A method for extracting negative contour lines based on original contour lines

Abstract

This invention provides a method for extracting negative contour lines based on original contour lines, comprising the following steps: S1) Acquisition of original data: Obtaining original contour line data through mapping equipment; S2) Extraction of vector depression points: Based on the original contour line data, using windowing calculation and simulation, obtaining vector defect points in the elevation surface; S3) Extraction of depression areas in the original contour line data: Using the original contour line data, obtaining the preliminary vector depression range based on depression filling; S4) Optimization of depression range: Using spatial attribute fusion and spatial difference methods, obtaining the optimized depression range; S5) Obtaining negative contour lines: Spatially connecting the optimized depression range and vector defect points to obtain the final depression area, and performing spatial intersection operation between the final depression area and the original contour line data to obtain negative contour lines.

Description

Technical Field

[0001] This invention belongs to the field of contour line extraction and recognition technology, and specifically relates to a negative contour line extraction method based on the original contour lines. Background Technology

[0002] Contour lines play a crucial role in interpreting topographic maps. When using topographic maps, people often understand the Earth's surface morphology by analyzing and interpreting the spatial relationships between contour lines. Therefore, various applications based on contour maps frequently require the representation of these spatial relationships. Furthermore, contour topographic maps are also a significant product of digital mapping production.

[0003] Negative contour lines appear as contour lines in geographic space representing areas of depression or elevation decrease. Marking topographic features such as negative and non-negative contour lines is an important step when creating topographic maps.

[0004] However, there is currently no mature method to automatically and quickly distinguish between negative and non-negative contour lines from raw contour data. Although some scholars have proposed using a "contour tree" method to filter the results, this approach is inefficient and inaccurate on a large scale, making it unsuitable for large-scale production. Therefore, at present, the production of topographic maps still relies primarily on manual methods, with operators wearing stereoscopic glasses and using specialized stereoscopic acquisition equipment to manually select contours, which significantly limits production efficiency.

[0005] In existing technologies, human thought can directly recognize and memorize the spatial relationships of contour lines on simulated topographic maps, but computers struggle to do so because they rely on geometric coordinates to memorize and store the positions of spatial entities. Therefore, transforming the spatial relationships of contour lines into data structures or models that can be effectively described, expressed, recognized, and calculated in a computer environment is an effective way to achieve automatic terrain identification. Summary of the Invention

[0006] This invention provides a method for extracting negative contour lines based on original contour lines. It obtains the final negative contour lines by processing and extracting the original contour line data.

[0007] To achieve the above-mentioned technical effects, the present invention is implemented through the following technical solutions.

[0008] A method for extracting negative contour lines based on original contour lines includes the following steps:

[0009] S1) Acquisition of raw data: Obtain raw contour line data through mapping equipment;

[0010] S2) Extraction of vector depression points: Based on the original contour data, vector defect points in the elevation surface are obtained through window calculation and simulation.

[0011] S3) Extraction of depression areas from original contour data: Using the original contour data, the preliminary vector depression range is obtained based on the depression filling.

[0012] S4) Optimization of the concave range: The optimized concave range is obtained by using spatial attribute fusion and spatial difference methods;

[0013] S5) Obtaining negative contour lines: Spatially connect the optimized depression range and vector defect points to obtain the final depression area. Perform spatial intersection operation between the final depression area and the original contour line data to obtain negative contour lines.

[0014] The difference between this solution and existing technologies lies in the following:

[0015] Furthermore, before extracting the vector depression points in step S2), the method also includes obtaining a simulated elevation surface. Specifically, it involves using the elevation values ​​in the contour data attributes to extract point data at several nodes on the original contour lines and assigning elevation attributes to the point data to obtain a simulated elevation surface.

[0016] In this scheme, the original data is used to perform point transformation and threshold simulation to obtain a simulated elevation surface. In this process, in conjunction with various subsequent calculations, although some simulations are performed initially, there are fewer subsequent simulations and more combinations and comparisons with the original data, which improves the accuracy.

[0017] This solution flexibly integrates spatial interpolation algorithms, window filtering algorithms, spatial raster overlay operations, spatial intersection operations, spatial difference operations, and spatial attribute connection algorithms from digital terrain analysis. This optimizes the simulation of landforms, ensures a true reflection of negative landforms, and thus accurately extracts negative contour lines. The solution is logically simple to understand and easy to operate.

[0018] Furthermore, the construction of the simulated elevation surface specifically includes the following steps:

[0019] A1) Creating Thiessen polygons: Create Thiessen polygons based on point data on the original contour lines;

[0020] A2) Interpolation: Interpolate the unknown region to modify the Thiessen polygons so that they form new Thiessen polygons in the unknown region.

[0021] A3) Obtaining the elevation surface: Based on the area of ​​the overlapping region of the original Thiessen polygon and the new Thiessen polygon, combined with the weights and the elevation of the unknown points, a simulated elevation surface is formed.

[0022] In this scheme, the spatial interpolation method of Thiessen polygons is used to ensure that no simulated or inferred surface morphology is generated, and a simulated elevation surface that is more closely related to the surface morphology is formed. In essence, it is an adjustment after the original interpolation. The size of the area of ​​the overlapping region between the new Thiessen polygon and the original Thiessen polygon determines the weight of nearby different elevation points. The larger the area of ​​the point, the higher its weight, and the greater the influence on the elevation of unknown points.

[0023] Furthermore, the interpolation step A2) specifically involves selecting sample points where the new Thiessen polygon intersects with the Thiessen polygon in the region to be interpolated, and using them to participate in the interpolation.

[0024] Current methods for generating elevation surfaces incorporate simulation and inference to more closely approximate the actual landform. However, these methods can also create false topography and depressions, hindering the accurate extraction of negative landforms. This proposed method, which omits simulation and inference, while resulting in a slight difference from the actual terrain, is more conducive to the accurate extraction of negative landforms.

[0025] Further, the extraction of vector depression points in step S2) specifically involves: performing a raster windowing operation on the original contour data, performing minimum value filtering on the windowed area to obtain the filtered elevation surface; if the value of the original elevation surface data minus the filtered elevation surface is 0, then it is a vector depression point.

[0026] In this scheme, when identifying and extracting all depressions from the obtained elevation surface data, a raster windowing operation is first performed on the elevation surface data. The window size is determined by the contour interval of the original contour lines, and through analysis and experiments, it is generally twice the contour interval (assuming a contour interval of 10 meters, the window size for the windowing operation is a pixel range of 20 rows * 20 columns). Then, minimum value filtering is performed within the window range to obtain the filtered elevation surface. Finally, the filtered elevation surface is subtracted from the original elevation surface data; the areas with a value of 0 after the subtraction are the depressions.

[0027] Further, step S2) extraction of vector depression points: specifically, using a simulated elevation surface for windowed median filtering, and then performing a difference operation between the filtered elevation surface and the filled elevation surface to obtain the preliminary vector depression range.

[0028] In this scheme, the difference between the filled elevation surface and the filtered elevation surface is calculated, so that all calculation processes are based on the original data, and the final map will be closer to the original and have less error.

[0029] Furthermore, the depression filling in the extraction of vector depression points in step S2) specifically involves: within the windowed area, comparing the elevation values ​​of the central pixel with those of other pixels; when the elevation values ​​of other pixels are low, changing the central pixel to achieve depression filling on the elevation surface.

[0030] In this scheme, when the elevation of the center pixel is lower than the elevations of other pixels, the pixel with the lowest elevation is selected as the new center pixel.

[0031] For this elevation surface data, depression filling is performed (within the window area, if the elevation value of the central pixel is lower than the elevation values ​​of its surrounding 8 pixels, then the lowest elevation value of the surrounding 8 pixels is assigned to the central pixel; if the elevation value of the central pixel is equal to or greater than the elevation values ​​of its surrounding 8 pixels, then the elevation value of the central pixel is not changed). Figure 4 The difference between the median-filtered elevation surface data (before the depression was filled) and the elevation surface data after the depression was filled is calculated. Areas with a difference greater than 0 are considered to be areas that are not depressed.

[0032] Further, the optimization of the depression range in step SS4) is specifically as follows: using the original contour data to obtain contour surfaces, and based on the spatial connection of the contour surfaces and the preliminary vector depression range, combined with spatial discrepancies, the optimized depression range is obtained.

[0033] In this solution, the processed initial vector depression range is spatially connected with the original one again, and comparison and connection with the original are continuously achieved to ensure that the final product has a high degree of correlation with the original.

[0034] Furthermore, the method also includes obtaining the final negative contour line results. Specifically, the obtained negative contour lines are divided into lines according to elevation information areas, and attribute information is optimized by combining redundant attributes to obtain the final negative contour line results. In this scheme, the classification of lines allows the obtained negative contour line results to be displayed in the form of surfaces, making the effect more intuitive.

[0035] Furthermore, this also includes obtaining non-negative contour lines: by spatial differentiation of the obtained negative contour results, non-negative contour lines can be obtained. Specifically, non-negative contour lines can be obtained from negative contour lines, and with further information differentiation, the final output of non-negative contour lines can be achieved.

[0036] This invention utilizes digital terrain analysis technology to develop a rapid method for extracting negative contour lines based on raw contour data. This method can be applied to large-scale topographic map production, significantly improving efficiency while ensuring accuracy. Using this invention reduces the manual collection of negative contour lines by approximately 90%. Taking the Loess Plateau in northern Shaanxi and the desert regions of Xinjiang as examples, the time required to collect negative contour lines for a single 1:10,000 scale topographic map was previously approximately 3-3.5 hours per person; using this method, automatic extraction takes approximately 55 seconds, greatly reducing manpower requirements. Attached Figure Description

[0037] Figure 1 A flowchart of a negative contour line extraction method based on original contour lines provided by the present invention;

[0038] Figure 2 A detailed flowchart of a negative contour line extraction method based on original contour lines provided by the present invention;

[0039] Figure 3 A schematic diagram of the fitting of the elevation surface provided by the present invention;

[0040] Figure 4 An elevation longitudinal profile after elevation surface fitting provided by the present invention;

[0041] Figure 5 This is a schematic diagram of the structure of the difference method provided by the present invention;

[0042] Figure 6 This is a topographic map of the experimental area in Embodiment 3 of the present invention;

[0043] Figure 7 This is a topographic map of the original contour lines in the experimental area provided in Embodiment 3 of the present invention;

[0044] Figure 8 This is a topographic map showing the negative contour line extraction results in the experimental area of ​​Embodiment 3 provided by the present invention;

[0045] Figure 9 The topographic map after distinguishing the primary curve and the calculated curve in the negative contour line results in the experimental area of ​​Embodiment 3 provided by the present invention;

[0046] Figure 10 This is a schematic diagram of the extraction results in the experimental area of ​​Embodiment 3 provided by the present invention. Detailed Implementation

[0047] The present invention will now be described in detail with reference to the embodiments shown in the accompanying drawings. However, it should be noted that these embodiments are not intended to limit the present invention. Equivalent changes or substitutions in function, method, or structure made by those skilled in the art based on these embodiments are all within the scope of protection of the present invention.

[0048] In the description of this embodiment, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting the invention.

[0049] Furthermore, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more. The terms "installed," "connected," and "linked" should be interpreted broadly; for example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0050] Example 1

[0051] This invention provides a method for rapidly extracting negative contour lines based on original contour data. By inputting original contour line data, the method preprocesses the contour lines to construct elevation surfaces, extracts depressions, constructs depression contour surfaces, and performs digital terrain spatial analysis. This enables the differentiation and extraction of both negative and non-negative contour lines. Compared to other methods, this method is convenient to use, fast, and significantly improves production efficiency.

[0052] The process in this invention is as follows:

[0053] Step 1: Convert the original contour data into elevation point data;

[0054] (This method is a basic and general method for geographic information systems. A line is composed of multiple points, and this method extracts the points at the nodes of the line. This is not directly related to the present invention, but is a step in the method of the present invention, which uses some general methods.)

[0055] Step 2: Fit the elevation point data and convert it into elevation surface data, controlling the resolution to be half of the contour interval;

[0056] Step 3: Identify and extract all depressions on the elevation surface based on the elevation surface data from Step 2;

[0057] Step 4: Identify and extract the range of all depression areas based on the elevation surface data from Step 2;

[0058] Step 5: Generate contour surface data based on the elevation values ​​of the original contour line data;

[0059] Step 6: Perform spatial overlay analysis on the contour surface from Step 5 and the concave region from Step 4, i.e., perform spatial intersection operation to obtain the spatially overlapping areas, thus obtaining the contour surface result of the concave region; here, we obtain surface data, not line data. (Surface data is obtained by intersecting surfaces)

[0060] Step 7: Connect the contour surface of the concave area obtained in Step 6 with all the concave points obtained in Step 3, and perform spatial overlay analysis with the original contour data to obtain the contour data of the concave area, which is the negative contour line.

[0061] Step 8: Perform spatial overlay analysis on the negative contour lines and the original contour lines, and perform spatial difference operation (intersection inversion to obtain spatially non-overlapping areas) to obtain non-negative contour line data, thereby distinguishing and extracting negative and non-negative contour lines;

[0062] Step 9: Automatically distinguish between primary curves, calculation curves, and intermediate curves based on elevation values ​​and contour intervals.

[0063] Lines are composed of points. Contour data is actually stored in computers as a large amount of point coordinate data. Converting the original contour data into elevation point data mainly utilizes the elevation values ​​in the contour data attributes. All points located at the nodes of the contour lines are extracted and assigned elevation attributes.

[0064] See attached document Figure 2-3In step 2 of this invention, the algorithm for obtaining the elevation surface by fitting elevation points uses a natural neighbor interpolation algorithm. The interpolation algorithm used in this method is characterized by being based on the coordinates and elevation information of the points themselves. No simulation or inference of the landform is incorporated during the interpolation process, thus avoiding the appearance of false depressions that could affect the accuracy of negative contour line extraction. Current methods for generating elevation surfaces incorporate simulation and inference because these processes result in a more accurate representation of the real landform, but they can also create false terrain and false depressions, hindering the accurate extraction of negative landforms. This method does not incorporate simulation or inference; although the overall appearance differs from the real terrain, it is more conducive to the accurate extraction of negative landforms. (See Appendix) Figure 3 A schematic diagram of the fitting result (partial) of the elevation surface. Assume that the elevation surface is cut by a straight line as shown in the diagram, and the resulting longitudinal profile of the elevation is as follows. Figure 4 As shown, Figure 3 The intersection of the median line and the contour lines and Figure 4 Consistent with the middle. And through Figure 4 It can be seen that at this time, no false depressions will appear on the Chenggao surface.

[0065] The specific method is as follows: First, create Thiessen polygons for all elevation point data obtained in step 1. When interpolating in an unknown region, these Thiessen polygons are modified, and a new Thiessen polygon is generated in the unknown region. Sample points in the Thiessen polygons that intersect with the Thiessen polygons of the region to be interpolated are used for interpolation. The weight of the unknown region is determined by the area of ​​intersection between the Thiessen polygon of the known elevation point and the newly generated Thiessen polygon of the interpolated point; the larger the area, the higher the weight. Figure 5 The result.

[0066] The spatial interpolation method of this invention utilizes the characteristics of Thiessen polygons, ensuring that it does not simulate or infer surface morphology. Furthermore, this invention can generate elevation surface data from elevation point data through spatial interpolation. Spatial interpolation uses the values ​​of known points to infer values ​​for unknown areas, thereby generating a surface.

[0067] When identifying and extracting all depressions from the obtained elevation surface data, a raster windowing operation is first performed on the elevation surface data. The window size is determined by the contour interval of the original contour lines, and through analysis and experimentation, it is generally twice the contour interval (assuming a contour interval of 10 meters, the window size for the windowing operation is a pixel range of 20 rows * 20 columns). Then, minimum value filtering is performed within the window range to obtain the filtered elevation surface. Finally, the original elevation surface data is subtracted from the obtained filtered elevation surface; the areas with a value of 0 after the subtraction are the depressions.

[0068] In step 4, when extracting the concave region, median filtering is applied to the original elevation surface based on the windowed area to obtain the median-filtered elevation surface data. This data is then subtracted from the minimum-filtered elevation surface data obtained in step 3. The area with a subtraction value greater than 0 is the concave region. The concave points and concave region ranges obtained in the above steps are superimposed and fused with the original contour data to filter out the contour lines (negative contour lines) of the concave region. Spatial discretization (intersection inversion) is then performed between the original contour lines and the contour lines of the concave region to obtain the contour lines (non-negative contour lines) of the non-concave region.

[0069] Example 2

[0070] See attached document Figure 2 As shown, in this embodiment, the entire detailed operation process is as follows:

[0071] The first step is to obtain the raw data.

[0072] The raw contour data layer is obtained using mapping equipment, filtered, and then the contour data is obtained.

[0073] The second step is the extraction of vector concave points.

[0074] The contour data is converted to points to obtain contour data. Then, the simulated elevation surface is obtained by interpolation simulation. The simulated elevation surface is then subjected to window minimum value filtering to obtain the filtered elevation surface. The difference between the filtered elevation surface and the simulated elevation surface is calculated to obtain the difference result. Then, the area with the result of 0 is extracted to obtain the raster depression point. After raster to vector conversion, the vector depression point is obtained.

[0075] The third step is to obtain the contour lines of the depressions.

[0076] Using the simulated elevation surface from the second step, a windowed median filter is performed to obtain a filtered elevation surface. The filtered elevation surface is then filled with depressions to obtain a filled elevation surface. The filled elevation surface is then compared with the previously filtered elevation surface. From the difference results, areas with results greater than 0 are extracted to obtain the range of the depression area. The depression area is then converted from raster to vector to obtain the preliminary vector depression range.

[0077] Step 4: Optimization of the depression range

[0078] The contour data obtained in the first step is transformed into a surface by elevation lines to obtain contour surfaces. The contour surfaces are then spatially connected with the preliminary vector concave range obtained in the third step to obtain concave contour surfaces. Spatial attribute fusion is performed on the concave contour surfaces to obtain fused contour surfaces. Fine patches are removed from the fused contour surfaces to obtain optimized contour surfaces. Spatial discretization is performed between the optimized contour surfaces and the fused contour surfaces in this step to obtain the calculation results.

[0079] The optimized contour surface is output as an element to obtain the optimized contour surface result. The optimized contour surface result is then subjected to a spatial interaction operation with the spatial difference result to obtain the final result. Redundant elements in the final result are then removed to obtain the optimized concave range.

[0080] Step 5: Outputting the contour results

[0081] The two vector depression points obtained in the second step are spatially connected with the optimized depression range obtained in the fourth step to obtain the final depression region. This region is then spatially intersected with the contour data obtained in the first step to obtain negative contour lines. The negative contour lines are then distinguished into primary curves, calculation curves, and intermediate curves based on elevation information to obtain the negative contour line results. The negative contour line results are then optimized using redundant attributes to obtain the final negative contour line results. The final negative contour line results are then spatially differentiated from the original contour line data layer in the first step to obtain non-negative contour lines. Finally, the primary curves, calculation curves, and intermediate curves are distinguished based on elevation information to obtain the final non-negative contour line results.

[0082] In this invention, the depression region is extracted by the difference between the median-filtered elevation surface and the median-filtered elevation surface after filling the depression. The depression point is extracted by the difference between the result of minimum filtering and the original elevation surface; the two are independent of each other. The method in this invention is to first determine the depression point, then determine the depression range, and then use both to jointly determine the negative contour line.

[0083] Example 3

[0084] This embodiment is mainly used for accuracy verification in this invention.

[0085] This invention was verified using real topographic data from a local area of ​​the Loess Plateau in northern Shaanxi. The terrain in northern Shaanxi is fragmented, with many areas exhibiting sand dune-like landforms. Figure 6 The depressions are densely distributed, making the extraction of negative contour lines difficult. Other methods require significant time and manpower, and demand a high level of professional skill from the operators.

[0086] The extraction process and results using this method are shown in [link to documentation]. Figure 7-10 As shown, negative contour lines were automatically extracted based on the original contour line data, and the negative contour lines were distinguished as primary curves and secondary curves.

[0087] Through append Figure 6 As can be seen, this map shows the topography of the experimental area. The numerous pit-like patterns densely distributed in the map represent the depression areas. Manually extracting contour lines from such densely distributed depression areas would be a huge undertaking and prone to omissions. Figure 7 This is the original contour map. Figure 8 The resulting image is after the negative contour lines have been extracted. The contour lines in the image are all located in the depression area. Figure 9 In order to be in Figure 8 Based on this, the initial curve and the calculation curve were distinguished, and Figure 10 This is a screenshot of the results for a local area of ​​the experimental region.

[0088] The results obtained through manual stereoscopic acquisition were considered true values. The accuracy of the results extracted using this method was verified, and the verification results are shown in Table 1. A total of 3268 negative contour lines and 3084 non-negative contour lines were automatically identified, with an overall accuracy of 96.88% and a Kappa coefficient of 0.94, indicating good quality results.

[0089] Table 1 Accuracy Verification Results

[0090]

[0091] The present invention has the following advantages:

[0092] (1) This invention makes full use of GIS digital terrain analysis technology and spatial overlay analysis technology, and flexibly integrates spatial interpolation algorithm, window filtering algorithm and spatial raster overlay operation, spatial intersection operation, spatial difference operation and spatial attribute connection algorithm in digital terrain analysis, optimizes the simulation of landform, ensures the true reflection of negative landform, and thus accurately extracts negative contour lines. It is simple to understand logically and easy to operate.

[0093] (2) The present invention has low requirements for the original contour data, can be applied to the production of large-scale, wide-range 1:10000 digital topographic map data, and has greatly improved efficiency, which has transformed the traditional production operation mode and eliminated the need for manual three-dimensional equipment to collect data line by line in large-scale data production.

[0094] (3) In terms of accuracy, the judgment of micro-topography is more accurate and the extraction precision is higher, which can fully meet the quality requirements of digital topographic map production with a scale of 1:10000.

[0095] In this invention, a filtering process is used when acquiring contour lines. This is mainly because there are some manually plotted non-contour lines in the original contour data layer. Therefore, it is necessary to filter out the contour data by using the GB code number of the attribute field. This step belongs to the data preprocessing stage.

[0096] The detailed descriptions listed above are merely specific descriptions of feasible embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. All equivalent embodiments or modifications made without departing from the spirit of the present invention should be included within the scope of protection of the present invention.

[0097] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0098] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for extracting negative contour lines based on original contour lines, characterized in that, Includes the following steps: S1) Acquisition of raw data: Obtain raw contour line data through mapping equipment; S2) Extraction of vector depression points: Based on the original contour data, vector defect points in the elevation surface are obtained through window calculation and simulation. S3) Extraction of depression areas from original contour data: Using the original contour data, the preliminary vector depression range is obtained based on the depression filling. S4) Optimization of the concave range: The optimized concave range is obtained by using spatial attribute fusion and spatial difference methods; S5) Obtaining negative contour lines: Spatially connect the optimized depression range and vector defect points to obtain the final depression area. Perform spatial intersection operation between the final depression area and the original contour line data to obtain negative contour lines.

2. The method for extracting negative contour lines based on original contour lines according to claim 1, characterized in that, Before extracting the vector concave points in step S2), the method further includes obtaining a simulated elevation surface. Specifically, this involves using the elevation values ​​in the contour data attributes to extract point data at several nodes on the original contour lines and assigning elevation attributes to the point data to obtain a simulated elevation surface.

3. The method for extracting negative contour lines based on original contour lines according to claim 2, characterized in that, The construction of the simulated elevation surface specifically includes the following steps: A1) Creating Thiessen polygons: Create Thiessen polygons based on point data on the original contour lines; A2) Interpolation: Interpolate the unknown region to modify the Thiessen polygons so that they form new Thiessen polygons in the unknown region. A3) Obtaining the elevation surface: Based on the area of ​​the overlapping region of the original Thiessen polygon and the new Thiessen polygon, combined with the weights and the elevation of the unknown points, a simulated elevation surface is formed.

4. The method for extracting negative contour lines based on original contour lines according to claim 3, characterized in that, The interpolation step A2) specifically involves selecting sample points where the new Thiessen polygon intersects with the Thiessen polygon in the region to be interpolated, and using these points for interpolation.

5. The method for extracting negative contour lines based on original contour lines according to claim 1, characterized in that, The extraction of vector depression points in step S2) specifically involves: performing a raster windowing operation on the original contour data, performing minimum value filtering on the windowed area to obtain the filtered elevation surface. If the value of the original elevation surface data minus the filtered elevation surface is 0, then it is a vector depression point.

6. The method for extracting negative contour lines based on original contour lines according to claim 2, characterized in that, Step S3) specifically involves: performing windowed median filtering using a simulated elevation surface, and then performing a difference calculation between the filtered elevation surface and the filled elevation surface to obtain the preliminary vector concavity range.

7. The method for extracting negative contour lines based on original contour lines according to claim 6, characterized in that, The depression filling in the extraction of vector depression points in step S3) is specifically as follows: within the windowed area, the elevation values ​​of the central pixel and other pixels are compared. When the elevation values ​​of other pixels are low, the central pixel is changed to achieve elevation surface depression filling.

8. The method for extracting negative contour lines based on original contour lines according to claim 6, characterized in that, The optimization of the depression range in step S4) is specifically as follows: using the original contour data to obtain contour surfaces, and based on the spatial connection of the contour surfaces and the preliminary vector depression range, combined with spatial discrepancies, the optimized depression range is obtained.

9. The method for extracting negative contour lines based on original contour lines according to claim 1, characterized in that, It also includes obtaining the final negative contour line results, specifically: dividing the obtained negative contour lines according to the elevation information area, and optimizing the attribute information by combining redundant attributes to obtain the final negative contour line results.

10. The method for extracting negative contour lines based on original contour lines according to claim 1, characterized in that, It also includes obtaining non-negative contour lines: by spatial differentiation of the obtained negative contour results, non-negative contour lines are obtained.

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