatial coordinate system are established through control point registration, noise reduction, and cropping, including:

[0014] Determine the slope range used to completely cover all the ditches to be tested, so as to ensure that each of the ditches to be tested is within the slope range;

[0015] Several ground control points are set up within the slope area and the three-dimensional coordinates of the ground control points are measured, which are used as a common reference for point clouds from various viewpoints.

[0016] Using a 3D laser scanner or UAV aerial surveying equipment, perform multi-view scanning of the slope area from different directions to collect raw point cloud data;

[0017] The original point cloud data is imported into point cloud processing software for coloring and noise reduction preprocessing, and point cloud registration and stitching are performed based on the ground control points to obtain a complete slope point cloud.

[0018] A spatial coordinate system for point cloud data is established with the transverse slope direction as the x-axis, the longitudinal slope direction as the y-axis, and the vertical direction as the z-axis.

[0019] Based on the spatial coordinate system, in the complete slope point cloud, the boundary of the area to be measured is delineated according to the area where the gully to be measured is located, and redundant point cloud data exceeding the area to be measured is trimmed to obtain the processed complete slope point cloud.

[0020] Preferably, constructing a digital elevation model based on the slope point cloud further includes:

[0021] Export the processed complete slope point cloud in ".xyz" and ".las" formats respectively;

[0022] The point cloud data in the “.las” format is converted to LAS, TIN and raster in ArcGIS to generate the digital elevation model of the slope gully.

[0023] Preferably, in the slope point cloud, the coordinate differences between adjacent points in the transverse and vertical directions are calculated to obtain a first error threshold, which is used to determine point pairs located in the same cross section, including:

[0024] In the slope point cloud, a sample point zone with uniform and continuous point spacing is selected and denoted as the sample point area.

[0025] For each pair of adjacent points within the sample area, calculate the absolute difference between the coordinates in the transverse direction and the coordinates in the vertical direction.

[0026] The absolute differences are averaged to obtain a first error threshold, which is used to determine point pairs that are in the same cross section.

[0027] Preferably, for point pairs that meet the first error threshold, the slope-direction coordinate difference is calculated and compared with a preset ditch width threshold to extract the ditch boundary points.

[0028] Retrieve all pairs of points in the slope point cloud that simultaneously satisfy |ΔY|≤ε and |ΔZ|≤ε to form a candidate point pair set; where ΔY and ΔZ are the coordinate differences of two points in the transverse and vertical directions, respectively, and ε is the first error threshold obtained from the statistics of the sample area;

[0029] For each candidate point pair, calculate its downslope coordinate difference ΔX; where ΔX = |Xi − Xj|, and Xi and Xj are the coordinate values ​​of two points located in the same cross section in the X direction.

[0030] Compare ΔX with the groove width threshold D1. When ΔX≥D1, mark the two points in the point pair as groove boundary points to obtain the groove boundary point set.

[0031] Preferably, based on the extracted ditch boundary points, the spatial consistency with the digital elevation model is verified, the average width of each ditch is calculated, and the width classification results are output, including:

[0032] Under the premise of being consistent with the spatial coordinate system, the ditch boundary point cloud of the ditch boundary point set is loaded into the same data environment as the digital elevation model and the coordinates are aligned.

[0033] Read the elevation value of each boundary point in the set of fine groove boundary points at the corresponding plane coordinates in the digital elevation model, calculate the vertical distance from the boundary point to the surface of the digital elevation model, and if the vertical distance is less than the preset spatial consistency threshold, the boundary point is confirmed to be valid; otherwise, it is discarded to obtain the verified boundary points.

[0034] Based on connectivity, the verified boundary points are divided into multiple independent fine groove edge curves;

[0035] For each ditch edge curve, symmetrical boundary points are matched in pairs along the cross-sectional direction, and the coordinate difference along the slope is calculated to form a width data sequence.

[0036] The average width of the groove is obtained by applying a weighted average or truncated average algorithm to the width data sequence.

[0037] The average width is compared with a preset width threshold set, and the corresponding groove width classification result is output.

[0038] An automated system for extracting and grading the width of fine ditches on slopes based on point cloud data includes:

[0039] The point cloud acquisition and modeling unit is used to acquire three-dimensional point cloud data covering the area of ​​the ditch to be measured, and to establish a complete slope point cloud and corresponding spatial coordinate system through control point registration, noise reduction and clipping.

[0040] The elevation model generation unit is used to construct a digital elevation model based on the slope point cloud, which serves as a verification benchmark for the ditch boundary.

[0041] A cross-section recognition threshold calculation unit is used to calculate the coordinate difference between adjacent points in the transverse and vertical directions in the slope point cloud to obtain a first error threshold, which is used to determine point pairs in the same cross section.

[0042] The boundary extraction unit is used to calculate the slope-direction coordinate difference for point pairs that meet the first error threshold, and compare it with the preset ditch width threshold to extract the ditch boundary points.

[0043] The width calculation and grading unit is used to verify the spatial consistency with the digital elevation model based on the extracted ditch boundary points, calculate the average width of each ditch, and output the width grading results.

[0044] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0045] This invention integrates the acquired point cloud data into a computer program for extraction, allowing the computer to automatically extract slope width information, significantly reducing errors caused by manual measurement; it also improves the measurement accuracy of ditch width, solving the problem of difficulty in obtaining and inaccurate measurement of ditch width due to the irregularity of ditch cross-section in existing slope ditch research. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 A flowchart of the method provided in an embodiment of the present invention;

[0048] Figure 2 Laser scan image of the soil trench provided in an embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram of point cloud data provided in an embodiment of the present invention;

[0050] Figure 4 This is a schematic diagram of the system structure provided in an embodiment of the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] The purpose of this invention is to provide an automated extraction and grading method and system for slope ditch width based on point cloud data, which significantly reduces errors caused by manual measurement and improves the measurement accuracy of ditch width.

[0053] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] Figure 1 The method flowchart provided in the embodiments of the present invention is as follows: Figure 1 As shown, this invention provides an automated method for extracting and grading the width of fine gullies on slopes based on point cloud data, including:

[0055] Step 100: Acquire 3D point cloud data covering the area of ​​the ditch to be measured, and establish a complete slope point cloud and corresponding spatial coordinate system through control point registration, noise reduction and cropping;

[0056] Step 200: Construct a digital elevation model based on the slope point cloud as a benchmark for verifying the ditch boundary;

[0057] Step 300: In the slope point cloud, calculate the coordinate difference between adjacent points in the transverse and vertical directions to obtain the first error threshold, which is used to determine point pairs in the same cross section;

[0058] Step 400: For point pairs that meet the first error threshold, calculate the slope-direction coordinate difference and compare it with the preset ditch width threshold to extract the ditch boundary points;

[0059] Step 500: Based on the extracted ditch boundary points, verify the spatial consistency with the digital elevation model, calculate the average width of each ditch, and output the width classification results.

[0060] The technical solution of this invention is as follows:

[0061] 1) Point cloud data acquisition

[0062] 1.1) Slope definition: The selected slope should completely cover the area of ​​the ditch to be measured, while ensuring the integrity of each ditch within the area to be measured.

[0063] 1.2) Determination of ground control points: Based on the slope selected in 1.1, a certain number of ground control points are set on the slope.

[0064] 1.3) Use scanning equipment such as 3D laser scanners / UAVs to scan the slope. In order to make the subsequent point cloud data modeling accurate and without blind spots, multiple scans should be performed from different angles during the scanning process to obtain point cloud data of the same slope from multiple directions.

[0065] 1.5) Import the acquired point cloud data into the point cloud data processing software for preprocessing such as coloring and noise reduction. Then, stitch together the point cloud data from different directions on the same slope based on the ground control points, and obtain complete slope point cloud data after correction.

[0066] As an optional implementation, this embodiment provides a noise reduction method in the preprocessing stage, the specific steps of which are as follows:

[0067] 1.5.1) In this embodiment, after importing the acquired point cloud data into the processing software, the coordinates and color channels are first read and basic coloring is performed. Then, a neighborhood set with a fixed radius or a fixed number is established for each point, and the color difference is robustly calibrated using the median and median absolute deviation of the neighborhood to obtain the dimensionless color difference intensity, which serves as the basis for subsequent noise identification and weight calculation. The formula is as follows:

[0068] in, For point Color vector; For point The color mean of the neighborhood; A robust scale for neighborhood color differences (converted using median absolute deviation); The intensity of color difference.

[0069] 1.5.2) In this embodiment, for each neighborhood pair A "joint robust Z-score" is constructed and mapped to similarity weights using a radial basis function kernel. These weights do not require manual hyperparameter adjustments; they are self-calibrated by neighborhood statistics and used for subsequent local fitting and noise suppression. The formula is as follows:

[0070] in, Let these be the coordinates of the point; for the median; A robust scale for spatial distance; for the median; Same as above; For joint Z-scores; This represents the similarity weight (0~1, the larger the value, the more similar the two people are).

[0071] 1.5.3) After obtaining the similarity weights, this embodiment performs weighted local plane fitting on the neighborhood of each point and applies soft thresholding to the normal residual of that point: small residuals are retained, and excessively large residuals are shrunk according to the threshold, thereby suppressing high-frequency noise while preserving the geometric structure and generating denoised point locations, as shown in the following formula:

[0072] in, The normal and intercept of the weighted plane; For point Planar residuals; The soft threshold is calculated by converting the median absolute deviation based on the neighborhood residuals according to a fixed ratio. The residual after shrinkage; These are the points after noise reduction.

[0073] 1.5.4) To avoid over-smoothing geometrical abrupt changes such as gully edges, this embodiment constructs confidence levels based on the residual energy after denoising and the color dispersion of the neighborhood. Only high-confidence points are denoised, while low-confidence points remain in their original positions to protect the edges. Subsequently, using ground control points as constraints, the point clouds from different orientations are stitched together into a complete slope surface using a conventional registration strategy, as shown in the formula:

[0074] in, For point The confidence level is (0~1); the numerator is the weighted sum of local residual energies after contraction, and the denominator is the sum of similarity weights; These are the output points ultimately used for registration and splicing.

[0075] 1.6) Establish a spatial coordinate system for the point cloud data with the transverse slope as the x-axis, the longitudinal slope as the y-axis, and the vertical direction as the z-axis, and trim away any excess parts outside the area to be measured.

[0076] 1.7) Export and store the processed point cloud data in ".xyz" format. Simultaneously, export point cloud data in ".las" format and import the generated data into ArcGIS to create a LAS dataset. Then, using the method of converting LAS to TIN and then to raster data, generate a DEM of the slope gullies as a basis for comparison with the point cloud data.

[0077] 2) Automated extraction of fine groove width

[0078] 2.1) The calculation of the ditch width is mainly based on the difference in the x-coordinate between two adjacent point cloud data points along the x-axis. To ensure that any two adjacent point cloud data points in all slope point cloud data lie in the same plane, the difference in distance between these two points in the y and z directions needs to be calculated before calculating the ditch width. The specific method is as follows: export all slope point cloud data in xyz format, select a relatively neat area in the point cloud data, select a certain number of adjacent sample points in this area, calculate the difference in distance between adjacent points in the y and z directions, and calculate the average. This value is used as the allowable error value in the y and z directions between adjacent points.

[0079] 2.2) Similarly, according to 2.1, select a relatively neat region in the point cloud data, calculate the difference between two adjacent point clouds in the x-axis direction, and calculate the mean. Use this value as the threshold D1 for the groove width.

[0080] Import the saved point cloud data in ".xyz" format into the Python software. Based on the error values ​​calculated in section 2.1, ensure that the y-axis and z-axis coordinates are within the allowable error range, and calculate the distance D between all adjacent pairs of point clouds in the x-direction.

[0081] 2.3) Based on the calculation in step 2.2, the difference in the x-axis direction between all adjacent points in the point cloud data of the slope is obtained. All points / values ​​with D greater than D1 are selected. This point / value is the range of the ditch edge line in the x-direction of the slope / the width of the ditch.

[0082] 2.4) Using the Python Open3D function, regenerate point cloud data from these points. This point cloud data represents the contours of the gullies and is compared and verified with the previously prepared DEM data. Simultaneously, obtain the average width of each gully on the slope using the trimmed mean method. As an example, the trimmed mean is calculated by removing a certain proportion of the minimum and maximum values ​​from the dataset, thus reducing the impact of extreme values.

[0083] 2.5) To more accurately classify the slope erosion process, referring to the gully classification method used by predecessors, the gullies on the slope can be classified according to their width. This classification is used for the subsequent establishment of slope erosion models and the prediction of erosion processes and trends.

[0084] 2.6) Using the Open3D function in Python, point cloud data was regenerated from these points. This point cloud data represents the outline of the gullies and was compared and verified with the previously prepared DEM data. Simultaneously, the average width of each gully in the slope was obtained using the averaging method.

[0085] 2.7) To more accurately classify slope erosion processes, referring to previous gully classification methods, slope gullies can be classified according to their width. (Developing an algorithm to automatically categorize gullies into the corresponding levels may involve setting width thresholds.)

[0086] As an optional implementation method, such as Figure 2 and Figure 3 As shown, the soil sample used in this embodiment was taken from the topsoil layer at a depth of 0-20cm in a sample plot in a certain county. The soil particles were mainly silt, accounting for about 53%, and the soil bulk density was 1.2g / cm³. 3 After the soil samples were allowed to air dry naturally, impurities such as dead branches, fallen leaves, and stones were removed using a 10mm diameter sieve. A variable slope test trench measuring 5m × 2m × 0.6m was used, with 5mm diameter perforations drilled in the steel plate at the bottom to allow for free water seepage. When filling the trench, a layer of coarse sand approximately 10cm thick was first laid at the bottom to ensure permeability. Layered filling and compaction were used, with each layer approximately 10cm thick. Simultaneously, soil samples were taken from different locations using a ring cutter to determine the bulk density, ensuring it was comparable to the topsoil bulk density under natural conditions. During the filling process, the edges were compacted as much as possible to avoid gaps between the soil and the walls.

[0087] Based on the erosive rainfall standard of the Loess Plateau and combined with measured rainfall data from a certain county, the standard for short-duration, high-intensity erosive rainfall on the Loess Plateau was found to be 10.5~234.8 mm / h. Based on the reach of the nozzles and pressure pipes of the indoor simulated rainfall device, the designed rainfall intensities were: 1 mm / min, 1.33 mm / min, 1.67 mm / min, and 2.0 mm / min. According to the spatial characteristics of the Loess Plateau's outdoor slopes, the designed slope angles were 10°, 15°, 20°, and 25°. To ensure that the initial soil moisture content of each test slope reached 30%, a light rain with a non-erosive intensity of 30 mm / h was pre-rained the day before the experiment until runoff began on the slope, and then the slope was left to stand for 24 hours in preparation for the experiment.

[0088] Each rainfall test lasted one hour. After the rainfall, the FAROFocus3D 3D laser scanning equipment was used to extract slope point cloud data. The 3D laser scanner achieved a measured accuracy of 1.4mm at 50m, with a scanning speed of 5000 points / s. Scanning a standard slope took approximately 3 minutes. To ensure accurate and comprehensive slope modeling, six positioning spheres were placed at fixed locations on the slope. During the scanning process, the slope was scanned from three different angles (left, center, and right), obtaining three slope point cloud images per scan. In image processing, the images were stitched together using the six positioning spheres as control points, and after correction, complete slope point cloud data was obtained.

[0089] The acquired point cloud data was imported into SCENE software for preprocessing such as coloring and noise reduction. Then, point cloud data from three different locations on the same slope at the same time were stitched together based on the positioning points, establishing a unified spatial coordinate system with the width of the trench as the x-axis, the length as the y-axis, and the vertical direction of the trench as the z-axis. The data was then cropped according to the slope trench outline and exported as point cloud data in ".xyz" format. Simultaneously, point cloud data in ".las" format was also exported and stored. The generated data was imported into ArcGIS to create a LAS dataset. A DEM of the slope's fine trenches was generated using the LAS to TIN conversion and then to raster data conversion method, serving as a reference for trench width.

[0090] Since the calculation of the ditch width is mainly based on the difference in the x-coordinate between two adjacent point cloud data points along the x-axis, this embodiment needs to determine that these two points are in the same plane in both the y and z directions, or that their error values ​​are within an acceptable range. Specifically, export all xyz data, visually observe a relatively neat area in the point cloud data, select 10-20 adjacent sample points in this area, calculate the difference in the y and z distances between adjacent points, and calculate the average. This value is taken as the acceptable error value in the y and z directions between adjacent points. Import the saved ".xyz" format point cloud data into Python software. Read the point cloud data sequentially from bottom to top, and based on the calculated error value, ensure that the y-axis and z-axis coordinates are within the acceptable error range. Calculate the distance D between all adjacent point clouds in the x-direction. Filter out all points / values ​​where D is greater than 0.01; this point / value represents the range / width of the ditch edge line in the x-direction of the slope. Using Python's Open3D function, point cloud data was regenerated from these points. This point cloud data represents the contours of the gullies and was compared and verified with the previously prepared DEM data. Simultaneously, the average width of each gully on the slope was obtained using an averaging method. To more accurately delineate the slope erosion process, referring to previous gully classification methods, the gullies on the slope can be classified according to their width. Based on the classification criteria, a weighted average algorithm was used to calculate the average gully width on the slope.

[0091] Specifically, in this embodiment, cross-sections are laid out along the axis of each groove within a predetermined measurement range at fixed sampling intervals (e.g., 0.10 meters or 0.20 meters). The distance between the left and right boundary points of each cross-section is read as the cross-sectional width value. Outliers at both ends of all cross-sectional width values ​​of the groove are removed (e.g., 5% each), and the average width of the groove is obtained by arithmetic mean. The cross-sectional samples used in the calculation must undergo data validity verification before being included in the statistics. When the effective sample size is less than the preset minimum sample size, the groove is marked as "not included in the statistics temporarily" and will be calculated again after supplementary measurements.

[0092] After obtaining the average width of each ditch, width is graded according to a pre-determined threshold system. For example: less than 0.02 meters is grade 1, 0.02–0.05 meters is grade 2, 0.05–0.10 meters is grade 3, and greater than or equal to 0.10 meters is grade 4. Then, the average ditch width of the slope is calculated using a length-weighted standard method, i.e., the sum of the products of the average width of each ditch and its length, divided by the total length of all ditches on the slope. The sample size, proportion, total length, and length-weighted average width for each grade are output simultaneously. In cases where individual ditch length data are temporarily unavailable, a simplified calculation method using equal weighted averages is used to maintain calculation completeness and comparability, and the calculation method used is noted in the results record.

[0093] As an optional implementation, in addition to the conventional methods described above, this embodiment also provides a process for classifying slope ditches and calculating the average width of slope ditches:

[0094] (1) Initial quantile anchoring for data preparation and slope condition correction:

[0095] This embodiment first calculates a representative value of the average width sequence for each gully in the slope, denoted as the average width of each gully. Then, it constructs a set of widths for the entire slope and extracts representative quantile points. To consider the systematic influence of slope on the geometric scale of the gullies, slope condition corrections are applied based on the quantile points to form an initial grading boundary, as shown in the following formula:

[0096] in, For the first Initial hierarchical boundaries; Set of widths of the entire slope At quantile (For example quantile at ) This is the slope correction factor; The average slope of the target area; This represents the total number of levels.

[0097] (2) Adaptive selection of the number of levels and error constraints:

[0098] This embodiment uses the "intra-group dispersion ratio limit" criterion to automatically determine the number of levels, ensuring that the ratio of intra-group dispersion to overall dispersion after grading does not exceed a set upper limit, thereby avoiding over- or under-grading; when the ratio meets the threshold requirement, the current level is accepted. and The announcement is as follows:

[0099] in, For the first The average width of the fine groove; For the first The mean of the first-order sample; The mean of all samples; For the first The grade indication of the fine grooves; The number of fine grooves; The upper limit of the proportion of dispersion within a group (e.g.) ).

[0100] (3) Hierarchical boundary robustness and minimum spacing constraint:

[0101] To improve the robustness of the hierarchical boundaries to outliers and avoid boundaries from being too close together, this embodiment applies neighborhood smoothing to the initial boundaries and imposes a minimum spacing constraint to obtain the final hierarchical boundaries. When smoothing causes a tendency for boundaries to cross, adjacent boundaries are sequentially backtracked and corrected according to the minimum spacing, as shown in the following formula:

[0102] and satisfy

[0103] in, For the first The final classification boundary; For smoothing coefficients (e.g.) ); This represents the minimum distance between adjacent boundaries; For the first The initial boundaries on both sides of the boundary.

[0104] (4) Definition of adaptive weights for hierarchy awareness:

[0105] After obtaining the final classification, this embodiment defines an adaptive weight for each gully consistent with its engineering significance, comprehensively considering the gully length and measurement stability, and assigning a moderately higher contribution to higher grades (more severe erosion); the weights are normalized to ensure additivity and comparability, as shown in the following formula:

[0106] in, For the first The original weights of the fine grooves; For normalized weights; Let this be the length of the ditch's connectivity on the slope. To measure stability indicators (such as spatial consistency pass rate); The level coefficient is a monotonically increasing coefficient that does not decrease with each level (e.g., 1.0 for level 1, 1.1 for level 2, 1.2 for level 3, and 1.3 for level 4).

[0107] (5) Average width of gullies on slope and statistical output of classification:

[0108] After determining the weights, this embodiment calculates the average width of the ditch on the slope using a weighted method, and outputs the weighted average of each level to serve the zoning management; when overall indicators are needed, the weighted average of all indicators is used, and when a hierarchical view is needed, the calculation is performed hierarchically and the results are archived together, as shown in the following formula:

[0109] in, The width of the ditch is the weighted average width of the slope. For the first The weighted average width of the fine grooves at each level; For the first Normalized weights of the fine grooves; For the first The average width of the fine groove; For level indication; This represents the number of fine grooves.

[0110] Corresponding to the above methods, such as Figure 4 As shown, this embodiment also provides an automated extraction and grading system for slope ditch width based on point cloud data, including:

[0111] The point cloud acquisition and modeling unit is used to acquire three-dimensional point cloud data covering the area of ​​the ditch to be measured, and to establish a complete slope point cloud and corresponding spatial coordinate system through control point registration, noise reduction and clipping.

[0112] The elevation model generation unit is used to construct a digital elevation model based on the slope point cloud, which serves as a verification benchmark for the ditch boundary.

[0113] A cross-section recognition threshold calculation unit is used to calculate the coordinate difference between adjacent points in the transverse and vertical directions in the slope point cloud to obtain a first error threshold, which is used to determine point pairs in the same cross section.

[0114] The boundary extraction unit is used to calculate the slope-direction coordinate difference for point pairs that meet the first error threshold, and compare it with the preset ditch width threshold to extract the ditch boundary points.

[0115] The width calculation and grading unit is used to verify the spatial consistency with the digital elevation model based on the extracted ditch boundary points, calculate the average width of each ditch, and output the width grading results.

[0116] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0117] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for automatically extracting and classifying the width of fine ditches on slopes based on point cloud data, characterized in that, include: Acquire three-dimensional point cloud data covering the area of ​​the ditch to be measured, and establish a complete slope point cloud and corresponding spatial coordinate system through control point registration, noise reduction and cropping; A digital elevation model is constructed based on the slope point cloud and used as a benchmark for verifying the ditch boundary. In the slope point cloud, the coordinate difference between adjacent points in the transverse and vertical directions is calculated to obtain a first error threshold, which is used to determine point pairs that are in the same cross section. For point pairs that meet the first error threshold, calculate the slope-direction coordinate difference and compare it with the preset ditch width threshold to extract the ditch boundary points; Based on the extracted ditch boundary points, the spatial consistency with the digital elevation model is verified, the average width of each ditch is calculated, and the width classification results are output.

2. The automated extraction and grading method for slope ditch width based on point cloud data according to claim 1, characterized in that, Acquire 3D point cloud data covering the area of ​​the gully to be measured, and establish a complete slope point cloud and its corresponding spatial coordinate system through control point registration, noise reduction, and cropping, including: Determine the slope range used to completely cover all the ditches to be tested, so as to ensure that each of the ditches to be tested is within the slope range; Several ground control points are set up within the slope area and the three-dimensional coordinates of the ground control points are measured, which are used as a common reference for point clouds from various viewpoints. Using a 3D laser scanner or UAV aerial surveying equipment, perform multi-view scanning of the slope area from different directions to collect raw point cloud data; The original point cloud data is imported into point cloud processing software for coloring and noise reduction preprocessing, and point cloud registration and stitching are performed based on the ground control points to obtain a complete slope point cloud. A spatial coordinate system for point cloud data is established with the transverse slope direction as the x-axis, the longitudinal slope direction as the y-axis, and the vertical direction as the z-axis. Based on the spatial coordinate system, in the complete slope point cloud, the boundary of the area to be measured is delineated according to the area where the gully to be measured is located, and redundant point cloud data exceeding the area to be measured is trimmed to obtain the processed complete slope point cloud.

3. The automated extraction and grading method for slope ditch width based on point cloud data according to claim 2, characterized in that, Constructing a digital elevation model based on the slope point cloud also includes: Export the processed complete slope point cloud in ".xyz" and ".las" formats respectively; The point cloud data in ".las" format is converted to LAS, TIN and raster in ArcGIS to generate the digital elevation model of the slope gully.

4. The automated extraction and grading method for slope ditch width based on point cloud data according to claim 1, characterized in that, In the slope point cloud, the coordinate differences between adjacent points in the transverse and vertical directions are calculated to obtain a first error threshold, which is used to determine point pairs located on the same cross section, including: In the slope point cloud, a sample point zone with uniform and continuous point spacing is selected and denoted as the sample point area. For each pair of adjacent points within the sample area, calculate the absolute difference between the coordinates in the transverse direction and the coordinates in the vertical direction. The absolute differences are averaged to obtain a first error threshold, which is used to determine point pairs that are in the same cross section.

5. The automated extraction and grading method for slope ditch width based on point cloud data according to claim 1, characterized in that, For point pairs that meet the first error threshold, calculate the slope-direction coordinate difference and compare it with the preset ditch width threshold to extract the ditch boundary points. Retrieve all pairs of points in the slope point cloud that simultaneously satisfy |ΔY|≤ε and |ΔZ|≤ε to form a candidate point pair set; where ΔY and ΔZ are the coordinate differences of two points in the transverse and vertical directions, respectively, and ε is the first error threshold obtained from the statistics of the sample area; For each candidate point pair, calculate its downslope coordinate difference ΔX; where ΔX = |Xi − Xj|, and Xi and Xj are the coordinate values ​​of two points located in the same cross section in the X direction. Compare ΔX with the groove width threshold D1. When ΔX≥D1, mark the two points in the point pair as groove boundary points to obtain the groove boundary point set.

6. The automated extraction and grading method for slope ditch width based on point cloud data according to claim 1, characterized in that, Based on the extracted ditch boundary points, the spatial consistency with the digital elevation model is verified, the average width of each ditch is calculated, and the width classification results are output, including: Under the premise of being consistent with the spatial coordinate system, the ditch boundary point cloud of the ditch boundary point set is loaded into the same data environment as the digital elevation model and the coordinates are aligned. Read the elevation value of each boundary point in the set of fine groove boundary points at the corresponding plane coordinates in the digital elevation model, calculate the vertical distance from the boundary point to the surface of the digital elevation model, and if the vertical distance is less than the preset spatial consistency threshold, the boundary point is confirmed to be valid; otherwise, it is discarded to obtain the verified boundary points. Based on connectivity, the verified boundary points are divided into multiple independent fine groove edge curves; For each ditch edge curve, symmetrical boundary points are matched in pairs along the cross-sectional direction, and the coordinate difference along the slope is calculated to form a width data sequence. The average width of the groove is obtained by applying a weighted average or truncated average algorithm to the width data sequence. The average width is compared with a preset width threshold set, and the corresponding groove width classification result is output.

7. An automated extraction and grading system for slope ditch width based on point cloud data, characterized in that, include: The point cloud acquisition and modeling unit is used to acquire three-dimensional point cloud data covering the area of ​​the ditch to be measured, and to establish a complete slope point cloud and corresponding spatial coordinate system through control point registration, noise reduction and clipping. The elevation model generation unit is used to construct a digital elevation model based on the slope point cloud, which serves as a verification benchmark for the ditch boundary. A cross-section recognition threshold calculation unit is used to calculate the coordinate difference between adjacent points in the transverse and vertical directions in the slope point cloud to obtain a first error threshold, which is used to determine point pairs in the same cross section. The boundary extraction unit is used to calculate the slope-direction coordinate difference for point pairs that meet the first error threshold, and compare it with the preset ditch width threshold to extract the ditch boundary points. The width calculation and grading unit is used to verify the spatial consistency with the digital elevation model based on the extracted ditch boundary points, calculate the average width of each ditch, and output the width grading results.

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