Road slope digital elevation model partition reconstruction method based on unmanned aerial vehicle

Abstract

The invention discloses a road slope digital elevation model partition reconstruction method based on an unmanned aerial vehicle, and the method comprises the steps: firstly, carrying out the grid division of point cloud data of a reconstruction region, determining a feature code corresponding to a grid according to the topographic feature in a region range corresponding to the grid, and generating a global feature code grid of the reconstruction region; then, classifying all point clouds covered by all grids belonging to the same feature code in the feature code grids into the same point cloud subset to form point cloud subsets corresponding to different feature codes; and then, carrying out interpolation processing on the point cloud subsets corresponding to the feature codes by adopting optimal interpolation parameters matched with the feature codes, and generating corresponding local digital elevation models. And finally, combining all the local digital elevation models to obtain a digital elevation model of the reconstruction region. The partition independent optimization interpolation of the reconstruction area is realized, the limitation of the global single interpolation method of the traditional DEM reconstruction is solved, and the terrain reconstruction precision and the detail reduction capability are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle remote sensing data processing, and particularly relates to a method for reconstructing a digital elevation model of a highway slope based on an unmanned aerial vehicle. Background Art

[0002] Existing digital elevation model interpolation methods (such as TIN, IDW, Kriging, etc.) have obvious limitations in complex terrain areas. Global single interpolation methods are difficult to adapt to the terrain feature differences in flat areas, gentle slope areas, and steep slope areas of highway slopes. For example, there are elevation mutations at the junction of steep slopes and flat areas, resulting in terrain continuity breaks and loss of terrain details. Traditional hybrid interpolation, on the other hand, has the problem of fixed parameters and lack of a dynamic adjustment mechanism, resulting in insufficient matching between terrain partitions and interpolation methods, affecting the reconstruction accuracy and detail restoration ability of the digital elevation model. Summary of the Invention

[0003] In view of the deficiencies of the existing technology, the present invention proposes a method for reconstructing a digital elevation model of a highway slope based on an unmanned aerial vehicle, which can improve the reconstruction accuracy and detail restoration ability of the digital elevation model. The specific technical solutions are as follows: There is provided a method for reconstructing a digital elevation model of a highway slope based on an unmanned aerial vehicle. In a first implementable manner, it includes: Performing grid division on the point cloud data of the reconstruction area, and determining the feature code corresponding to each grid to form a global feature code grid; Segmenting the point cloud data based on the feature code grid to generate point cloud subsets corresponding to each feature code; Interpolating each point cloud subset according to the optimal interpolation parameters matched by the feature code, and generating corresponding local digital elevation models; Merging all the local digital elevation models to generate a digital elevation model of the reconstruction area.

[0004] Combined with the first implementable manner, in a second implementable manner, determining the feature code corresponding to each grid includes: Classifying the feature categories of each grid cell according to different classification criteria respectively to generate corresponding feature classification mask grids; Overlaying all the feature classification mask grids, and combining all the feature categories corresponding to each grid cell to generate corresponding feature codes.

[0005] Combined with the first implementable manner, in a third implementable manner, segmenting the point cloud data based on the feature code grid includes: Merge the geographical ranges corresponding to all raster cells with the same feature encoding in the feature encoding raster into corresponding encoded regions, and divide the point cloud located within the encoded region into the same point cloud subset.

[0006] Combined with the third implementation method, in the fourth implementation method, merging the geographical ranges corresponding to all raster cells with the same feature encoding into an encoded region includes: Convert the geographical ranges of each raster cell with the same feature encoding into geographical polygons respectively; Use a spatial merging algorithm to merge the scattered geographical polygons into an encoded region.

[0007] Combined with the third implementation method, in the fifth implementation method, segmenting the point cloud data based on the feature encoding raster includes: When the point cloud is located at the junction of multiple encoded regions, divide the point cloud subset to which the point cloud belongs according to the feature encoding of the raster cell to which the point cloud belongs; When the number of point clouds within the encoded region is 0, record the point cloud subset as an empty set, and use the default parameters or the optimal interpolation parameters matching the point cloud subset of the adjacent encoded region for interpolation during interpolation; When the number of point clouds within the encoded region is less than the quantity threshold, merge the point cloud subset with the point cloud subset of the adjacent encoded region.

[0008] Combined with the first implementation method, in the sixth implementation method, interpolating the point cloud subset according to the optimal interpolation parameters matched by the feature encoding includes: Define a parameter mapping table between the feature encoding and the optimal interpolation parameters; Match the feature encoding corresponding to the point cloud subset with the predefined parameter mapping table to determine the optimal interpolation parameters matched by the feature encoding.

[0009] Combined with the sixth implementation method, in the seventh implementation method, defining a parameter mapping table between the feature encoding and the optimal interpolation parameters includes: Obtain the point cloud data corresponding to different feature encodings respectively to construct corresponding sample data sets; Interpolate each sample data set with different interpolation parameters respectively, and evaluate the interpolation performance of each sample data set with different interpolation parameters; Compare the interpolation performances of different interpolation parameters corresponding to each sample data set respectively, determine the optimal interpolation parameters of each sample data set, and generate a parameter mapping table between the feature encoding and the optimal interpolation parameters.

[0010] Combined with the seventh implementation method, in the eighth implementation method, comparing the interpolation performances of different interpolation parameters corresponding to the sample data set includes: Interpolated results obtained by interpolating using different interpolation parameters according to the sample data set are used to calculate various interpolation performance indicators for different interpolation parameters; According to the corresponding various interpolation performance indicators, performance quantization indices corresponding to each interpolation parameter are calculated respectively; Compare the performance quantization indices corresponding to each interpolation parameter to determine the optimal interpolation parameter for the sample data set.

[0011] Combined with the first implementation manner, in the ninth implementation manner, generating a digital elevation model of the reconstruction area includes: Perform intersection detection on the digital elevation model to determine the intersection area in the digital elevation model; Convert the intersection area into a binary raster mask and determine the overlapping raster cells in the binary raster mask; Obtain the Z values of the overlapping raster cells in each local digital elevation model, and calculate the fused Z value of the overlapping raster cells in combination with the weights corresponding to each local digital elevation model; Update the Z values of the corresponding areas in the digital elevation model according to the fused Z value.

[0012] Combined with the ninth implementation manner, in the tenth implementation manner, determine the weight corresponding to the local digital elevation model according to the interpolation accuracy of the corresponding point cloud subset.

[0013] Beneficial effects: By using the method for partitioning and reconstructing the digital elevation model of a highway slope based on an unmanned aerial vehicle of the present invention, the point cloud data of the reconstruction area can be partitioned according to the terrain features, the point cloud subsets corresponding to different terrain feature areas can be determined, and then the optimal interpolation parameters matching the terrain features are used to perform interpolation processing on each point cloud subset, realizing independent and optimized interpolation for each partition, thereby solving the limitations of the traditional global single interpolation method for DEM reconstruction and significantly improving the terrain reconstruction accuracy and detail restoration ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the specific embodiments of the present invention, the drawings required for use in the specific embodiments will be briefly introduced below. In all the drawings, the components or parts do not necessarily draw according to the actual ratio.

[0015] Figure 1 It is a flowchart of a method for partitioning and reconstructing a digital elevation model of a highway slope based on an unmanned aerial vehicle provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The embodiments of the technical solutions of the present invention will be described in detail below with reference to the drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention, and thus are only examples and cannot be used to limit the protection scope of the present invention.

[0017] As Figure 1 shown in the flowchart of the UAV-based digital elevation model zoning reconstruction method for highway slopes, the reconstruction method includes: Step 1: Perform grid division on the point cloud data of the reconstruction area, and determine the feature coding corresponding to each grid to form a global feature coding grid; Step 2: Segment the point cloud data based on the feature coding grid to generate point cloud subsets corresponding to each feature coding; Step 3: Interpolate each point cloud subset according to the optimal interpolation parameters matched by the feature coding, and generate corresponding local digital elevation models; Step 4: Merge all local digital elevation models to generate a digital elevation model of the reconstruction area.

[0018] Specifically, first, the point cloud data of the reconstruction area can be grid-divided, and the feature coding corresponding to each grid can be determined according to the terrain features within the area range corresponding to each grid, generating a global feature coding grid for the reconstruction area. Then, all the point clouds covered by all the grids belonging to the same feature coding in the feature coding grid can be grouped into the same point cloud subset, thus forming point cloud subsets corresponding to different feature codings. After that, the optimal interpolation parameters matched with the feature coding can be used to perform interpolation processing on the point cloud subsets corresponding to each feature coding and generate corresponding local digital elevation models. Finally, all local digital elevation models are merged to obtain the digital elevation model of the reconstruction area. In this way, independent optimization interpolation of different zones in the reconstruction area can be realized, thereby solving the limitations of the traditional global single interpolation method for DEM reconstruction and significantly improving the terrain reconstruction accuracy and detail restoration ability.

[0019] In this embodiment, optionally, determining the feature coding corresponding to each grid includes: Classify the feature categories of each grid cell according to different classification criteria respectively to generate corresponding feature classification mask grids; Overlay all the feature classification mask grids and combine all the feature categories corresponding to each grid cell to generate corresponding feature coding.

[0020] Specifically, in Step 1, first, the feature categories of each grid can be classified according to different feature classification criteria to generate corresponding feature classification mask grids. For example, the point cloud data of the reconstruction area can be divided into terrain slope grids and point cloud density grids according to the same spatial resolution. The spatial resolution is consistent with the digital elevation model. Then, a terrain partition discrimination function can be used to classify each grid in the terrain slope grid and mark the terrain category to which each grid belongs, thereby forming a terrain mask grid. The terrain partition discrimination function can be set as: ; Among them, is the slope angle corresponding to the grid. The flat area emphasizes smoothness and efficiency, the gentle slope area balances continuity and details, and the steep slope area gives priority to retaining mutation features.

[0021] Similarly, the number of point clouds in each grid of the point cloud density grid can be counted, and each grid can be classified according to the set density zoning standard, and the density category of each grid can be marked, so as to form a density mask grid. The specific classification criteria are as follows: ; Among them, D is the number of point clouds in the grid.

[0022] Then, the density mask grid and the terrain mask grid can be superimposed, and the density category and terrain category corresponding to the overlapping grids can be combined to form the feature encoding of the grid. The formed feature encoding is as follows:

[0023] In this embodiment, optionally, segmenting the point cloud data based on the feature encoding grid includes: Merging the geographical ranges corresponding to all grid cells with the same feature encoding in the feature encoding grid into corresponding encoding regions, and dividing the point clouds located within the encoding regions into the same point cloud subset.

[0024] Specifically, first, according to the geographical coordinates of each grid, the geographical ranges corresponding to each grid cell with the same feature encoding in the feature encoding grid can be converted into geographical polygons. Then, an existing spatial merging algorithm is used to merge the geographical polygons of adjacent grids, so as to merge the scattered geographical polygons into encoding regions. Finally, the point clouds falling within the encoding regions are retrieved, and all the retrieved point clouds are divided into the point cloud subsets corresponding to the same feature encoding, so as to realize the topographic feature zoning of the point clouds in the reconstruction region, providing a basis for subsequent independent optimization interpolation of each zone.

[0025] In this embodiment, optionally, segmenting the point cloud data based on the feature encoding grid includes: When the point cloud is located at the junction of encoding regions with multiple different feature encodings, dividing the point cloud subset to which the point cloud belongs according to the feature encoding of the grid cell to which the point cloud belongs; When the number of point clouds in the encoding region is 0, the point cloud subset is recorded as an empty set, and the default parameters or the optimal interpolation parameters matching the point cloud subsets of adjacent encoding regions are used for interpolation during interpolation; When the number of grids in the encoding region is less than the quantity threshold, the point cloud subset is merged with the point cloud subsets of adjacent encoding regions.

[0026] Specifically, when performing point cloud division, it is possible that the point cloud is located at the junction of the coding regions corresponding to multiple feature encodings. For such a situation, the grid to which the point cloud belongs can be determined according to the geographical coordinates corresponding to the point cloud, and the point cloud subset to which the point cloud belongs can be determined according to the feature encoding corresponding to the grid.

[0027] It is also possible that the number of point clouds in the coding region is 0, such as water areas. For such a situation, the point cloud subset corresponding to this region can be recorded as an empty set. When interpolating the empty set, the optimal interpolation parameters corresponding to the feature encodings of the adjacent regions of this region can be referred to for interpolation, or the default parameters can be used.

[0028] It is also possible that the number of grids in the coding region is small, only 1 or 2 grids. For such a situation, these two grids can be merged into other adjacent coding regions, and the point clouds in the grids can be incorporated into the point cloud subsets corresponding to the adjacent coding regions, or merged into the point cloud subsets with similar feature encodings, so as to avoid interpolation over-parameterization.

[0029] In this embodiment, optionally, interpolating the point cloud subset according to the optimal interpolation parameters matched by the feature encoding includes: Defining a parameter mapping table between the feature encoding and the optimal interpolation parameters; Matching the feature encoding corresponding to the point cloud subset with the predefined parameter mapping table to determine the optimal interpolation parameters matched by the feature encoding.

[0030] Specifically, first, it is necessary to determine the optimal interpolation parameters matched by different feature encodings, determine the mapping relationship between the feature encoding and the optimal interpolation parameters, and form a corresponding parameter mapping table. Then, the feature encodings corresponding to each point cloud subset can be matched with the parameter mapping table, so as to determine the optimal interpolation parameters corresponding to each point cloud subset. The optimal interpolation parameters include the interpolation method most suitable for the terrain feature corresponding to the feature encoding, and the parameter values corresponding to the various parameters required by the interpolation method.

[0031] In this embodiment, optionally, defining a parameter mapping table between the feature encoding and the optimal interpolation parameters includes: Respectively obtaining the point cloud data corresponding to different feature encodings to construct corresponding sample data sets; Respectively interpolating each sample data set with different interpolation parameters and evaluating the interpolation performance of each sample data set with different interpolation parameters; Respectively comparing the interpolation performances of different interpolation parameters corresponding to each sample data set, determining the optimal interpolation parameters of each sample data set, and generating a parameter mapping table between the feature encoding and the optimal interpolation parameters.

[0032] Specifically, when determining the mapping relationship between the feature encoding and the optimal interpolation parameter, first, the point cloud data corresponding to different terrain features can be obtained respectively, and the sample data sets corresponding to the feature encoding can be constructed respectively. Then, the sample data is interpolated using different interpolation parameters respectively, and the interpolation performance of each interpolation parameter for the sample data set is evaluated. Finally, the interpolation performances of different interpolation parameters for the sample data set are compared, and the interpolation parameter with the best interpolation performance is selected as the optimal interpolation parameter matching the feature encoding. By repeating this process, the optimal interpolation parameters corresponding to different encoded features can be obtained, and a parameter mapping table between the feature encoding and the optimal interpolation parameter can be formed.

[0033] In this embodiment, optionally, comparing the interpolation performances of different interpolation parameters corresponding to the sample data set includes: Calculating various interpolation performance indexes of different interpolation parameters respectively according to the interpolation results obtained by interpolating the sample data set with different interpolation parameters; Calculating the performance quantization index corresponding to each interpolation parameter respectively according to the corresponding various interpolation performance indexes; Comparing the performance quantization indexes corresponding to each interpolation parameter to determine the optimal interpolation parameter of the sample data set.

[0034] Specifically, when comparing the interpolation performances of different interpolation parameters, various interpolation performance indexes of the interpolation parameter can be calculated first according to the interpolation result of the sample data under the action of the interpolation parameter. The interpolation performance indexes can include MAE, RMSE, and R². Among them, R² can be used to evaluate the fitting degree of the interpolation surface and the real terrain, RMSE can emphasize the penalty for large errors, and MAE reflects the average absolute deviation between the interpolation result and the real value. To ensure the accuracy of the evaluation result, it can be repeated multiple times and the average value of each interpolation performance index can be taken.

[0035] To quantify the interpolation performance with the same dimension, interval normalization processing can be performed on MAE, RMSE, and R², and the performance quantization index can be calculated using the following calculation formula: ; Among them, , , are the weight coefficients corresponding to each interpolation performance index respectively, , , are the maximum values of each interpolation performance index corresponding to all data sample sets under the current feature encoding respectively. is the set of interpolation parameters corresponding to the current feature encoding.

[0036] Finally, compare the performance quantization indices of different interpolation parameters for the sample data set, and select the interpolation parameter with the largest performance quantization index as the optimal interpolation parameter matching the feature encoding. Repeat this process to obtain the optimal interpolation parameters corresponding to different encoded features. Based on the currently collected sample data, the parameter mapping table between the formed feature encoding and the optimal interpolation parameter is shown in the following table:

[0037] It should be understood that the parameters recorded in the above table are only the evaluation results based on the currently collected sample data, and do not represent that the optimal interpolation parameters and methods corresponding to each feature encoding are as shown in the above table. As the sample data increases, the optimal interpolation parameters and methods corresponding to each feature encoding in the above table may also change.

[0038] In this embodiment, optionally, generating a digital elevation model of the reconstruction area includes: Perform intersection detection on the digital elevation model to determine the intersection area in the digital elevation model; Convert the intersection area into a binary raster mask to determine the overlapping raster cells in the binary raster mask; Obtain the Z values of the overlapping raster cells in each local digital elevation model, and calculate the fused Z value of the overlapping raster cells in combination with the weights corresponding to each local digital elevation model; Update the Z value of the corresponding area in the digital elevation model according to the fused Z value.

[0039] Specifically, after determining the optimal interpolation parameters for each point cloud subset match, a distributed computing framework can be used to perform interpolation optimization processing on each point cloud subset in parallel to improve data processing efficiency. Then, generate the corresponding local digital elevation model according to the optimized point cloud subset. Finally, preliminarily merge all local digital elevation models according to their spatial positions to form an initial global digital elevation model.

[0040] Since there may be overlapping parts between local digital elevation models, the initial global digital elevation model generated by the merger has an overlapping area. For this reason, a topological retrieval tool can be used to perform intersection detection on the initial global digital elevation model to determine the overlapping area in the initial global digital elevation model. Then, convert the overlapping area into a binary raster mask, where the raster is 1 indicating the overlapping area of the local digital elevation models and 0 indicating the non-overlapping area. For the overlapping raster cells marked as 1 in the binary raster mask, all local digital elevation models covering the raster can be determined through a spatial index or a hash table, and according to the Z values of the raster in each local digital elevation model, combined with the weights corresponding to the local digital elevation models, the fused Z value corresponding to the overlapping raster cell can be calculated. The specific calculation formula is as follows: ; wherein, is the Z value corresponding to the overlapping grid cell in the local digital elevation model, is the weight corresponding to the local digital elevation model, is the number of local digital elevation models covering the overlapping grid cell. Finally, the fused Z value corresponding to the overlapping grid cell can be written into the initial global digital elevation model to obtain the final digital elevation model.

[0041] In this embodiment, optionally, the weight corresponding to the local digital elevation model is determined according to the interpolation accuracy of the corresponding point cloud subset.

[0042] Specifically, the weight corresponding to the local digital elevation model can be set according to the interpolation accuracy of the point cloud subset for constructing the local digital elevation model. The higher the interpolation accuracy, the greater the weight corresponding to the local digital elevation model. The specific calculation formula of the weight is as follows: ; wherein, is the loop variable in the summation process, indicating the traversal from the 1st local digital elevation model (k = 1) to the th local digital elevation model (k = n).

[0043] Since the same grid cell may have multiple Z values, to improve the accuracy of the reconstructed digital elevation model, it is necessary to select the Z value with the highest interpolation accuracy, or perform weighted summation according to the interpolation accuracy weights. The higher the interpolation accuracy, the greater the weight, and the lower the interpolation accuracy, the smaller the weight. Only in this way can the accuracy of the fused Z value be guaranteed.

[0044] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered by the scope of the claims and the specification of the present invention.

Claims

1. A method for reconstructing highway slope digital elevation models based on drones, characterized in that: include: Divide the point cloud data of the reconstruction area into grids, and determine the feature code corresponding to each grid to form a feature code grid for the entire area; Segment the point cloud data based on the feature coding grid to generate point cloud subsets corresponding to each feature coding; Interpolating each point cloud subset according to the optimal interpolation parameters matched by the feature code, and generating a corresponding local digital elevation model; All the local digital elevation models are merged to generate a digital elevation model of the reconstruction area.

2. The method for reconstructing highway slope digital elevation models based on drones according to claim 1 is characterized in that: Determine the feature code corresponding to each grid, including: Classify the feature category of each grid unit according to different classification standards and generate corresponding feature classification mask grids; All feature classification mask grids are superimposed, and all feature categories corresponding to each grid cell are combined to generate the corresponding feature code.

3. The method for reconstructing highway slope digital elevation models based on drones according to claim 1 is characterized in that: Segmentation of point cloud data based on feature-coded grids, including: The geographical ranges corresponding to all grid cells with the same feature code in the feature coding grid are merged into a corresponding coding area, and the point clouds located in the coding area are divided into the same point cloud subset.

4. The method for reconstructing highway slope digital elevation models based on drones according to claim 3 is characterized in that: Merge the geographic ranges corresponding to all grid cells with the same feature code into a coded area, including: Convert the geographic range of each raster unit of the same feature code into a geographic polygon; A spatial merging algorithm is used to merge scattered geographic polygons into coded regions.

5. The method for reconstructing highway slope digital elevation models based on drones according to claim 3 is characterized in that: Segmentation of point cloud data based on feature-coded grids, including: When a point cloud is located at the junction of multiple coding areas, dividing the point cloud into a point cloud subset according to the feature coding of the grid unit to which the point cloud belongs; When the number of point clouds in the coding area is 0, the point cloud subset is recorded as an empty set, and the default parameters or the optimal interpolation parameters matching the point cloud subsets of the adjacent coding areas are used for interpolation; When the number of point clouds in the coding region is less than a number threshold, the point cloud subset is merged with a point cloud subset of an adjacent coding region.

6. The method for reconstructing highway slope digital elevation models based on drones according to claim 1 is characterized in that: Interpolating the point cloud subset according to the optimal interpolation parameters matched by the feature code, including: Define a parameter mapping table between feature encoding and optimal interpolation parameters; The feature codes corresponding to the point cloud subset are matched with a predefined parameter mapping table to determine the optimal interpolation parameters for the feature code matching.

7. The method for reconstructing highway slope digital elevation models based on unmanned aerial vehicles according to claim 6 is characterized in that: Define the parameter mapping table between feature encoding and optimal interpolation parameters, including: Obtain the point cloud data corresponding to different feature codes respectively to build corresponding sample data sets; Different interpolation parameters are used to interpolate each sample data set, and the interpolation performance of each sample data set using different interpolation parameters is evaluated; The interpolation performances of different interpolation parameters corresponding to each sample data set are compared respectively, the optimal interpolation parameters of each sample data set are determined, and a parameter mapping table between feature coding and optimal interpolation parameters is generated.

8. The method for reconstructing highway slope digital elevation models based on unmanned aerial vehicles according to claim 7 is characterized in that: Compare the interpolation performance of different interpolation parameters corresponding to the sample data set, including: Calculate various interpolation performance indicators of different interpolation parameters according to interpolation results obtained after interpolation of the sample data set using different interpolation parameters; According to the corresponding interpolation performance indicators, the performance quantification index corresponding to each interpolation parameter is calculated respectively; The performance quantitative indexes corresponding to each interpolation parameter are compared to determine the optimal interpolation parameters for the sample data set.

9. The method for reconstructing highway slope digital elevation models based on drones according to claim 1, characterized in that: Generate a digital elevation model of the reconstruction area, including: Performing intersection detection on the digital elevation model to determine the intersection area in the digital elevation model; Convert the intersection area into a binary raster mask and determine the overlapping raster cells in the binary raster mask; Obtain the Z value of the overlapping grid cells in each local digital elevation model, and calculate the fused Z value of the overlapping grid cells in combination with the weights corresponding to each local digital elevation model; The Z value of the corresponding area in the digital elevation model is updated according to the fused Z value.

10. The method for reconstructing highway slope digital elevation models based on unmanned aerial vehicles according to claim 9, characterized in that: According to the interpolation accuracy of the corresponding point cloud subset, the weight corresponding to the local digital elevation model is determined.

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