Method for constructing digital elevation model

Abstract

The invention relates to the technical field of geographic information, and discloses a method for constructing a digital elevation model, which comprises the following steps: acquiring point cloud data of a target area; determining vegetation point cloud data and building point cloud data from the point cloud data of the target area; acquiring first target point cloud data of the target area according to the point cloud data, the vegetation point cloud data and the building point cloud data of the target area; and generating a digital elevation model according to the first target point cloud data. According to the method, local optimization is carried out on the point cloud data in the target area through the vegetation point cloud data and the building point cloud data, so that the quality of the point cloud data is improved. And a digital elevation model with better precision can be generated. The invention further discloses a device for constructing the digital elevation model, electronic equipment and a storage medium.

Description

Technical Field

[0001] The present application relates to the field of geographic information technology, for example, to a method for constructing a digital elevation model. Background Art

[0002] A digital elevation model (DEM) is a three-dimensional model that simulates the ground terrain in digital form using limited terrain elevation data. Digital elevation models are an important geographic information data resource and are widely used in terrain analysis, urban planning, disaster prevention and control, water resources management, agricultural management, and other aspects. The data source for generating digital elevation models is generally a topographic map. With the construction of Realistic 3D China, cities across the country are producing and updating real-life 3D models. In this process, a large amount of point cloud data is generated for generating digital elevation models. Point cloud data refers to a collection of three-dimensional spatial points in a specific area obtained by devices such as laser radar (LiDAR), RGB-D cameras, and structured light scanners. Each point cloud point contains three-dimensional coordinates (X, Y, Z), as well as information such as color, reflection intensity, and normal direction.

[0003] Currently, when generating digital elevation models using point cloud data, related technologies only perform simple denoising on the collected point cloud data, which results in low quality of the point cloud data and low accuracy of the generated digital elevation model.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to ordinary technicians in this field. Summary of the Invention

[0005] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical elements or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0006] The embodiments of the present disclosure provide a method and apparatus, an electronic device, and a storage medium for constructing a digital elevation model, so as to generate a digital elevation model with better accuracy.

[0007] In some embodiments, the method includes: obtaining point cloud data of a target area; determining vegetation point cloud data and building point cloud data from the point cloud data of the target area; obtaining first target point cloud data of the target area based on the point cloud data of the target area, the vegetation point cloud data and the building point cloud data; and generating a digital elevation model based on the first target point cloud data.

[0008] In some embodiments, the device includes: a first acquisition module, configured to acquire point cloud data of a target area; a determination module, configured to determine vegetation point cloud data and building point cloud data from the point cloud data of the target area; a second acquisition module, configured to acquire first target point cloud data of the target area based on the point cloud data of the target area, the vegetation point cloud data and the building point cloud data; and a generation module, configured to generate a digital elevation model based on the first target point cloud data.

[0009] In some embodiments, the electronic device includes a processor and a memory storing program instructions, and the processor is configured to execute the above-mentioned method for constructing a digital elevation model when running the program instructions.

[0010] In some embodiments, the storage medium stores program instructions, and the program instructions are executed by a processor to implement the above-mentioned method for constructing a digital elevation model.

[0011] The method and apparatus, electronic device, and storage medium for constructing a digital elevation model provided by the embodiments of the present disclosure can achieve the following technical effects: by acquiring vegetation point cloud data and building point cloud data within a target area, first target point cloud data of the target area is acquired based on the point cloud data, vegetation point cloud data, and building point cloud data of the target area, thereby generating a digital elevation model based on the first target point cloud data. In this way, the point cloud data within the target area is locally optimized using the vegetation point cloud data and building point cloud data, thereby improving the quality of the point cloud data. This in turn enables the generation of a more accurate digital elevation model.

[0012] The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] One or more embodiments are exemplarily described by corresponding drawings. These exemplary descriptions and drawings do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements. The drawings do not constitute a scale limitation. In addition,

[0014] Figure 1 is a schematic diagram of a method for constructing a digital elevation model provided by an embodiment of the present disclosure;

[0015] Figure 2 is a schematic diagram of a device for constructing a digital elevation model provided by an embodiment of the present disclosure;

[0016] Figure 3 It is a schematic diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0017] In order to be able to understand the features and technical content of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The accompanying drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the sake of convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

[0018] In the description and claims of the embodiments of the present disclosure, as well as in the accompanying drawings, the terms "first," "second," and the like are used to distinguish similar items and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate to describe the embodiments of the present disclosure herein. In addition, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions.

[0019] Unless otherwise stated, the term "plurality" means two or more.

[0020] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0021] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0022] The term "correspondence" may refer to an association relationship or a binding relationship. The correspondence between A and B means that there is an association relationship or a binding relationship between A and B.

[0023] The disclosed embodiments provide a method for constructing a digital elevation model, executed by an electronic device. The electronic device may include a computer or server. The electronic device acquires point cloud data within a target area and determines vegetation point cloud data and building point cloud data therein. The vegetation point cloud data and building point cloud data are deleted from the point cloud data of the target area to obtain first target point cloud data of the target area. A digital elevation model is then generated based on the first target point cloud data. In this way, the point cloud data within the target area is locally optimized using the vegetation point cloud data and the building point cloud data, thereby improving the quality of the point cloud data. This allows for the generation of a more accurate digital elevation model.

[0024] Combine Figure 1 As shown, the embodiment of the present disclosure provides a method for constructing a digital elevation model, including:

[0025] Step S101: Obtain point cloud data of a target area. The point cloud data of a target area is a set of three-dimensional points in the target area. A three-dimensional point is a point cloud point, and each point cloud point contains three-dimensional coordinates (X, Y, Z).

[0026] Step S102 : determining vegetation point cloud data and building point cloud data from the point cloud data of the target area.

[0027] Step S103 : acquiring first target point cloud data of the target area according to the point cloud data, vegetation point cloud data and building point cloud data of the target area.

[0028] Step S104: generating a digital elevation model according to the first target point cloud data.

[0029] The method for constructing a digital elevation model provided by the embodiments of the present disclosure obtains vegetation point cloud data and building point cloud data within a target area. Based on the point cloud data, vegetation point cloud data, and building point cloud data, first target point cloud data of the target area is obtained, and a digital elevation model is generated based on the first target point cloud data. In this way, the point cloud data within the target area is locally optimized using the vegetation point cloud data and building point cloud data, thereby improving the quality of the point cloud data and generating a more accurate digital elevation model.

[0030] Optionally, obtaining point cloud data of the target area includes: obtaining initial point cloud data of the target area; performing denoising on the initial point cloud data according to a preset denoising strategy to obtain candidate point cloud data; and obtaining point cloud data of the target area based on the candidate point cloud data.

[0031] In some embodiments, a collection device is used to collect three-dimensional spatial points within a target area. The three-dimensional spatial points collected by the collection device are determined as initial point cloud data. For example, the initial point cloud data of the target area is obtained using a collection device such as a laser radar (LiDAR), an RGB-D camera, or a structured light scanner.

[0032] Furthermore, the initial point cloud data is denoised according to a preset denoising strategy to obtain candidate point cloud data, including:

[0033] Step S201 : obtaining a first point cloud density and a first point cloud average distance, wherein the first point cloud density is the density of initial point cloud data in the target area, and the first point cloud average distance is the average distance between each initial point cloud data.

[0034] In step S202 , a first discrete point in the initial point cloud data is determined based on the first point cloud average distance, and the first discrete point is deleted. Step S203 is then executed on the remaining initial point cloud data.

[0035] In step S203 , the elevation value of each initial point cloud data is updated according to the first point cloud average distance, and step S204 is executed on the initial point cloud data after the elevation value is updated.

[0036] Step S204 : determining a second discrete point in the initial point cloud data according to the first point cloud density, deleting the second discrete point, and determining the remaining initial point cloud data as candidate point cloud data.

[0037] By first deleting the first discrete point in the initial point cloud data, then updating the elevation values ​​of each point in the remaining initial point cloud data, and finally deleting the second discrete point, the three-level noise filtering mechanism is used to efficiently remove noisy point cloud data.

[0038] Furthermore, obtaining the first point cloud density includes: calculating Get the first point cloud density, where ρ is the first point cloud density, q is the number of initial point cloud data, and m 2 is the area of ​​the target region.

[0039] Furthermore, the average distance of the first point cloud is obtained, including: calculating Obtain the first point cloud average distance, where d is the first point cloud average distance.

[0040] Furthermore, based on the average distance of the first point cloud, the first discrete point in the initial point cloud data is determined, and the first discrete point is deleted, including: respectively calculating the elevation mean and standard deviation of all the initial point cloud data within each first preset range, and obtaining the first discrete point within each first preset range based on the elevation mean and standard deviation corresponding to each first preset range, and deleting the first discrete point within each first preset range. The first preset range is a circle with any point in the initial point cloud data as the center and the first preset distance as the radius. In this way, each point in the initial point cloud data corresponds to a first preset range. The first preset distance is a third preset multiple of the first point cloud average distance. For example, the third preset multiple is 5, that is, the first preset distance is 5 times the first point cloud average distance.

[0041] The method of obtaining the first discrete point within each first preset range based on the elevation mean and standard deviation corresponding to each first preset range includes: for each first preset range, determining the initial point cloud data whose elevation mean is less than a first preset multiple of the standard deviation, or greater than a second preset multiple of the standard deviation, as the first discrete point within the first preset range. The first preset multiple is less than the second preset multiple. For example, the first preset multiple is -3 times, and the second preset multiple is 3 times. That is, for each first preset range, determining the initial point cloud data whose elevation mean is less than -3 times the standard deviation, or whose elevation mean is greater than 3 times the standard deviation, as the first discrete point within the first preset range.

[0042] Furthermore, based on the average distance of the first point cloud, the elevation value of the initial point cloud data is updated, including: determining the second preset range corresponding to each initial point cloud data, obtaining the median elevation value of all initial point cloud data within each second preset range, and determining the median elevation value corresponding to the second preset range as the elevation value of the corresponding initial point cloud data. The original elevation value of the initial point cloud data is deleted. The second preset range corresponding to the initial point cloud data is a rectangular range with a preset length and a preset width centered on the initial point cloud data. The preset length is the fourth preset multiple of the average distance of the first point cloud. The preset width is the fifth preset multiple of the average distance of the first point cloud. The fourth preset multiple is equal to or unequal to the fifth preset multiple. For example, if the fourth preset multiple and the fifth preset multiple are both 3, then the second preset range corresponding to the initial point cloud data is a 3d×3d rectangular range centered on the initial point cloud data.

[0043] Obtaining the median elevation value of all initial point cloud data within the second preset range includes: obtaining the elevation values ​​of all initial point cloud data within the second preset range, and sorting the obtained elevation values ​​in descending order, or in descending order. If the number of initial point cloud data within the second preset range is odd, the elevation value of the initial point cloud data in the middle of the row is determined as the median elevation value. If the number of initial point cloud data within the second preset range is even, the average of the elevation values ​​of the two initial point cloud data in the middle of the row is determined as the median elevation value.

[0044] Furthermore, determining a second discrete point in the initial point cloud data based on the first point cloud density and deleting the second discrete point includes: determining a third preset range corresponding to each piece of initial point cloud data; if the number of all initial point cloud data within the third preset range is less than a set threshold, determining the initial point cloud data corresponding to the third preset range as the second discrete point, and deleting the second discrete point. The set threshold is an integer greater than or equal to 3. The third preset range corresponding to the initial point cloud data is a circle centered on the initial point cloud data and having a radius of the second preset distance.

[0045] Among them, by calculating A second preset distance is obtained, where r is the second preset distance and ρ is the first point cloud density.

[0046] In this disclosed embodiment, a statistical outlier filter is used to remove the first discrete point from the initial point cloud data. A median filter is then used to update the elevation values ​​of each point in the remaining initial point cloud data. Finally, a radius filter is used to remove the second discrete point from the initial point cloud data. This three-stage noise filtering mechanism effectively removes noisy point clouds.

[0047] In some embodiments, after obtaining the alternative point cloud data, it also includes: obtaining a second point cloud density and a second point cloud average distance, wherein the second point cloud density is the density of the alternative point cloud data in the target area, and the second point cloud average distance is the average distance between each alternative point cloud data.

[0048] Optionally, obtaining the point cloud data of the target area based on the candidate point cloud data includes: performing density balancing processing on the candidate point cloud data to obtain the point cloud data of the target area. In this way, by performing density balancing processing on the candidate point cloud data, the obtained point cloud data of the target area can be more evenly distributed.

[0049] Furthermore, density equalization processing is performed on the candidate point cloud data, including: using the moving least squares interpolation method to perform surface fitting on the sparse area to generate interpolation points. The sparse area is a square range with a first set length as the side length, and the point cloud density within the square range is less than a first preset value. The three-dimensional space of the target area is divided into cubes with a second set length as the side length using a voxel grid downsampling method. The divided cubes are used as voxel grids, and only one elevation median point is retained in each voxel grid. The elevation median point is the elevation median value of all candidate point cloud data within the voxel grid.

[0050] In some embodiments, the first set length is a sixth preset multiple of the average distance of the second point cloud, for example, the sixth preset multiple is 10. The first preset value is a seventh preset multiple of the density of the second point cloud, for example, the seventh preset multiple is 0.1. The fitting constraint elevation error is less than or equal to a second preset value, for example, the second preset value is 0.1 m. The second set length is an eighth preset multiple of the average distance of the second point cloud, for example, the eighth preset multiple is 3.

[0051] In this way, the density equalization of the candidate point cloud data is performed by combining voxel grid downsampling with moving least squares interpolation, which solves the problem of uneven distribution of the candidate point cloud data and resulting grid distortion.

[0052] In some embodiments, after performing density equalization on the candidate point cloud data, the process further includes: verifying the point cloud density within the moving window using a preset moving window. If the standard deviation of the point cloud density within the moving window is greater than a third preset value, density equalization is performed again on the candidate point cloud data. For example, the third preset value is 30%. The preset moving window is a square moving window with a side length of the first set length.

[0053] Optionally, after density equalization processing is performed on the alternative point cloud data, the method further includes: obtaining a third point cloud density and a third point cloud average distance, wherein the third point cloud density is the density of the point cloud data in the target area, and the third point cloud average distance is the average distance between each point cloud data in the target area.

[0054] Optionally, determining vegetation point cloud data from the point cloud data of the target area includes: obtaining the slope of each point cloud data in the target area, and obtaining the remote sensing vegetation index corresponding to each point cloud data. Based on the slope of each point cloud data, the vegetation index threshold corresponding to each point cloud data is determined. When the remote sensing vegetation index of the point cloud data is greater than the corresponding vegetation index threshold, the corresponding point cloud data is determined to be vegetation point cloud data. By first obtaining the slope of the point cloud data and dynamically determining the corresponding vegetation index threshold based on the slope of each point, the remote sensing vegetation index and terrain parameters are coupled to more accurately segment the vegetation point cloud data, further optimizing the accuracy of vegetation removal.

[0055] In some embodiments, remote sensing vegetation index data is obtained through satellite or aerial remote sensing platforms. Remote sensing vegetation index data is a quantitative indicator data reflecting the coverage and growth vitality of surface vegetation.

[0056] Furthermore, obtaining the slope of each point cloud data within the target area includes: converting the point cloud data of the target area into raster data; determining a target window corresponding to each point cloud data; and performing calculations using the raster data within the target window to obtain the slope of the corresponding point cloud data. In some embodiments, the target window corresponding to the point cloud data is a square window centered on the point cloud data and having a third predetermined length as a side length. The third predetermined length is a ninth predetermined multiple of the average distance of the third point cloud. For example, the ninth predetermined multiple is 3.

[0057] Furthermore, the point cloud data of the target area is converted into raster data, including: using GIS software to generate a triangulated irregular network (TIN) from the point cloud data of the target area, and then converting the TIN into raster data. Each point in the point cloud data corresponds to a grid point, and the raster data of each grid point includes three-dimensional coordinates (x, y, z) and a remote sensing vegetation index (NDVI).

[0058] Furthermore, the grid data in the target window is used to calculate the slope of the corresponding point cloud data, including: Get the slope of the point cloud data. k is the slope of the k-th point cloud data, is the difference between the maximum and minimum elevations of the grid data in the target window corresponding to the k-th point cloud data. is the difference between the maximum x value and the minimum x value of the grid data in the target window corresponding to the k-th point cloud data, It is the difference between the maximum y value and the minimum y value of the grid data in the target window corresponding to the k-th point cloud data.

[0059] The slope of the point cloud data obtained by the above algorithm actually represents the average slope within the target window corresponding to the point cloud data. In this way, the slope of the point cloud data can be obtained more accurately.

[0060] Furthermore, determining the vegetation index threshold corresponding to each point cloud data point based on its slope includes performing a table lookup in a preset data table using the slope of each point cloud data point to find the vegetation index threshold corresponding to each point cloud data point. The preset data table stores the correspondence between slope and vegetation index threshold. In this way, dynamically determining the corresponding vegetation index threshold based on the slope of each point cloud point enables more accurate identification of vegetation point cloud data.

[0061] In some embodiments, Table 1 is an example of a preset data table. As shown in Table 1, when the slope is less than 15°, the corresponding vegetation index threshold is 0.3. When 15°≤slope≤30°, the corresponding vegetation index threshold is 0.4, and the elevation constraint is combined (point clouds within 1m above the ground are retained);

[0062] When the slope is greater than 30°, the corresponding vegetation index threshold is 0.5, and morphological opening operation is performed on the high-NDVI value area to denoise it.

[0063] Table 1

[0064] slope Vegetation index threshold Slope <15° 0.3 15°≤slope≤30° 0.4 Slope>30° 0.5

[0065] Optionally, determining building point cloud data from the point cloud data of the target area includes obtaining building footprint data. Point cloud data within the building footprint outline is determined as building point cloud data. Building footprint data refers to the projected boundary information of a building on the ground. It is geometrically represented as a polygon consisting of closed line segments or points, reflecting the planar outline of the building. Examples include rectangular, L-shaped, and irregular shapes.

[0066] Optionally, obtaining first target point cloud data for the target area based on the point cloud data, vegetation point cloud data, and building point cloud data of the target area includes: removing the vegetation point cloud data and building point cloud data from the point cloud data of the target area to obtain the first target point cloud data. In this way, the vegetation point cloud data and building point cloud data in the target area are removed, and the remaining point cloud data is the first target point cloud data. A digital elevation model is then generated based on the first target point cloud data. This can avoid interference from vegetation and buildings that could lead to misidentification of ground point clouds.

[0067] Optionally, generating a digital elevation model based on the first target point cloud data includes: obtaining road centerline vector data within a target area; determining a road coverage area based on the road centerline vector data; obtaining road edges based on the road centerline vector data and the first target point cloud data; deleting point cloud data within the road coverage area from the first target point cloud data to obtain second target point cloud data; and constructing a digital elevation model based on the second target point cloud data and the road edges.

[0068] Road centerline vector data represents the geometric center axis of the road. It is a polyline or polyline composed of continuous nodes, representing the direction and connection relationship of the road. Road centerline vector data includes node coordinates and road width. Due to the influence of street trees, roadside parking, etc., if the digital elevation model is directly constructed using the first target point cloud data, it may result in an uneven road. The embodiment of the present disclosure uses the road centerline to determine the road coverage area and further obtains the road edge line. This optimizes the elevation continuity of the road area. This enables the generation of a more accurate digital elevation model.

[0069] Furthermore, determining the road coverage area based on the road centerline vector data includes: using the road centerline as a reference, offsetting the road centerline parallel to the road centerline by a preset distance to obtain two side lines, connecting the starting points of the two side lines, and connecting the end points of the two side lines to obtain a regional surface, and determining the regional surface as the road coverage area. The preset distance is the tenth preset multiple of the road width. For example, the tenth preset multiple is 0.5 times.

[0070] Furthermore, obtaining the road edgeline based on the road centerline vector data and the first target point cloud data includes: setting interpolation nodes at a preset interval starting from the road centerline starting point; obtaining the elevation value of each interpolation node based on the first target point cloud data; linearly interpolating between adjacent interpolation nodes to generate a continuous target curve; and obtaining the road edgeline based on the target curve. The preset interval is 10 meters.

[0071] Furthermore, obtaining the elevation value of each interpolation node based on the first target point cloud data includes: determining a fourth preset range corresponding to each interpolation node, obtaining an average elevation of all first target point cloud data within the fourth preset range, and determining the average elevation as the elevation value of the corresponding interpolation node. The fourth preset range corresponding to an interpolation node is a circular area centered at the interpolation node and having a road width as its diameter.

[0072] Further, obtaining the average elevation of all first target point cloud data within the fourth preset range includes: calculating Get the average elevation. s is the average elevation of all first target point cloud data within the sth fourth preset range, hsi is the elevation value of the i-th first target point cloud data within the s-th fourth preset range, w si is the elevation weight of the i-th first target point cloud data within the s-th fourth preset range. That is, H s It is the elevation value of the sth interpolation node.

[0073] By calculation Get the elevation weight of the i-th first target point cloud data within the s-th fourth preset range, where D si It is the horizontal distance from the i-th first target point cloud data to the s-th interpolation node within the s-th fourth preset range, ∈ is the smoothing factor to prevent the denominator from being zero, and ∈ is greater than 0.

[0074] Furthermore, obtaining the road edge line based on the target curve includes: taking the target curve as a reference, offsetting the target curve parallel to both sides by a preset distance to obtain two road edge lines. The preset distance is the tenth preset multiple of the road width. For example, the tenth preset multiple is 0.5 times. A continuous target curve is generated by linear interpolation between adjacent interpolation nodes. Then, a new road edge line is generated based on the target curve. The elevation continuity of the road area is optimized. The point cloud data belonging to the road coverage area in the first target point cloud data is deleted, and the remaining first target point cloud data is determined as the second target point cloud data. In this way, a more accurate digital elevation model can be generated based on the second target point cloud data and the newly generated road edge line.

[0075] Optionally, constructing a digital elevation model based on the second target point cloud data and the roadside line includes: obtaining initial water level boundary data for a target water area within the target area and obtaining the water area type of the target water area; obtaining target water level boundary data for the target water area based on the water area type and the initial water level boundary data; updating the water area point cloud data in the second target point cloud data based on the water area type of the target water area to obtain third target point cloud data; and constructing a digital elevation model based on the third target point cloud data, the roadside line, and the target water level boundary data.

[0076] Water level boundary data (Water Level Contour Data) refers to the water surface boundary contour information of water bodies such as rivers, lakes, and oceans, which are usually expressed in the form of polygons or contour lines and are used to describe the spatial range and dynamic changes of water bodies. The water level boundary data includes the coordinates of all nodes on the water level boundary, where the node coordinates include horizontal coordinates, vertical coordinates, and elevation values. The water level boundary data also includes water level height, such as altitude or relative height. The water level boundary data also includes attributes such as timestamp and water flow direction. Because the water surface is too smooth, the processing requirements for optical images are relatively high. There may be a large number of elevation anomalies in the initial water level boundary data, making the originally smooth water surface uneven. In the embodiment of the present disclosure, the target water level boundary data is re-acquired for different types of water areas, and the water area point cloud data within the target water level boundary is updated. Thereby, the third target point cloud data is obtained. The elevation continuity of the water area is optimized. Thus, a digital elevation model with better accuracy can be generated.

[0077] In some embodiments, the water area type includes closed water areas, such as ponds, lakes, reservoirs, etc., or open water areas, such as rivers, etc. The target water areas are all water areas within the target area.

[0078] The initial waterline boundary data includes an initial waterline boundary node. The target waterline boundary data includes a target waterline boundary node. Target waterline boundary data for the target water area is obtained based on the water area type of the target water area and the initial waterline boundary data, including: obtaining a fifth preset range corresponding to each initial waterline boundary node; determining the minimum elevation value of all second target point cloud data within the fifth preset range; and determining the minimum elevation value as the new elevation value of the corresponding initial waterline boundary node. The original elevation value of the initial waterline boundary node is deleted. If the water area type of the target water area is open water, the initial waterline boundary node with the updated elevation value is determined as the target waterline boundary node of the target water area. If the water area type of the target water area is closed water, the initial waterline boundary node with the updated elevation value is determined as the candidate waterline boundary node. Then, the average elevation value of all candidate waterline boundary nodes is calculated. The elevation values ​​of the candidate waterline boundary nodes are updated to the average elevation value, and the original elevation values ​​of the candidate waterline boundary nodes are deleted. The candidate water level boundary node after the elevation value is updated is determined as the target water level boundary node of the target water area.

[0079] The fifth preset range corresponding to the waterline boundary node is a circular area centered at the waterline boundary node and having a third preset distance as a radius. The third preset distance is an eleventh preset multiple of the average distance of the third point cloud. For example, the eleventh preset multiple is 2.

[0080] In some embodiments, the water area point cloud data in the second target point cloud data is the second target point cloud data within the boundary of the target water level line. Updating the water area point cloud data in the second target point cloud data means updating the elevation of the water area point cloud data. All second target point cloud data after the elevation value of the water area point cloud data is updated is determined as the third target point cloud data.

[0081] Furthermore, based on the water area type of the target water area, the water area point cloud data in the second target point cloud data is updated, including: if the water area type of the target water area is a closed water area, the elevation value of the water area point cloud data is updated to the elevation value of the boundary node corresponding to the target water level line, and the original elevation value of the water area point cloud data is deleted.

[0082] If the target water area is open water, calculate the elevation values ​​of the target waterline boundary nodes to obtain candidate elevation values ​​for each water area point cloud data. Update the elevation value of each water area point cloud data to the corresponding candidate elevation value. Delete the original elevation value of the water area point cloud data.

[0083] Furthermore, by calculating Obtain the alternative elevation value of the water point cloud data, where H j is the alternative elevation value of the j-th water area point cloud data, h p is the elevation value of the pth target water level boundary node, w jp is the elevation weight of the pth target water level boundary node relative to the jth water area point cloud data.

[0084] By calculation Get the elevation weight of the pth target water level boundary node relative to the jth water area point cloud data. jp is the horizontal distance from the j-th water area point cloud data to the p-th target water level boundary node, ∈ is a smoothing factor to prevent the denominator from being zero, and ∈ is greater than 0.

[0085] Furthermore, a digital elevation model is constructed based on the third target point cloud data, the road edge line and the target water level line boundary data, including: generating a TIN (Triangulated Irregular Network) using GIS software based on the third target point cloud data, the road edge line and the target water level line boundary data, and converting the generated TIN into raster data to generate a digital elevation model.

[0086] The method for constructing a digital elevation model provided by the embodiment of the present disclosure realizes efficient removal of noise point cloud data in the initial point cloud data through a three-level noise filtering mechanism of statistical outlier filtering, median filtering and radius filtering. The density equalization method combining voxel grid downsampling and moving least squares interpolation solves the grid distortion problem caused by uneven distribution of point cloud data. The vegetation index-slope joint dynamic threshold algorithm is further introduced to couple the remote sensing vegetation index with terrain parameters to accurately segment the vegetation canopy point cloud data. At the same time, the road centerline constraint interpolation, building bottom surface segmentation and water level elevation correction are integrated to generate a continuous and reliable high-precision digital elevation model.

[0087] Combine Figure 2 As shown, an embodiment of the present disclosure provides an apparatus 200 for constructing a digital elevation model, comprising a first acquisition module 201, a determination module 202, a second acquisition module 203, and a generation module 204. The first acquisition module 201 is configured to acquire point cloud data of a target area. The determination module 202 is configured to determine vegetation point cloud data and building point cloud data from the point cloud data of the target area. The second acquisition module 203 is configured to acquire first target point cloud data of the target area based on the point cloud data, vegetation point cloud data, and building point cloud data of the target area. The generation module 204 is configured to generate a digital elevation model based on the first target point cloud data.

[0088] Using the apparatus for constructing a digital elevation model provided by the embodiments of the present disclosure, vegetation point cloud data and building point cloud data within a target area are obtained. Based on the point cloud data, vegetation point cloud data, and building point cloud data of the target area, first target point cloud data of the target area is obtained, and a digital elevation model is generated based on the first target point cloud data. In this way, the point cloud data within the target area is locally optimized using the vegetation point cloud data and building point cloud data, thereby improving the quality of the point cloud data. This allows for the generation of a more accurate digital elevation model.

[0089] Optionally, the first acquisition module is configured to acquire point cloud data of the target area by: acquiring initial point cloud data of the target area; performing denoising on the initial point cloud data according to a preset denoising strategy to acquire candidate point cloud data; and acquiring point cloud data of the target area based on the candidate point cloud data.

[0090] Optionally, the first acquisition module is configured to obtain point cloud data of the target area based on the candidate point cloud data in the following manner: performing density equalization processing on the candidate point cloud data to obtain point cloud data of the target area.

[0091] Optionally, the determination module is configured to determine vegetation point cloud data from the point cloud data of the target area in the following manner: obtaining the slope of each point cloud data in the target area, and obtaining the remote sensing vegetation index corresponding to each point cloud data; determining the vegetation index threshold corresponding to each point cloud data based on the slope of each point cloud data; when the remote sensing vegetation index of the point cloud data is greater than the corresponding vegetation index threshold, determining the corresponding point cloud data as vegetation point cloud data.

[0092] Optionally, the second acquisition module is configured to acquire first target point cloud data of the target area based on the point cloud data, vegetation point cloud data and building point cloud data of the target area in the following manner: deleting vegetation point cloud data and building point cloud data from the point cloud data of the target area to obtain first target point cloud data.

[0093] Optionally, the generation module is configured to generate a digital elevation model based on the first target point cloud data in the following manner: obtaining road centerline vector data within the target area; determining the road coverage area based on the road centerline vector data; obtaining the road edgeline based on the road centerline vector data and the first target point cloud data; deleting the point cloud data within the road coverage area in the first target point cloud data to obtain second target point cloud data; and constructing a digital elevation model based on the second target point cloud data and the road edgeline.

[0094] Optionally, the generation module is configured to construct a digital elevation model based on the second target point cloud data and the road sideline in the following manner: obtain the initial water level line boundary data of the target water area in the target area, and obtain the water area type of the target water area; obtain the target water level line boundary data of the target water area based on the water area type of the target water area and the initial water level line boundary data; update the water area point cloud data in the second target point cloud data based on the water area type of the target water area to obtain third target point cloud data; construct a digital elevation model based on the third target point cloud data, the road sideline and the target water level line boundary data.

[0095] Combine Figure 3 As shown, an embodiment of the present disclosure provides an electronic device 300, including a processor 304 and a memory 301 storing program instructions. Optionally, the electronic device may further include a communication interface 302 and a bus 303. The processor 304, the communication interface 302, and the memory 301 may communicate with each other via the bus 303. The communication interface 302 may be used for information transmission. The processor 304 may call the program instructions in the memory 301 to execute the method for constructing a digital elevation model according to the above embodiment.

[0096] In addition, the logic instructions in the memory 301 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0097] Memory 301, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of the present disclosure. Processor 304 executes the program instructions / modules stored in memory 301 to perform functional applications and data processing, thereby implementing the methods for constructing digital elevation models in the above-described embodiments.

[0098] The memory 301 may include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function; the data storage area may store data generated based on the use of the terminal device. Furthermore, the memory 301 may include high-speed random access memory and non-volatile memory.

[0099] An embodiment of the present disclosure provides a storage medium storing program instructions, which are executed by a processor to implement the above-mentioned method for constructing a digital elevation model.

[0100] The technical solution of the embodiments of the present disclosure may be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the present disclosure. The aforementioned storage medium may be a non-transitory storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program code, or a transient storage medium.

[0101] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible variations. Unless explicitly required, individual components and functions are optional, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to also include plural forms. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of one or more associated listings. In addition, when used in this application, the term "comprise" and its variations "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups of these. In the absence of further restrictions, an element defined by the sentence "comprising a..." does not exclude the presence of other identical elements in the process, method or device that includes the element. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments can be referenced to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can be found in the description of the method part.

[0102] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure. The technicians will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0103] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units can be merely a logical functional division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, and can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected to implement this embodiment according to actual needs. In addition, the functional units in the embodiments of the present disclosure may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0104] The flowcharts and block diagrams in the accompanying drawings show the possible implementation architectures, functions and operations of the systems, methods and computer program products according to the embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of the code, and the module, program segment or part of the code contains one or more executable instructions for implementing the specified logical functions. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or action, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A method for constructing a digital elevation model, characterized in that: include: Obtain point cloud data of the target area; Determining vegetation point cloud data and building point cloud data from the point cloud data of the target area; Acquire first target point cloud data of the target area according to the point cloud data of the target area, the vegetation point cloud data and the building point cloud data; A digital elevation model is generated based on the first target point cloud data.

2. The method according to claim 1, characterized in that Obtain point cloud data of the target area, including: Obtain initial point cloud data of the target area; De-noising the initial point cloud data according to a preset de-noising strategy to obtain candidate point cloud data; The point cloud data of the target area is obtained according to the candidate point cloud data.

3. The method according to claim 2, characterized in that Obtaining point cloud data of the target area according to the candidate point cloud data includes: Density equalization is performed on the candidate point cloud data to obtain point cloud data of the target area.

4. The method according to claim 1, wherein Determining vegetation point cloud data from the point cloud data of the target area includes: Obtaining the slope of each point cloud data in the target area, and obtaining the remote sensing vegetation index corresponding to each point cloud data; According to the slope of each point cloud data, the vegetation index threshold corresponding to each point cloud data is determined; When the remote sensing vegetation index of the point cloud data is greater than the corresponding vegetation index threshold, the corresponding point cloud data is determined to be vegetation point cloud data.

5. The method according to claim 1, wherein Acquiring first target point cloud data of the target area according to the point cloud data of the target area, the vegetation point cloud data, and the building point cloud data, including: The vegetation point cloud data and the building point cloud data are deleted from the point cloud data of the target area to obtain first target point cloud data.

6. The method according to claim 1, characterized in that Generating a digital elevation model according to the first target point cloud data includes: Obtain road centerline vector data within the target area; determining a road coverage area according to the road centerline vector data; Acquire a road sideline according to the road centerline vector data and the first target point cloud data; Deleting the point cloud data belonging to the road coverage area from the first target point cloud data to obtain second target point cloud data; A digital elevation model is constructed based on the second target point cloud data and the road edge line.

7. The method according to claim 6, characterized in that Constructing a digital elevation model according to the second target point cloud data and the road edge line, including: Obtaining initial water level boundary data of a target water area within a target area, and obtaining the water area type of the target water area; Obtaining target water level boundary data of the target water area according to the water area type and initial water level boundary data of the target water area; updating the water area point cloud data in the second target point cloud data according to the water area type of the target water area to obtain third target point cloud data; A digital elevation model is constructed based on the third target point cloud data, the road edge line and the target water level line boundary data.

8. A device for constructing a digital elevation model, characterized in that: include: A first acquisition module is configured to acquire point cloud data of a target area; a determination module configured to determine vegetation point cloud data and building point cloud data from the point cloud data of the target area; A second acquisition module is configured to acquire first target point cloud data of the target area based on the point cloud data of the target area, the vegetation point cloud data and the building point cloud data; The generating module is configured to generate a digital elevation model according to the first target point cloud data.

9. An electronic device comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to execute the method for constructing a digital elevation model according to any one of claims 1 to 7 when running the program instructions.

10. A storage medium storing program instructions, characterized in that: The program instructions are executed by a processor to implement the method for constructing a digital elevation model according to any one of claims 1 to 7.

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