Automatic surveying and mapping method and system for complex terrain land parcels for urban and rural planning

Abstract

The invention relates to the technical field of topographic survey, in particular to an automatic surveying and mapping method and system for a complex topographic land parcel for urban and rural planning. The method comprises the following steps: acquiring satellite remote sensing data and a digital elevation model corresponding to an urban and rural planning survey area, and performing survey area terrain pre-detection to generate an urban and rural planning complex terrain survey area plot; the method comprises the following steps: carrying out element semantic recognition and segmentation on an urban and rural planning complex terrain plot to obtain terrain obstacle distribution corresponding to each building, vegetation and water body element in the urban and rural planning complex terrain plot; obtaining a corresponding three-dimensional surveying and mapping boundary constraint of the complex terrain land parcel and carrying out surveying and mapping path obstacle planning to generate a terrain constraint surveying and mapping planning path corresponding to the urban and rural planning complex terrain land parcel; and performing automatic navigation surveying and mapping processing on the urban and rural planning complex terrain plot to generate urban and rural planning complex terrain plot surveying and mapping data. According to the invention, the efficient, accurate and automatic navigation surveying and mapping process of the plot under the complex terrain condition can be realized.

Description

Technical Field

[0001] The present invention relates to the field of topographic surveying technology, and in particular to an automated surveying and mapping method and system for complex terrain blocks in urban and rural planning. Background Art

[0002] Remote sensing, drone-based aerial photography, and laser radar (LiDAR) technology have been widely used in the surveying and mapping of complex terrain. These technologies can efficiently collect large amounts of spatial data and possess strong adaptability and flexibility. Especially in complex terrain environments, by combining multi-source remote sensing data, drone-based aerial photography, and LiDAR technology, and through intelligent data processing and analysis algorithms, they can achieve rapid and accurate mapping of complex terrain plots. By automatically identifying important elements such as terrain features, plot boundaries, and buildings, and leveraging efficient data fusion and 3D modeling techniques, they significantly improve data processing efficiency and reduce the need for human intervention while ensuring mapping accuracy. However, traditional plot mapping methods typically rely on manual operation or traditional surveying instruments. These methods have many limitations in complex terrain mapping, especially in terrain conditions such as mountains, hills, rivers, and densely built-up areas. Due to the complex terrain and large amount of data, processing this data still requires extensive manual intervention and complex calculations, making it impossible to fully automate and thus unable to complete the mapping task quickly and accurately. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a method and system for automated mapping of complex terrain plots for urban and rural planning to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a method for automated surveying and mapping of complex terrain plots for urban and rural planning is provided, comprising the following steps:

[0005] Step S1: obtaining satellite remote sensing data and digital elevation models corresponding to the urban and rural planning survey area, and performing terrain pre-detection of the survey area based on the satellite remote sensing data and digital elevation models corresponding to the urban and rural planning survey area to generate plots of complex terrain survey areas for urban and rural planning;

[0006] Step S2: Obtain corresponding 3D point cloud data of complex terrain blocks through the urban and rural planning complex terrain survey area and construct a 3D model of the complex terrain blocks in the urban and rural planning; perform element semantic recognition and segmentation on the 3D model of the complex terrain blocks in the urban and rural planning to obtain the terrain obstacle distribution corresponding to each building, vegetation and water element in the complex terrain blocks in the urban and rural planning;

[0007] Step S3: Obtaining the corresponding 3D mapping boundary constraints of the complex terrain plots from the complex terrain survey area plots in the urban and rural planning, and performing mapping path obstacle planning for the complex terrain survey area plots in the urban and rural planning based on the terrain obstacle distribution corresponding to each building, vegetation, and water element within the complex terrain plots in the urban and rural planning, combined with the 3D mapping boundary constraints of the complex terrain plots, to generate a terrain-constrained mapping planning path corresponding to the complex terrain plots in the urban and rural planning;

[0008] Step S4: performing automated navigation and mapping processing on the urban and rural planning complex terrain survey area block based on the terrain constraint surveying and mapping planning path corresponding to the urban and rural planning complex terrain block to generate urban and rural planning complex terrain block surveying and mapping data.

[0009] Furthermore, step S1 includes the following steps:

[0010] Step S11: Acquire satellite remote sensing data corresponding to the urban and rural planning survey area;

[0011] Step S12: obtaining a digital elevation model corresponding to the urban and rural planning survey area;

[0012] Step S13: Obtaining the topographic distribution, building density, and traffic network layout corresponding to the urban and rural planning survey area;

[0013] Step S14: Based on the digital elevation model corresponding to the urban and rural planning survey area and in combination with the corresponding topographic distribution, building density, and transportation network layout, the satellite remote sensing data corresponding to the urban and rural planning survey area is subjected to spatial terrain fusion modeling to generate a spatial terrain distribution fusion model corresponding to the urban and rural planning survey area;

[0014] Step S15: Preliminary terrain detection of the urban and rural planning survey area is performed based on the spatial terrain distribution fusion model corresponding to the urban and rural planning survey area to generate a plot of complex terrain survey area for the urban and rural planning survey area.

[0015] Furthermore, step S15 includes the following steps:

[0016] Step S151: Divide the spatial terrain distribution fusion model corresponding to the urban and rural planning survey area into terrain blocks according to a preset block size to generate terrain distribution sub-blocks for each urban and rural planning survey area;

[0017] Step S152: performing terrain distribution gradient statistics on each of the urban and rural planning survey area terrain distribution sub-blocks to obtain the terrain distribution gradient corresponding to each of the urban and rural planning survey area sub-blocks;

[0018] Step S153: Obtaining the building distribution density and pipeline network distribution density corresponding to each urban and rural planning survey area sub-block through each urban and rural planning survey area terrain distribution sub-block, and estimating the terrain distribution density of the corresponding urban and rural planning survey area terrain distribution sub-block based on the building distribution density and pipeline network distribution density corresponding to each urban and rural planning survey area sub-block, so as to obtain the terrain distribution density corresponding to each urban and rural planning survey area sub-block;

[0019] Step S154: performing terrain complexity assessment based on the terrain distribution gradient and terrain distribution density corresponding to each urban and rural planning survey area sub-block to obtain the terrain distribution complexity corresponding to each urban and rural planning survey area sub-block;

[0020] Step S155: Preliminary terrain detection of the urban and rural planning survey area is performed based on the terrain distribution complexity corresponding to each urban and rural planning survey area sub-block, so as to compare and judge the terrain distribution complexity corresponding to the urban and rural planning survey area sub-block according to a preset terrain complexity threshold, and mark the urban and rural planning survey area sub-blocks corresponding to terrain distribution complexity greater than or equal to the preset terrain complexity threshold as terrain complex sub-blocks, and aggregate the surrounding terrain complex sub-blocks corresponding to the corresponding terrain complex sub-blocks together and use morphological operations to fuse them to generate corresponding regional plots, thereby generating urban and rural planning complex terrain survey area plots.

[0021] Furthermore, step S2 includes the following steps:

[0022] Step S21: 3D laser scanning including UAV-mounted LiDAR, ground mobile measurement system and fixed monitoring station is used to measure the complex terrain survey area of ​​urban and rural planning to obtain corresponding 3D point cloud data of the complex terrain plot;

[0023] Step S22: performing a combination of straight-through filtering and statistical filtering to remove noise from the three-dimensional point cloud data of the complex terrain block, so as to obtain denoised point cloud data of the complex terrain block;

[0024] Step S23: performing a three-dimensional spatial coordinate system conversion on the denoised point cloud data of the complex terrain block to generate corresponding spatial point cloud data of the complex terrain block in the three-dimensional spatial coordinate system;

[0025] Step S24: performing three-dimensional spatial modeling of the complex terrain plot for urban and rural planning based on the corresponding complex terrain plot spatial point cloud data in the three-dimensional spatial coordinate system to generate a three-dimensional model of the complex terrain plot for urban and rural planning;

[0026] Step S25: performing element semantic recognition and segmentation on the three-dimensional model of the complex terrain block in urban and rural planning, and obtaining the terrain obstacle distribution corresponding to each building, vegetation and water element in the complex terrain block in urban and rural planning.

[0027] Furthermore, step S25 includes the following steps:

[0028] Step S251: Obtaining the outline, material characteristics, and spatial layout of the corresponding building from the urban and rural planning complex terrain survey area, and performing building element semantic segmentation on the building area within the three-dimensional model of the urban and rural planning complex terrain area based on the building outline, material characteristics, and spatial layout combined with geometric morphology and structural analysis to obtain each building area element within the urban and rural planning complex terrain area;

[0029] Step S252: Obtaining the greening degree and growth space characteristics of corresponding vegetation from the plots of complex terrain in urban and rural planning, and performing semantic segmentation of vegetation elements within the 3D model of the plots of complex terrain in urban and rural planning based on the greening degree and growth space characteristics of the vegetation, thereby evaluating the volume and distribution density of the vegetation elements in the 3D space to determine the corresponding vegetation element distribution area, and obtaining various vegetation area elements within the plots of complex terrain in urban and rural planning;

[0030] Step S253: Obtaining the distribution area of ​​the corresponding water body through the urban and rural planning complex terrain survey area block, and performing water body element semantic segmentation on the water body area within the three-dimensional model of the urban and rural planning complex terrain block based on the distribution area of ​​the water body, so as to obtain each water body area element within the urban and rural planning complex terrain block;

[0031] Step S254: Based on the terrain distribution range restrictions corresponding to each building area element, vegetation area element and water area element in the complex terrain block of urban and rural planning, the element obstacle distribution three-dimensional model of the complex terrain block of urban and rural planning is analyzed to obtain the terrain obstacle distribution corresponding to each building, vegetation and water element in the complex terrain block of urban and rural planning.

[0032] Furthermore, step S3 includes the following steps:

[0033] Step S31: obtaining the corresponding complex terrain plot slope size through the complex terrain survey area of ​​urban and rural planning;

[0034] Step S32: analyzing the elevation mutation range of the complex terrain survey area in urban and rural planning to obtain the elevation mutation boundary range of the complex terrain block;

[0035] Step S33: performing a surveying boundary constraint analysis on the complex terrain survey area plots in urban and rural planning based on the slope size of the complex terrain plots and the boundary range of the complex terrain plots with sudden elevation changes, so as to generate a three-dimensional surveying boundary constraint for the complex terrain plots;

[0036] Step S34: Based on the terrain obstacle distribution corresponding to each building, vegetation and water element in the complex terrain block of urban and rural planning and the three-dimensional mapping boundary constraints of the complex terrain block, the mapping path obstacle planning is performed on the complex terrain survey area block of urban and rural planning, and the terrain constraint mapping planning path corresponding to the complex terrain block of urban and rural planning is generated.

[0037] Furthermore, step S32 includes the following steps:

[0038] By dividing the boundary area corresponding to the complex terrain survey area of ​​urban and rural planning into equally spaced elevation profile lines, the equally spaced elevation profile lines of the boundaries of each complex terrain plot are generated;

[0039] Based on the equidistant elevation profile lines of the boundaries of each complex terrain block, the urban and rural planning complex terrain survey area blocks are divided into equidistant elevation sub-blocks to obtain the equidistant elevation boundary sub-blocks of each complex terrain;

[0040] Based on the equidistant elevation boundary sub-blocks of each complex terrain, the elevation mutation range of the complex terrain survey area of ​​urban and rural planning is analyzed to obtain the elevation mutation boundary range of the complex terrain block.

[0041] Furthermore, the elevation mutation range analysis of the complex terrain survey area plots for urban and rural planning based on each complex terrain equidistant elevation boundary sub-block includes the following steps:

[0042] By determining the elevation distribution corresponding to each terrain distribution point in the complex terrain equidistant elevation boundary sub-block, and calculating the local elevation curvature of each terrain distribution point in the corresponding complex terrain equidistant elevation boundary sub-block based on the elevation distribution corresponding to each terrain distribution point, the local elevation distribution curvature corresponding to each terrain distribution point in the complex terrain equidistant elevation boundary sub-block is obtained;

[0043] The elevation mutation points are screened based on the local elevation distribution curvature corresponding to each terrain distribution point in the complex terrain equidistant elevation boundary sub-block. If the local elevation distribution curvature corresponding to a terrain distribution point is greater than or equal to the mean of the local elevation distribution curvature in the 8 neighborhoods around the terrain distribution point, the elevation mutation point is selected. When it reaches 2 times, it is determined as the elevation distribution mutation point within the complex terrain equidistant elevation boundary sub-block, and the process continues until all terrain distribution points are determined, thereby obtaining the elevation distribution mutation points corresponding to each complex terrain equidistant elevation boundary sub-block;

[0044] According to the elevation distribution mutation points corresponding to each complex terrain equidistant elevation boundary sub-block, the corresponding complex terrain elevation distribution mutation boundary line is generated, and based on the complex terrain elevation distribution mutation boundary line, the elevation mutation range of the urban and rural planning complex terrain survey area plots is analyzed to obtain the elevation mutation boundary range of the complex terrain plots.

[0045] Furthermore, step S34 includes the following steps:

[0046] Step S341: performing spatial obstacle perception analysis based on the terrain obstacle distribution corresponding to each building, vegetation, and water element within the complex terrain of the urban and rural planning area to generate a terrain spatial obstacle perception distribution map corresponding to the complex terrain of the urban and rural planning area;

[0047] Step S342: Based on the terrain spatial obstacle perception distribution map corresponding to the complex terrain parcel in urban and rural planning, combined with the boundary constraints of the three-dimensional mapping of the complex terrain parcel, terrain constraint superposition division is performed, so as to combine the distribution of buildings, vegetation, and water obstacles within the complex terrain parcel in urban and rural planning with the boundary constraints to delineate corresponding accessible areas and inaccessible areas, thereby generating a terrain constraint access division area corresponding to the complex terrain parcel in urban and rural planning;

[0048] Step S343: Based on the accessible area within the terrain-constrained access demarcation area corresponding to the complex terrain plot in urban and rural planning, a surveying and mapping path access planning analysis is performed on the complex terrain survey area plot in urban and rural planning, so as to fully consider the impact of obstacles corresponding to buildings, vegetation and water bodies and terrain constraints, calculate the shortest distance, minimum time consumption and minimum energy consumption of the corresponding surveying and mapping path, and determine the optimal surveying and mapping path based on the shortest distance, minimum time consumption and minimum energy consumption of the surveying and mapping path, and generate the terrain-constrained surveying and mapping planning path corresponding to the complex terrain plot in urban and rural planning.

[0049] Furthermore, the present invention also provides an automated mapping system for complex terrain plots in urban and rural planning, which is used to execute the automated mapping method for complex terrain plots in urban and rural planning as described above. The automated mapping system for complex terrain plots in urban and rural planning includes:

[0050] The complex terrain pre-detection module is used to obtain satellite remote sensing data and digital elevation models corresponding to the urban and rural planning survey area, and perform terrain pre-detection of the survey area based on the satellite remote sensing data and digital elevation models corresponding to the urban and rural planning survey area, thereby generating plots of complex terrain survey areas for urban and rural planning;

[0051] The feature terrain obstacle analysis module is used to obtain the corresponding complex terrain block 3D point cloud data from the urban and rural planning complex terrain survey area and construct the 3D model of the urban and rural planning complex terrain block; perform feature semantic recognition and segmentation on the 3D model of the urban and rural planning complex terrain block, so as to obtain the terrain obstacle distribution corresponding to each building, vegetation and water element in the urban and rural planning complex terrain block;

[0052] The mapping path obstacle planning module is used to obtain the corresponding three-dimensional mapping boundary constraints of the complex terrain plots in the urban and rural planning complex terrain survey area through the plots. Based on the distribution of terrain obstacles corresponding to each building, vegetation and water element in the complex terrain plots in the urban and rural planning complex terrain, combined with the three-dimensional mapping boundary constraints of the complex terrain plots, the mapping path obstacle planning is performed for the plots in the urban and rural planning complex terrain survey area, thereby generating a terrain-constrained mapping planning path corresponding to the complex terrain plots in the urban and rural planning;

[0053] The automated navigation and mapping module is used to perform automated navigation and mapping processing on the complex terrain survey area blocks in urban and rural planning based on the terrain constraint surveying and mapping planning path corresponding to the complex terrain blocks in urban and rural planning, so as to generate surveying and mapping data for the complex terrain blocks in urban and rural planning.

[0054] Beneficial effects of the present invention:

[0055] 1. The automated mapping method for complex terrain plots in urban and rural planning proposed in the present invention has the beneficial effect of obtaining satellite remote sensing data and digital elevation models (DEMs) corresponding to the urban and rural planning survey area and combining these data to perform preliminary terrain detection in the survey area. Satellite remote sensing data can provide a wide perspective for the survey area and can quickly and accurately obtain surface information, thereby helping planners fully understand the topographic characteristics of the survey area. The digital elevation model can accurately depict the undulations and changing trends of the terrain by providing detailed information on elevation changes. By combining these two types of data, the detection of complex terrain survey areas in urban and rural planning can be preliminarily completed. Especially under complex terrain conditions, it can better identify and understand terrain features such as built-up areas, mountains, rivers, and hills in the survey area, providing more accurate terrain data and predictions. Through early terrain analysis, difficulties in the survey area, such as areas that are difficult to build or plots requiring special treatment, can be discovered, thereby providing data basis for feasibility assessment and obstacle analysis of subsequent planning. Secondly, by obtaining three-dimensional point cloud data of complex terrain survey areas in urban and rural planning and constructing three-dimensional models, the recognition accuracy and precision of terrain and elements can be greatly improved. As a sophisticated spatial data representation method, three-dimensional point cloud data can comprehensively cover the spatial distribution of elements such as buildings, vegetation, and water bodies within the plot, and can accurately reflect the three-dimensional structure of the terrain. By processing these point cloud data, a more detailed terrain model can be obtained, providing strong support for design decisions in urban and rural planning. In the element semantic recognition and segmentation stage, advanced semantic segmentation technology is used to classify and identify buildings, vegetation, and water bodies, which can clearly divide the various elements in complex terrain, facilitating subsequent analysis and processing. In this way, it can provide an accurate three-dimensional terrain model and provide planners with more three-dimensional plot data. In addition, through the semantic recognition of elements within the plot, terrain obstacles that affect planning and design can be accurately identified, providing detailed obstacle information for subsequent path planning and surveying and mapping processing, which helps to design more reasonable and efficient planning schemes. Then, by combining the 3D model of the complex terrain plots in urban and rural planning with terrain obstacle information, obstacle planning for the surveying and mapping path is performed. This allows the design of an optimized surveying and mapping path. This path not only avoids obstructed areas but also completes the surveying and mapping work more efficiently, reducing unnecessary duplication of work and wasted time. Through precise obstacle planning, planners can avoid dangerous or difficult-to-measure areas in complex terrain during the surveying and mapping process, improving the accuracy and efficiency of surveying and mapping. This ensures the smooth progress of surveying and mapping work and avoids surveying difficulties caused by terrain obstacles. On the other hand, by optimizing the path design, not only work efficiency is improved, but also the surveying and mapping tasks can be completed quickly and accurately. Finally, by performing automated navigation and mapping processing based on the previously obtained data and the planned path, the surveying and mapping tasks can be automatically performed by machines, greatly improving work efficiency and reducing errors and uncertainties in human operations.In areas with complex terrain, traditional manual surveying often requires a lot of time and effort, while automated technology can complete surveying tasks quickly and efficiently based on pre-planned paths and obstacle information. This not only improves the accuracy of surveying, but can also adapt to various complex terrains. Especially in difficult-to-reach or dangerous areas, automated surveying can replace manual work to ensure the safety and efficiency of the surveying process. This can significantly improve the efficiency and quality of surveying operations, ensure the accuracy of terrain data, and thus provide accurate data support for large-scale urban and rural planning projects.

[0056] 2. The automated surveying and mapping system for complex terrain plots in urban and rural planning proposed in the present invention is generally composed of a complex terrain pre-detection module, an element terrain obstacle analysis module, a surveying and mapping path obstacle planning module, and an automated navigation and surveying module. It can realize the automated surveying and mapping method for complex terrain plots in any urban and rural planning described in the present invention, and is used to combine the operations between computer programs running on each module to realize the automated surveying and mapping method for complex terrain plots in urban and rural planning. The internal structures of the system cooperate with each other, which can greatly reduce repetitive work and manpower investment, and can quickly and effectively provide a more accurate and efficient automated surveying and mapping process for complex terrain plots in urban and rural planning, thereby simplifying the operating procedures of the automated surveying and mapping system for complex terrain plots in urban and rural planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0058] Figure 1 A schematic flow chart of the steps of the automated surveying and mapping method for complex terrain plots in urban and rural planning according to the present invention;

[0059] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0060] Figure 3 for Figure 2 Detailed step flow chart of step S15. DETAILED DESCRIPTION

[0061] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0062] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0063] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0064] To achieve this, please refer to Figures 1 to 3 The present invention provides an automated mapping method for complex terrain plots in urban and rural planning, the method comprising the following steps:

[0065] Step S1: obtaining satellite remote sensing data and digital elevation models corresponding to the urban and rural planning survey area, and performing terrain pre-detection of the survey area based on the satellite remote sensing data and digital elevation models corresponding to the urban and rural planning survey area to generate plots of complex terrain survey areas for urban and rural planning;

[0066] Step S2: Obtain corresponding 3D point cloud data of complex terrain blocks through the urban and rural planning complex terrain survey area and construct a 3D model of the complex terrain blocks in the urban and rural planning; perform element semantic recognition and segmentation on the 3D model of the complex terrain blocks in the urban and rural planning to obtain the terrain obstacle distribution corresponding to each building, vegetation and water element in the complex terrain blocks in the urban and rural planning;

[0067] Step S3: Obtaining the corresponding 3D mapping boundary constraints of the complex terrain plots from the complex terrain survey area plots in the urban and rural planning, and performing mapping path obstacle planning for the complex terrain survey area plots in the urban and rural planning based on the terrain obstacle distribution corresponding to each building, vegetation, and water element within the complex terrain plots in the urban and rural planning, combined with the 3D mapping boundary constraints of the complex terrain plots, to generate a terrain-constrained mapping planning path corresponding to the complex terrain plots in the urban and rural planning;

[0068] Step S4: performing automated navigation and mapping processing on the urban and rural planning complex terrain survey area block based on the terrain constraint surveying and mapping planning path corresponding to the urban and rural planning complex terrain block to generate urban and rural planning complex terrain block surveying and mapping data.

[0069] In the embodiment of the present invention, please refer to Figure 1 FIG. 1 is a flow chart showing the steps of the automatic mapping method for complex terrain plots in urban and rural planning according to the present invention. In this example, the automatic mapping method for complex terrain plots in urban and rural planning includes the following steps:

[0070] Step S1: obtaining satellite remote sensing data and digital elevation models corresponding to the urban and rural planning survey area, and performing terrain pre-detection of the survey area based on the satellite remote sensing data and digital elevation models corresponding to the urban and rural planning survey area to generate plots of complex terrain survey areas for urban and rural planning;

[0071] In an embodiment of the present invention, a Gaofen-2 satellite with a resolution of 0.5 meters is selected to conduct transit photography of an urban and rural planning survey area of ​​15 square kilometers to obtain multispectral satellite remote sensing data, including four bands of blue, green, red and near-infrared. At the same time, an airborne laser radar (LiDAR) system is used to obtain a digital elevation model. The UAV scans along a predetermined route at a flight altitude of 150 meters and a speed of 5 meters per second. The laser radar emits a laser beam at a pulse frequency of 100kHz. After denoising and TIN interpolation algorithm processing, a digital elevation model with a resolution of 1 meter × 1 meter is generated. The satellite remote sensing data and the digital elevation model are imported into a geographic information system (GIS). The remote sensing image is geometrically corrected using ENVI software. With the digital elevation model as a reference, a quadratic polynomial transformation is used, and the error is controlled within 0.5 pixels. The two are superimposed using ArcGIS software, and areas with a terrain slope greater than 25° and an elevation standard deviation greater than 8 meters are set as complex terrain. For example, the mountains in the northeast of the survey area have a slope of 30°-45° and drastic changes in elevation, so they are designated as a complex terrain survey area for urban and rural planning, covering an area of ​​approximately 3 square kilometers.

[0072] Step S2: Obtain corresponding 3D point cloud data of complex terrain blocks through the urban and rural planning complex terrain survey area and construct a 3D model of the complex terrain blocks in the urban and rural planning; perform element semantic recognition and segmentation on the 3D model of the complex terrain blocks in the urban and rural planning to obtain the terrain obstacle distribution corresponding to each building, vegetation and water element in the complex terrain blocks in the urban and rural planning;

[0073] In an embodiment of the present invention, a 3-square-kilometer complex terrain survey area is measured by using drone-mounted LiDAR, a ground mobile measurement system, and a 3D laser scanning device at a fixed monitoring station. The drone-mounted LiDAR scans at a pulse frequency of 100kHz at an altitude of 150 meters; the ground mobile measurement system is installed on an off-road vehicle and collects data along the road at a speed of 20 kilometers per hour; the fixed monitoring station scans once an hour. After the three types of equipment collect data synchronously, they are timestamp aligned and spatially calibrated to obtain 3D point cloud data of the complex terrain plot with a point cloud density of 150 points per square meter. A 3D modeling method based on a triangulated network is adopted, and the point cloud is constructed into an irregular triangulated network using the Delaunay triangulation algorithm. Texture mapping is performed on each triangle facet. The texture information is provided by the images taken by the 4000×3000 pixel camera carried by the drone. The bilinear interpolation algorithm is used for mapping to generate a three-dimensional model of complex terrain plots for urban and rural planning containing 1.2 million triangular facets. The three-dimensional model is then segmented for feature semantics using the PointNet++ network model based on deep learning. The training dataset contains 200 three-dimensional models of similar terrain areas and 1 million annotated points, covering 8 categories. The model inputs the three-dimensional coordinate information of the point cloud and outputs the semantic category label of the point. The probability threshold is set to 0.7 to complete the semantic segmentation of elements such as buildings, vegetation, and water bodies, and finally obtain the distribution of terrain obstacles corresponding to each element. For example, the building complex in the middle of the survey area is marked as a high-obstacle area due to its dense buildings.

[0074] Step S3: Obtaining the corresponding 3D mapping boundary constraints of the complex terrain plots from the complex terrain survey area plots in the urban and rural planning, and performing mapping path obstacle planning for the complex terrain survey area plots in the urban and rural planning based on the terrain obstacle distribution corresponding to each building, vegetation, and water element within the complex terrain plots in the urban and rural planning, combined with the 3D mapping boundary constraints of the complex terrain plots, to generate a terrain-constrained mapping planning path corresponding to the complex terrain plots in the urban and rural planning;

[0075] In the embodiment of the present invention, by calculating the slope of complex terrain plots (based on a digital elevation model with a resolution of 1 meter × 1 meter, using a 3 × 3 grid window and a central difference method) and analyzing the elevation mutation range (dividing the elevation profile line at 50-meter intervals along the plot boundary, dividing the sub-blocks at 10-meter elevation intervals, and screening the mutation points with the average elevation plus 2 times the standard deviation as the threshold), the slope distribution map and the elevation mutation boundary range layer are superimposed in GIS, and the slope greater than 30° and within the elevation mutation boundary are set as a strong constraint area, and the slope of 15°-30° and close to the mutation boundary are set as a weak constraint area. The equipment has performance parameters of a maximum climbing slope of 25° and a minimum turning radius of 3 meters. The surveying and mapping boundaries are refined, and the GIS "boundary generation tool" is used to generate 3D surveying and mapping boundary constraints for complex terrain blocks, which are presented as vector polygons. The terrain obstacle distribution data of buildings, vegetation, and water elements are integrated with the 3D surveying and mapping boundary constraint data on the GIS platform. The terrain of the surveyable area is abstracted into nodes and edges to construct a graph network. The weight between nodes is calculated according to the formula weight = α × distance + β × time consumption + γ × energy consumption, where α = 0.4, β = 0.3, and γ = 0.3. The distance is calculated according to the three-dimensional space straight-line distance (formula ), time consumption is derived from the walking speed of 1.5 m / s and the distance, energy consumption is estimated based on the terrain slope and obstacle detour, and the Dijkstra algorithm is used to search for the minimum weight path from the starting point to the end point to generate a terrain-constrained mapping planning path, such as a detour route avoiding the steep slopes and buildings in the western part of the survey area.

[0076] Step S4: performing automated navigation and mapping processing on the urban and rural planning complex terrain survey area block based on the terrain constraint surveying and mapping planning path corresponding to the urban and rural planning complex terrain block to generate urban and rural planning complex terrain block surveying and mapping data.

[0077] In an embodiment of the present invention, a terrain-constrained mapping plan path is imported into the navigation system of an automated mapping device equipped with a high-precision GNSS receiver, an inertial measurement unit (IMU), and a lidar. During the mapping process, the GNSS receiver and IMU are combined for positioning, acquiring the device's three-dimensional coordinates and attitude information in real time with centimeter-level positioning accuracy. The lidar scans at an 80kHz frequency, matching the scanned data with a pre-built three-dimensional model in real time. The device's position and attitude are adjusted using an iterative closest point (ICP) algorithm to ensure accurate execution of the mapping path. For example, when passing through a densely vegetated area on the planned path, the lidar detects a vegetation obstacle in real time. The device automatically adjusts the path based on a preset obstacle avoidance strategy while maintaining mapping accuracy, continuing to complete the mapping task. After the mapping is completed, the collected terrain, building, vegetation, water body, and other data are integrated and processed through denoising and coordinate conversion to generate complex terrain plot mapping data for urban and rural planning, including terrain elevation, feature attributes, and other information. The data is stored in a GeoPackage format that complies with GIS standards, providing detailed information for urban and rural planning design.

[0078] Furthermore, step S1 includes the following steps:

[0079] Step S11: Acquire satellite remote sensing data corresponding to the urban and rural planning survey area;

[0080] Step S12: obtaining a digital elevation model corresponding to the urban and rural planning survey area;

[0081] Step S13: Obtaining the topographic distribution, building density, and traffic network layout corresponding to the urban and rural planning survey area;

[0082] Step S14: Based on the digital elevation model corresponding to the urban and rural planning survey area and in combination with the corresponding topographic distribution, building density, and transportation network layout, the satellite remote sensing data corresponding to the urban and rural planning survey area is subjected to spatial terrain fusion modeling to generate a spatial terrain distribution fusion model corresponding to the urban and rural planning survey area;

[0083] Step S15: Preliminary terrain detection of the urban and rural planning survey area is performed based on the spatial terrain distribution fusion model corresponding to the urban and rural planning survey area to generate a plot of complex terrain survey area for the urban and rural planning survey area.

[0084] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in FIG. 1 , in this embodiment, step S1 includes the following steps:

[0085] Step S11: Acquire satellite remote sensing data corresponding to the urban and rural planning survey area;

[0086] In an embodiment of the present invention, a high-resolution satellite remote sensing system is used to obtain satellite remote sensing data corresponding to the urban and rural planning survey area. A commercial remote sensing satellite with a resolution of 0.5 meters, such as the Gaofen-2 satellite, is selected to conduct transit photography of the urban and rural planning survey area with an area of ​​20 square kilometers. The satellite operates in a specific orbit and collects electromagnetic wave information reflected by the surface of the survey area through an optical imaging sensor to form multispectral image data covering four bands: blue, green, red, and near-infrared. In the data reception link, the ground receiving station receives the signal sent by the satellite in the X-band (8-12GHz) frequency band, and after demodulation and decoding processing, the original signal is converted into digital image data and stored in GeoTIFF format. For example, in a certain data acquisition, the receiving station obtains image data containing clear ground object information such as buildings, roads, and vegetation in the survey area. These data become the basic visual data for subsequent surveying and mapping work.

[0087] Step S12: obtaining a digital elevation model corresponding to the urban and rural planning survey area;

[0088] In an embodiment of the present invention, an airborne laser radar (LiDAR) system is used to obtain a digital elevation model (DEM) corresponding to an urban and rural planning survey area. A drone equipped with the LiDAR device scans the survey area at a predetermined flight path at an altitude of 150 meters and a speed of 5 meters per second. The LiDAR emits a laser beam at the ground at a pulse frequency of 100kHz. By measuring the time difference between the laser emission and its reflection back to the sensor, combined with known flight parameters, the three-dimensional coordinates (X, Y, Z) of the ground point are calculated. The collected point cloud data is denoised to remove noise caused by swaying vegetation, flying birds, etc., and then a TIN (triangulated irregular network) interpolation algorithm is used to convert the discrete point cloud data into a regular grid digital elevation model with a resolution of 1 meter by 1 meter. For example, in a mountainous and hilly area within the survey area, this method can accurately obtain the elevation information of the undulating terrain and construct a digital elevation model that reflects the actual terrain and landforms.

[0089] Step S13: Obtaining the topographic distribution, building density, and traffic network layout corresponding to the urban and rural planning survey area;

[0090] In an embodiment of the present invention, the topographic distribution, building density, and transportation network layout corresponding to the urban and rural planning survey area are obtained from a geographic information system (GIS) database and special survey results. The topographic distribution data is obtained by digitizing geological survey data and historical topographic mapping results, stored as vector data, and contains boundary and attribute information for different landform types such as mountains, plains, and water bodies. Building density information is calculated from building survey data from the urban planning department. The ratio of the building footprint within each standard grid (50 meters x 50 meters) to the total grid area is calculated to form a building density thematic layer. Traffic network layout data is derived from pipeline network survey data from the municipal department. ArcGIS software is used to digitize information such as the direction, diameter, and burial depth of various pipeline networks (such as water supply, drainage, gas, and power pipelines) to construct a three-dimensional pipeline network model. For example, in a certain urban survey area, the commercial district's building density reached 60%, as well as the intricate underground pipeline network distribution data, providing comprehensive geographic information for subsequent modeling.

[0091] Step S14: Based on the digital elevation model corresponding to the urban and rural planning survey area and in combination with the corresponding topographic distribution, building density, and transportation network layout, the satellite remote sensing data corresponding to the urban and rural planning survey area is subjected to spatial terrain fusion modeling to generate a spatial terrain distribution fusion model corresponding to the urban and rural planning survey area;

[0092] In an embodiment of the present invention, based on the digital elevation model corresponding to the urban and rural planning survey area, combined with the topographic distribution, building density and traffic pipe network layout, ENVI and ArcGIS software are used to perform spatial terrain fusion modeling on the satellite remote sensing data. First, the satellite remote sensing image is geometrically corrected in ENVI, and with the digital elevation model as a reference, the quadratic polynomial transformation method is used to accurately match the image coordinates with the actual geographic coordinates, and the error is controlled within 0.5 pixels. Then, in ArcGIS, the topographic vector data, the building density thematic layer, the traffic pipe network three-dimensional model and the corrected satellite remote sensing image are spatially superimposed, and the raster calculation tool is used to assign the elevation value of the digital elevation model to each pixel of the remote sensing image, and at the same time, the attribute data such as building density and pipe network information are associated with the corresponding geographic space position. Through the data fusion algorithm, the multi-source data is integrated into a unified coordinate system (CGCS2000) and a spatial terrain distribution fusion model with a resolution of 0.5 meters. For example, during the fusion process, the model clearly presents the distribution of buildings and pipeline laying on a mountainous terrain within the survey area, realizing the organic fusion of multi-source data.

[0093] Step S15: Preliminary terrain detection of the urban and rural planning survey area is performed based on the spatial terrain distribution fusion model corresponding to the urban and rural planning survey area to generate a plot of complex terrain survey area for the urban and rural planning survey area.

[0094] In the embodiment of the present invention, based on the generated spatial terrain distribution fusion model corresponding to the urban and rural planning survey area, the terrain pre-detection of the urban and rural planning survey area is carried out according to the preset process. First, the fusion model is gridded with 100 m × 100 m units to obtain 10,000 terrain distribution sub-blocks. Then, terrain distribution gradient statistics are performed on each sub-block, and the slope of each 0.5 m × 0.5 m grid point in the sub-block is calculated (formula: h1 and h2 are the elevations of adjacent grid points, and the average value is taken as the sub-block terrain gradient. Simultaneously, the building density (building area within the sub-block / total sub-block area) and pipeline network density (total pipeline network length within the sub-block / total sub-block area) of the sub-block are calculated. The terrain density is estimated using the formula: terrain density = 0.6 × building density + 0.4 × pipeline network density. Terrain complexity is then assessed using a comprehensive scoring model (terrain complexity = 0.7 × complexity score corresponding to the terrain gradient + 0.3 × complexity score corresponding to the terrain density). The gradient score and density score are each categorized into five levels, with a terrain complexity threshold of 3 points. Sub-blocks with a complexity score of 3 or greater are labeled as complex. Finally, morphological dilation and erosion operations are used to aggregate adjacent complex sub-blocks, removing redundant connections, ultimately generating complex terrain survey areas for urban and rural planning. For example, within the survey area, complex terrain plots such as densely built-up mountainous areas and complex pipeline network intersections were successfully identified, clarifying key areas for subsequent surveying and mapping.

[0095] Furthermore, step S15 includes the following steps:

[0096] Step S151: Divide the spatial terrain distribution fusion model corresponding to the urban and rural planning survey area into terrain blocks according to a preset block size to generate terrain distribution sub-blocks for each urban and rural planning survey area;

[0097] Step S152: performing terrain distribution gradient statistics on each of the urban and rural planning survey area terrain distribution sub-blocks to obtain the terrain distribution gradient corresponding to each of the urban and rural planning survey area sub-blocks;

[0098] Step S153: Obtaining the building distribution density and pipeline network distribution density corresponding to each urban and rural planning survey area sub-block through each urban and rural planning survey area terrain distribution sub-block, and estimating the terrain distribution density of the corresponding urban and rural planning survey area terrain distribution sub-block based on the building distribution density and pipeline network distribution density corresponding to each urban and rural planning survey area sub-block, so as to obtain the terrain distribution density corresponding to each urban and rural planning survey area sub-block;

[0099] Step S154: performing terrain complexity assessment based on the terrain distribution gradient and terrain distribution density corresponding to each urban and rural planning survey area sub-block to obtain the terrain distribution complexity corresponding to each urban and rural planning survey area sub-block;

[0100] Step S155: Preliminary terrain detection of the urban and rural planning survey area is performed based on the terrain distribution complexity corresponding to each urban and rural planning survey area sub-block, so as to compare and judge the terrain distribution complexity corresponding to the urban and rural planning survey area sub-block according to a preset terrain complexity threshold, and mark the urban and rural planning survey area sub-blocks corresponding to terrain distribution complexity greater than or equal to the preset terrain complexity threshold as terrain complex sub-blocks, and aggregate the surrounding terrain complex sub-blocks corresponding to the corresponding terrain complex sub-blocks together and use morphological operations to fuse them to generate corresponding regional plots, thereby generating urban and rural planning complex terrain survey area plots.

[0101] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 Detailed step flow diagram of step S15 in the embodiment, step S15 includes the following steps:

[0102] Step S151: Divide the spatial terrain distribution fusion model corresponding to the urban and rural planning survey area into terrain blocks according to a preset block size to generate terrain distribution sub-blocks for each urban and rural planning survey area;

[0103] In an embodiment of the present invention, a spatial terrain distribution fusion model is divided into blocks of a preset size of 100 meters by 100 meters (i.e., 0.01 square kilometers) within a 10 square kilometer urban and rural planning survey area. The model integrates satellite remote sensing imagery, drone aerial survey point cloud data, and ground elevation measurement data, achieving a spatial resolution of 0.5 meters. Starting from the coordinates (0, 0) in the upper left corner of the survey area, the model is gridded at intervals of 100 meters horizontally and vertically, with 100 grids divided horizontally and 100 grids divided vertically, generating a total of 10,000 sub-blocks of terrain distribution for the urban and rural planning survey area. Each sub-block contains spatial information such as terrain elevation and feature type within the area. For example, the sub-block numbered (20, 30) is primarily hilly with an average altitude of 120 meters and contains a small number of low buildings.

[0104] Step S152: performing terrain distribution gradient statistics on each of the urban and rural planning survey area terrain distribution sub-blocks to obtain the terrain distribution gradient corresponding to each of the urban and rural planning survey area sub-blocks;

[0105] In the embodiment of the present invention, terrain distribution gradient statistics are performed on each previously generated urban and rural planning survey area terrain distribution sub-block, and each 0.5 m × 0.5 m grid point in the sub-block is used as a calculation unit. The gradient is calculated based on the elevation difference of adjacent grid points. Assuming that the elevation of a grid point A in the sub-block is h1, the elevation of its adjacent grid point B is h2, and the horizontal distance between the two points is 0.5 m, according to the slope calculation formula Calculate the slope value, and then calculate the average slope value of all grid points in the sub-block to obtain the terrain distribution gradient corresponding to the sub-block. For example, for the sub-block numbered (15, 25), the average slope value of all grid points within it is calculated to be 15°, that is, the terrain distribution gradient of the sub-block is 15°, indicating that the terrain of the sub-block is relatively steep.

[0106] Step S153: Obtaining the building distribution density and pipeline network distribution density corresponding to each urban and rural planning survey area sub-block through each urban and rural planning survey area terrain distribution sub-block, and estimating the terrain distribution density of the corresponding urban and rural planning survey area terrain distribution sub-block based on the building distribution density and pipeline network distribution density corresponding to each urban and rural planning survey area sub-block, so as to obtain the terrain distribution density corresponding to each urban and rural planning survey area sub-block;

[0107] In the embodiment of the present invention, the building distribution density and pipeline distribution density corresponding to each sub-block of the urban and rural planning survey area are obtained through the geographic information system (GIS) database and the building and pipeline network census data. The building distribution density is calculated as the ratio of the building area in the sub-block to the total area of ​​the sub-block, and the pipeline network distribution density is calculated as the ratio of the total length of the pipeline network in the sub-block to the area of ​​the sub-block. Assuming that the area of ​​a sub-block is 10,000 square meters and the internal building area is 2,000 square meters, the building distribution density is 2,000 / 10,000=0.2; if the sub-block has a building area of ​​10,000 square meters and a building area of ​​2,000 square meters, the building distribution density is 2,000 / 10,000=0.2. The total length of the internal transportation pipeline network is 500 meters, so the pipeline network distribution density is 500 / 10000=0.05 meters / square meter. The terrain distribution density is estimated by weighted summation based on the building distribution density and the pipeline network distribution density. The calculation formula is: terrain distribution density=0.6×building distribution density+0.4×pipeline network distribution density. Substituting the above data, the terrain distribution density of this sub-block is 0.6×0.2+0.4×0.05=0.14, reflecting the complexity of terrain utilization in this sub-block due to the existence of buildings and pipelines.

[0108] Step S154: performing terrain complexity assessment based on the terrain distribution gradient and terrain distribution density corresponding to each urban and rural planning survey area sub-block to obtain the terrain distribution complexity corresponding to each urban and rural planning survey area sub-block;

[0109] In the embodiment of the present invention, the terrain complexity is evaluated according to the terrain distribution gradient and terrain distribution density corresponding to each urban and rural planning survey area sub-block. A comprehensive scoring model is adopted, and the terrain distribution gradient weight is set to 0.7, and the terrain distribution density weight is set to 0.3. The calculation formula is: terrain distribution complexity = 0.7 × complexity level score corresponding to the terrain distribution gradient + 0.3 × complexity level score corresponding to the terrain distribution density, wherein the terrain distribution gradient score is divided into 5 levels according to the gradient value, 0-5° is 1 point, 5-10° is 2 points, 10-15° is 3 points, 15-20° is 4 points, and 10-25° is 5 points. The terrain distribution density score is divided into 5 levels according to the calculation results: 0-0.1 is 1 point, 0.1-0.2 is 2 points, 0.2-0.3 is 3 points, 0.3-0.4 is 4 points, and 0.4 and above is 5 points. For example, the terrain distribution gradient of a sub-block is 12°, corresponding to a score of 3 points, and the terrain distribution density is 0.25, corresponding to a score of 3 points. Then the terrain distribution complexity of the sub-block is 0.7×3+0.3×3=3 points. The terrain complexity of the sub-block is quantitatively evaluated, and finally the terrain distribution complexity corresponding to the sub-blocks of the urban and rural planning survey area is obtained.

[0110] Step S155: Preliminary terrain detection of the urban and rural planning survey area is performed based on the terrain distribution complexity corresponding to each urban and rural planning survey area sub-block, so as to compare and judge the terrain distribution complexity corresponding to the urban and rural planning survey area sub-block according to a preset terrain complexity threshold, and mark the urban and rural planning survey area sub-blocks corresponding to terrain distribution complexity greater than or equal to the preset terrain complexity threshold as terrain complex sub-blocks, and aggregate the surrounding terrain complex sub-blocks corresponding to the corresponding terrain complex sub-blocks together and use morphological operations to fuse them to generate corresponding regional plots, thereby generating urban and rural planning complex terrain survey area plots.

[0111] In an embodiment of the present invention, a terrain complexity threshold of 3 is preset, and the terrain distribution complexity corresponding to each sub-block in the urban and rural planning survey area is compared with the threshold. Sub-blocks with a terrain distribution complexity greater than or equal to 3 are marked as terrain complex sub-blocks. For example, the sub-blocks numbered (30, 40), (30, 41), (31, 40), and (31, 41) in the survey area all have a terrain distribution complexity of 3 or above and are therefore marked as terrain complex sub-blocks. Then, the dilation and erosion algorithms in morphological operations are used to aggregate these adjacent terrain complex sub-blocks. First, a dilation operation is performed to expand the boundary of each terrain complex sub-block outward by one sub-block unit to connect adjacent sub-blocks. Then, an erosion operation is performed to remove the redundant connected parts caused by the dilation. Finally, the corresponding regional plots are generated by fusion, and the urban and rural planning complex terrain survey area plots are obtained. Through this process, the complex terrain areas within the survey area that require key surveying and mapping are clearly defined, providing clear targets for subsequent surveying and mapping work.

[0112] Furthermore, step S2 includes the following steps:

[0113] Step S21: 3D laser scanning including UAV-mounted LiDAR, ground mobile measurement system and fixed monitoring station is used to measure the complex terrain survey area of ​​urban and rural planning to obtain corresponding 3D point cloud data of the complex terrain plot;

[0114] In an embodiment of the present invention, a 5-square-kilometer urban and rural planning complex terrain survey area is measured by utilizing UAV-mounted LiDAR, a ground mobile measurement system, and a three-dimensional laser scanning device at a fixed monitoring station. The UAV-mounted LiDAR device is mounted on a multi-rotor UAV, and the flight altitude is set to 150 meters. The survey area is spirally scanned at a laser emission frequency of 100kHz and a scanning field of view of 15°. In areas such as complex mountainous areas that are difficult for UAVs to reach at close range, the route and altitude are adjusted to ensure complete data coverage. The ground mobile measurement system is installed on an off-road vehicle and is operated at a speed of 20 kilometers per hour. The vehicle travels along the main roads in the survey area at a speed of 100 kilometers per hour. Its equipped lidar collects data on the terrain and land features around the road at a frequency of 80 kHz. Fixed monitoring stations are distributed in the four corners of the survey area, continuously scanning the surrounding area at a frequency of once an hour. Three types of equipment collect data synchronously. After timestamp alignment and spatial calibration, the collected data are fused to finally obtain three-dimensional point cloud data of complex terrain plots with a point cloud density of 150 points / square meter. For example, in an area with a building complex in the survey area, the collaborative collection of multiple devices has completely acquired point cloud information on the building's facade, top surface and surrounding terrain.

[0115] Step S22: performing a combination of straight-through filtering and statistical filtering to remove noise from the three-dimensional point cloud data of the complex terrain block, so as to obtain denoised point cloud data of the complex terrain block;

[0116] In an embodiment of the present invention, denoising is performed on three-dimensional point cloud data of complex terrain plots by combining through-filtering and statistical filtering. First, through-filtering is used. Based on the actual terrain range of the survey area, the X-axis range is set to [1000, 6000] meters, the Y-axis range is set to [2000, 7000] meters, and the Z-axis range is set to [50, 300] meters. Outliers outside these ranges are directly removed, eliminating approximately 10% of invalid points. Next, statistical filtering is performed to calculate the Euclidean distance between each point and its k = 20 neighboring points. Assuming the mean distance of point P is μ and the standard deviation is σ, a distance threshold is set to d_thresh = μ + 2σ. Points with distances greater than the threshold are identified as noise points and deleted. In an area with dense vegetation, statistical filtering effectively removes noise points caused by vegetation shaking, significantly improving the quality of the point cloud data. After denoising, the proportion of noise points in the point cloud data is reduced from the initial 15% to 3%, ultimately obtaining denoised point cloud data for complex terrain plots.

[0117] Step S23: performing a three-dimensional spatial coordinate system conversion on the denoised point cloud data of the complex terrain block to generate corresponding spatial point cloud data of the complex terrain block in the three-dimensional spatial coordinate system;

[0118] In the embodiment of the present invention, the 3D space coordinate system conversion is performed on the denoised point cloud data of the complex terrain. It is known that the original point cloud data uses the local coordinate system of the device and needs to be converted to the national geodetic coordinate system (CGCS2000). First, the coordinate conversion is performed using the ground control points. Eight ground control points are evenly distributed in the survey area, and their coordinates (X, Y, and Z) in the CGCS2000 coordinate system are accurately measured by the total station. CGCS2000 ,Y CGCS2000 ,Z CGCS2000 ), and the coordinates in the local coordinate system (X local ,Y local ,Z local ), a seven-parameter transformation model is used to solve the rotation parameter (ω x ,ω y ,ω z ), translation parameters (ΔX, ΔY, ΔZ) and scale parameter ΔS, where the conversion formula corresponding to the spatial coordinate system transformation is: After calculation and conversion, all point cloud data are accurately converted to the CGCS2000 coordinate system, and finally the corresponding complex terrain spatial point cloud data in the three-dimensional space coordinate system is generated, with the conversion accuracy reaching the centimeter level.

[0119] Step S24: performing three-dimensional spatial modeling of the complex terrain plot for urban and rural planning based on the corresponding complex terrain plot spatial point cloud data in the three-dimensional spatial coordinate system to generate a three-dimensional model of the complex terrain plot for urban and rural planning;

[0120] In an embodiment of the present invention, a three-dimensional modeling method based on a triangulated network is used to perform three-dimensional spatial modeling of a complex terrain survey area plot in urban and rural planning based on spatial point cloud data of a complex terrain plot corresponding to a three-dimensional spatial coordinate system. First, the point cloud data is constructed into an irregular triangulated network (TIN) using a Delaunay triangulation algorithm to ensure that each triangle of the triangulated network meets the empty circumcircle criterion and the quality of the triangulated network. For each triangle facet, texture mapping is performed based on the normal vector and texture information of its vertex. The texture information is obtained by synchronously shooting images with a high-resolution camera carried by a drone. The image resolution is 4000×3000 pixels. A bilinear interpolation algorithm is used to accurately map the image texture to the triangulated network surface. During the modeling process, for areas with large terrain undulations, the density of the triangulated network is increased to improve the detail performance of the model; for flat areas, the triangulated network is appropriately simplified to reduce the amount of data. Finally, a three-dimensional model of the complex terrain plot in urban and rural planning containing 1 million triangulated facets is generated. The model can truly restore the three-dimensional form of the terrain and landforms in the survey area.

[0121] Step S25: performing element semantic recognition and segmentation on the three-dimensional model of the complex terrain block in urban and rural planning, and obtaining the terrain obstacle distribution corresponding to each building, vegetation and water element in the complex terrain block in urban and rural planning.

[0122] In an embodiment of the present invention, the semantic recognition and segmentation of elements of the three-dimensional model of the complex terrain of urban and rural planning is performed, and a PointNet++ network model based on deep learning is adopted. The input of the model is the three-dimensional coordinate information of the point cloud data, and the output is the semantic category label of each point. The training data set contains 200 three-dimensional models from similar urban and rural terrain areas, with a total of 1 million points marked, covering 8 categories such as buildings, vegetation, and water bodies. The point cloud data of the generated three-dimensional model of the complex terrain of urban and rural planning is input into the trained PointNet++ model. The model extracts the local and global features of the point cloud through a multi-layer perceptron (MLP) and a symmetric function, and outputs the probability of each point belonging to a different category through the classification layer. The probability threshold is set to 0.7, and the points with a probability greater than The points above the threshold are determined to be of the corresponding category, and the semantic segmentation of elements such as buildings, vegetation, and water bodies is completed. After the segmentation is completed, the terrain obstacle distribution analysis is performed on each element based on the spatial distribution of each element. For buildings, the spatial range they occupy and the height difference with the surrounding terrain are calculated. If the height difference is greater than 2 meters and the horizontal distance is less than 5 meters, it is determined to be a terrain obstacle; for vegetation, according to its height and density, when the height of the tree exceeds 10 meters and the crown coverage area is greater than 20 square meters, it is marked as a potential obstacle; for water bodies, areas with a water depth of more than 3 meters and a bank slope greater than 45° are identified as obstacle areas. Finally, the terrain obstacle distribution corresponding to each building, vegetation and water element in the complex terrain block of urban and rural planning is obtained, providing a detailed terrain reference basis for urban and rural planning design.

[0123] Furthermore, step S25 includes the following steps:

[0124] Step S251: Obtaining the outline, material characteristics, and spatial layout of the corresponding building from the urban and rural planning complex terrain survey area, and performing building element semantic segmentation on the building area within the three-dimensional model of the urban and rural planning complex terrain area based on the building outline, material characteristics, and spatial layout combined with geometric morphology and structural analysis to obtain each building area element within the urban and rural planning complex terrain area;

[0125] In an embodiment of the present invention, the outline, material characteristics and spatial layout of the corresponding building are obtained through the complex terrain survey area of ​​urban and rural planning, and are processed using satellite remote sensing images with a resolution of 0.5 meters and LiDAR data with a point cloud density of 200 points / square meter. First, an edge detection algorithm (such as the Canny operator) is combined with a morphological closing operation to extract the building outline from the satellite image, and the outline extraction accuracy reaches within 0.5 meters. For material feature analysis, a spectral feature library is constructed based on multispectral satellite data, and the building material is identified by a spectral matching algorithm. For example, the reflectivity of concrete material in the near-infrared band is lower than 0.3, while the reflectivity of metal material is higher than 0.7. Using a support vector machine (SVM) classifier, the material recognition accuracy reaches more than 92%, and the spatial layout analysis This is achieved by calculating the spacing, orientation, and relative position relationship between buildings, setting the spacing threshold to 5 meters, analyzing the relationship between building groups through the spatial adjacency matrix, and combining the Z value distribution of LiDAR point cloud data. The region growing algorithm is used to perform three-dimensional segmentation of buildings. The overlap between the segmentation results and the actual building units reaches 95%. Based on the above characteristics, the U-Net convolutional neural network is used to perform semantic segmentation of building areas within the three-dimensional model of complex terrain plots in urban and rural planning. The training dataset contains 1,000 labeled samples. Using the cross entropy loss function and the Adam optimizer, the model achieves an average intersection over union (mIoU) of 88% on the test set. Finally, 12 types of building elements such as building bases, walls, and roofs are obtained. Each element has attribute information such as material, height, and area.

[0126] Step S252: Obtaining the greening degree and growth space characteristics of corresponding vegetation from the plots of complex terrain in urban and rural planning, and performing semantic segmentation of vegetation elements within the 3D model of the plots of complex terrain in urban and rural planning based on the greening degree and growth space characteristics of the vegetation, thereby evaluating the volume and distribution density of the vegetation elements in the 3D space to determine the corresponding vegetation element distribution area, and obtaining various vegetation area elements within the plots of complex terrain in urban and rural planning;

[0127] In an embodiment of the present invention, the greening degree and growth space characteristics of the corresponding vegetation are obtained through the complex terrain survey area of ​​urban and rural planning, and the normalized vegetation index (NDVI) is calculated using the near-infrared band and red edge band of the satellite image. The formula is: NDVI = (NIR-RED) / (NIR+RED), where NIR is the reflectance of the near-infrared band and RED is the reflectance of the red band. The NDVI threshold is set to 0.35 to separate the vegetation area from the background. For the greening degree assessment, a stratified sampling method is adopted. Ten 5m×5m samples are randomly selected in each 100m×100m sub-block. The vegetation coverage in the sample is counted, and the sample data is expanded to the entire survey area through the Kriging interpolation method to generate a greening degree distribution map. The resolution is 1 meter. The growth space feature analysis is based on LiDAR point cloud data. The point cloud filtering algorithm is used to separate ground points and non-ground points, calculate the height distribution of vegetation points, set the canopy height threshold to 2 meters, distinguish between trees, shrubs and herbaceous vegetation, and use the three-dimensional convex hull algorithm to calculate the spatial volume of each plant. The volume calculation accuracy reaches 90%, and the vegetation area is semantically segmented by using the random forest classifier. The input features include 10 features such as NDVI value, canopy height, point cloud density, etc. The training data set contains 5,000 samples. The classification results divide the vegetation into 5 categories such as tree area, shrub area, grassland, etc. The classification accuracy reaches 85%, and finally the vegetation element distribution area is generated. Each distribution area contains attribute information such as vegetation type, coverage, volume density, etc.

[0128] Step S253: Obtaining the distribution area of ​​the corresponding water body through the urban and rural planning complex terrain survey area block, and performing water body element semantic segmentation on the water body area within the three-dimensional model of the urban and rural planning complex terrain block based on the distribution area of ​​the water body, so as to obtain each water body area element within the urban and rural planning complex terrain block;

[0129] In an embodiment of the present invention, the distribution area of ​​the corresponding water body is obtained by surveying the complex terrain of urban and rural planning areas, and the blue-green band ratio (B / G) of the satellite image is used to identify the water body. The formula is: B / G>1.2. Combined with the digital elevation model (DEM), the elevation mutation area is excluded and the shadow misjudgment is reduced. The water body identification accuracy rate reaches 96%. For the semantic segmentation of the water body area, the level set algorithm is used to process the satellite image. The initial contour line is located near the water body boundary. The contour position is iteratively optimized by minimizing the energy function. The energy function includes edge terms, regional terms, and regional terms. and regularization terms, set the number of iterations to 100 times, the convergence threshold to 0.01, the overlap between the segmentation results and the manual interpretation results reached 93%, and the segmented water area was morphologically processed to fill small holes and remove small noise. The area, perimeter, aspect ratio and other geometric parameters of each water patch were calculated. The area threshold was set to 100 square meters, and the water patches smaller than the threshold were merged into adjacent areas. Finally, different types of water area elements such as rivers, lakes, and ponds were obtained. Each element has attribute information such as area, perimeter, and water depth (calculated by DEM difference).

[0130] Step S254: Based on the terrain distribution range restrictions corresponding to each building area element, vegetation area element and water area element in the complex terrain block of urban and rural planning, the element obstacle distribution three-dimensional model of the complex terrain block of urban and rural planning is analyzed to obtain the terrain obstacle distribution corresponding to each building, vegetation and water element in the complex terrain block of urban and rural planning.

[0131] In an embodiment of the present invention, an element obstacle distribution analysis is performed on a three-dimensional model of a complex terrain block in urban and rural planning based on the terrain distribution range restrictions corresponding to each building area element, vegetation area element and water area element in the complex terrain block in urban and rural planning. First, the building area element, vegetation area element and water area element are converted into a voxel model in three-dimensional space, and the voxel size is 0.5 meters × 0.5 meters × 0.5 meters. For each voxel, its relative position relationship with the terrain surface is calculated, and the terrain surface is set as a reference plane. Voxels with a height exceeding 2 meters from the reference plane are defined as vertical obstacles, and voxels with a distance between adjacent voxels less than 1 meter in the horizontal direction are defined as horizontal obstacles. Continuous obstacle areas are identified through an eight-neighborhood search algorithm, and a cost distance analysis method is used to calculate the distance from the measured obstacle area to the measured obstacle area. The travel cost from the boundary of the area to each point inside the area is set to 10 (relative value) for the building area element, 8 for the tree area, 5 for the shrub area, 2 for the grassland area, and 15 for the water area element in the vegetation area. The cost of the area with a terrain slope of more than 30° is increased by 50%. The minimum cost path is generated by the Dijkstra algorithm, and the obstacle distribution on the path is analyzed. The area with an obstacle density exceeding 0.3 (the number of obstacle voxels / the total number of primes) is marked as a high-risk area, the area between 0.1-0.3 is marked as a medium-risk area, and the area less than 0.1 is marked as a low-risk area. Finally, a terrain obstacle distribution map corresponding to each building, vegetation and water element in the complex terrain block of urban and rural planning is obtained, providing quantitative spatial constraint information for urban and rural planning.

[0132] Furthermore, step S3 includes the following steps:

[0133] Step S31: obtaining the corresponding complex terrain plot slope size through the complex terrain survey area of ​​urban and rural planning;

[0134] In the embodiment of the present invention, the slope of the corresponding complex terrain plot is obtained by measuring the complex terrain area in urban and rural planning. The existing digital elevation model (DEM) data with a resolution of 1 meter × 1 meter is used, and a grid-based slope calculation method is adopted. For each grid cell in the DEM, a 3 × 3 grid window is selected with the cell as the center, and the slope is calculated using the elevation values ​​of the grid points in the window. The slope calculation formula is: in and are the elevation change rates in the x and y directions, respectively, and are calculated using the central difference method. For example, in a grid cell in the survey area, the elevation difference between adjacent grid cells in the x direction is 2 meters, and the elevation difference between adjacent grid cells in the y direction is 1.5 meters. The window size is 1 meter × 1 meter, then Substituting the formula into the equation yields a slope of Slope≈68.2°. Performing the above calculation for all grid cells in the survey area yields the slope of each location in the entire complex terrain, forming a slope distribution map. In the map, different colors represent different slope ranges, such as green for a slope less than 15°, yellow for 15°-30°, and red for greater than 30°, intuitively displaying the terrain slope of the survey area.

[0135] Step S32: analyzing the elevation mutation range of the complex terrain survey area in urban and rural planning to obtain the elevation mutation boundary range of the complex terrain block;

[0136] In an embodiment of the present invention, an elevation mutation range analysis is performed on a plot in a complex terrain survey area for urban and rural planning to obtain the elevation mutation boundary range of the complex terrain plot. First, along the boundary area corresponding to the plot in the complex terrain survey area for urban and rural planning, a geographic information system (GIS) is used to divide the elevation profile lines at 50-meter intervals, generating a total of 200 lines. Based on these profile lines, the plot is cut at 10-meter elevation intervals in the direction perpendicular to the profile lines to divide the plot into 2000 complex terrain equidistant elevation boundary sub-blocks. Then, the average elevation of the terrain points in each sub-block is calculated. where h i is the elevation of the i-th terrain point in the sub-block, n is the number of terrain points; the elevation standard deviation Set the elevation mutation threshold to the mean elevation plus 2 times the elevation standard deviation Traverse all sub-blocks. If there is a terrain point in a sub-block whose elevation exceeds the threshold, the sub-block is marked as a potential elevation mutation sub-block (for example, the average elevation of the sub-block "SB-010-05" is 90 meters, the elevation standard deviation is 6 meters, and the mutation threshold is 90+2×6=102 meters. When the elevation of a terrain point in the sub-block reaches 105 meters, the sub-block is marked). For another example, the local elevation distribution curvature of all terrain distribution points in the sub-block can be calculated, and for each terrain distribution point, the mean value μ of the local elevation distribution curvature in the 8 neighborhoods around it is calculated. At the same time, the threshold condition is set: if the local elevation distribution curvature of a point is with C i If the elevation distribution mutation point is between ≥2μ, the point is marked as an elevation distribution mutation point. Then, all the marked elevation distribution mutation points are merged through the GIS "fusion tool", and the "boundary extraction tool" is used to generate the elevation mutation boundary range of complex terrain blocks. The final boundary range clearly defines the elevation mutation areas such as steep slopes and cliffs, thereby generating the elevation mutation boundary range of complex terrain blocks.

[0137] Step S33: performing a surveying boundary constraint analysis on the complex terrain survey area plots in urban and rural planning based on the slope size of the complex terrain plots and the boundary range of the complex terrain plots with sudden elevation changes, so as to generate a three-dimensional surveying boundary constraint for the complex terrain plots;

[0138] In an embodiment of the present invention, a surveying boundary constraint analysis is performed on the complex terrain survey area plots in urban and rural planning based on the slope size of the complex terrain plots and the elevation mutation boundary range of the complex terrain plots. The slope distribution map and the elevation mutation boundary range layer are overlaid and analyzed in GIS. The area with a slope greater than 30° and located within the elevation mutation boundary range is set as a strong constraint area, restricting the entry of surveying and mapping equipment; the area with a slope between 15° and 30° and close to the elevation mutation boundary is set as a weak constraint area, restricting the surveying and mapping path. For example, a steep slope area in the eastern part of the survey area with a slope greater than 30° and located within the elevation mutation boundary range is designated as a prohibited surveying and mapping area; the surrounding area with a slope of 15° to 30° is set as a restricted access area, stipulating that surveying and mapping equipment can only enter along a specific route. At the same time, taking into account the performance parameters of the surveying and mapping equipment, such as the maximum climbing angle of 25° and the minimum turning radius of 3 meters, and combined with the terrain conditions, the surveying and mapping boundaries were further refined. Finally, the GIS "boundary generation tool" was used to generate three-dimensional surveying and mapping boundary constraints for complex terrain blocks, which were presented in the form of vector polygons, clearly identifying the surveyable area, restricted surveying and mapping area, and prohibited surveying and mapping area, providing a boundary basis for surveying and mapping path planning.

[0139] Step S34: Based on the terrain obstacle distribution corresponding to each building, vegetation and water element in the complex terrain block of urban and rural planning and the three-dimensional mapping boundary constraints of the complex terrain block, the mapping path obstacle planning is performed on the complex terrain survey area block of urban and rural planning, and the terrain constraint mapping planning path corresponding to the complex terrain block of urban and rural planning is generated.

[0140] In an embodiment of the present invention, mapping path obstacle planning is performed on the complex terrain survey area blocks of urban and rural planning based on the terrain obstacle distribution corresponding to each building, vegetation and water element in the complex terrain blocks of urban and rural planning and the three-dimensional mapping boundary constraints of the complex terrain blocks. In the GIS platform, the terrain obstacle distribution data of the buildings, vegetation and water elements are integrated with the three-dimensional mapping boundary constraint data, and the terrain information in the mappable area is abstracted into nodes and edges to construct a graph network. The weight calculation formula between nodes is: weight = α×distance + β×time consumption + γ×energy consumption, where α=0.4, β=0.3, and γ=0.3; where the distance is calculated based on the straight-line distance between the nodes, in meters, and the straight-line distance is obtained by calculating the difference between the coordinates of two nodes (x1, y1, z1) and (x2, y2, z2) in three-dimensional space using the Pythagorean theorem. The formula is: The time consumption is calculated based on the walking speed and distance. The walking speed is set to 1.5 m / s, and the calculation formula is time consumption = distance / 1.5. The energy consumption is estimated based on factors such as the terrain slope and the obstacle detour. For every 10° increase in the terrain slope, the energy consumption increases by 10%. Assuming that the basic energy consumption is E0 when there is no slope, when the slope is θ, the energy consumption calculation formula is If there is an obstacle to be bypassed, the energy consumption is increased proportionally according to the increased distance of the bypass. For example, if the bypass distance increases by d meters, the energy consumption increases by the original distance d×E0×0.2, and the Dijkstra algorithm is used to search for the shortest path from the starting point to the end point in the graph network. For example, in the middle of the survey area, the starting point is a flat area and the end point is the target surveying point located in a densely built area. The algorithm comprehensively considers avoiding building obstacles and crossing weakly constrained areas. After calculation, a path with the minimum total weight is obtained. This path bypasses high slopes and elevation mutation areas, and reasonably bypasses buildings, vegetation and water obstacles. Finally, a terrain-constrained surveying and mapping planning path is generated, providing accurate guidance for actual surveying and mapping operations.

[0141] Furthermore, step S32 includes the following steps:

[0142] By dividing the boundary area corresponding to the complex terrain survey area of ​​urban and rural planning into equally spaced elevation profile lines, the equally spaced elevation profile lines of the boundaries of each complex terrain plot are generated;

[0143] In an embodiment of the present invention, the spatial analysis function of a geographic information system (GIS) is used to divide equally spaced elevation profile lines along the boundary area of ​​an 8-square-kilometer urban and rural planning complex terrain survey area. The profile line spacing is set to 50 meters. The "Create Profile Line" tool of the GIS is used to generate an elevation profile line perpendicular to the boundary of the plot every 50 meters in a clockwise direction, with the plot boundary as the reference. For example, if the plot perimeter is 10 kilometers, a total of 200 equally spaced elevation profile lines are generated for the complex terrain plot boundary. Each profile line has a unique identifier, such as "PL-001" or "PL-002," and records the start and end coordinates and direction information of the profile line. These profile lines traverse the survey area in three-dimensional space, providing a basic framework for subsequent sub-block division and elevation analysis, ensuring that the terrain information of the plot boundary area is fully covered.

[0144] Preferably, the urban and rural planning complex terrain survey area plot is divided into equidistant elevation sub-blocks based on the equidistant elevation profile lines of the boundaries of each complex terrain plot, so as to obtain each complex terrain equidistant elevation boundary sub-block;

[0145] In an embodiment of the present invention, the complex terrain survey area plots for urban and rural planning are divided into equidistant elevation sub-blocks based on the previously generated 200 equidistant elevation profile lines of the complex terrain plot boundaries using the GIS "segmentation tool". The two adjacent profile lines are used as boundaries, and the area is cut at an elevation interval of 10 meters in the direction perpendicular to the profile lines. For example, the terrain elevation range between two adjacent profile lines is 50 meters to 150 meters. The area is divided into 10 sub-blocks along the elevation direction, and the elevation range of each sub-block is 10 meters (such as 50-60 meters, 60-70 meters, etc.). A total of 2,000 complex terrain equidistant elevation boundary sub-blocks are obtained. Each sub-block has a clear spatial range and elevation attributes. For example, the spatial range of sub-block "SB-001-01" is defined by two specific profile lines and upper and lower elevation limits, with an elevation range of 60-70 meters, laying the foundation for subsequent accurate analysis of terrain elevation changes.

[0146] Preferably, the elevation mutation range analysis is performed on the complex terrain survey area plots for urban and rural planning based on each complex terrain equidistant elevation boundary sub-block to obtain the elevation mutation boundary range of the complex terrain plots.

[0147] In the embodiment of the present invention, the elevation mutation range analysis of the complex terrain survey area of ​​urban and rural planning is performed based on 2000 complex terrain equidistant elevation boundary sub-blocks. For example, the average elevation and elevation standard deviation of the terrain points in each sub-block can be calculated. The formula is: average elevation where h i is the elevation of the i-th terrain point in the sub-block, n is the number of terrain points; the elevation standard deviation Set the elevation mutation threshold to the mean elevation plus 2 times the elevation standard deviation Traverse all sub-blocks. If there is a terrain point in a sub-block whose elevation exceeds the threshold, the sub-block is marked as a potential elevation mutation sub-block. For example, the local elevation distribution curvature of all terrain distribution points in it can be calculated, and for each terrain distribution point, the mean value μ of the local elevation distribution curvature in the 8 neighborhoods around it is calculated. At the same time, the threshold condition is set: if the local elevation distribution curvature of a point is greater than the threshold, the curvature of the local elevation distribution of the sub-block is greater than the threshold. with C i If the elevation distribution mutation point is between ≥2μ, the point is marked as an elevation distribution mutation point. Then, all the marked elevation distribution mutation points are merged through the GIS "fusion tool", and the "boundary extraction tool" is used to generate the elevation mutation boundary range of complex terrain blocks. The final boundary range clearly defines elevation mutation areas such as steep slopes and cliffs. For example, an elevation mutation boundary range of about 0.5 square kilometers is formed in the northeast corner of the survey area, providing a key reference basis for terrain processing and engineering design in urban and rural planning and construction.

[0148] Furthermore, the elevation mutation range analysis of the complex terrain survey area plots for urban and rural planning based on each complex terrain equidistant elevation boundary sub-block includes the following steps:

[0149] By determining the elevation distribution corresponding to each terrain distribution point in the complex terrain equidistant elevation boundary sub-block, and calculating the local elevation curvature of each terrain distribution point in the corresponding complex terrain equidistant elevation boundary sub-block based on the elevation distribution corresponding to each terrain distribution point, the local elevation distribution curvature corresponding to each terrain distribution point in the complex terrain equidistant elevation boundary sub-block is obtained;

[0150] In an embodiment of the present invention, the terrain of a complex terrain survey area for urban and rural planning is divided into equidistant elevation boundary sub-blocks at 10-meter contour intervals. Taking a sub-block as an example, its coverage area is 1 square kilometer, containing 1 million terrain distribution points, and the point cloud density is 100 points / square meter. The local elevation curvature is calculated for each terrain distribution point in the sub-block. A moving window method is used to set a 5×5 neighborhood window (window size is 25 square meters) with the target point as the center. For each point in the window, the local terrain morphology is fitted using a quadratic surface. The fitting equation is: z = a + bx + cy + dx 2 +exy+fy 2 Where z is the elevation value, x and y are plane coordinates, a, b, c, d, e, and f are fitting parameters. The least squares method is used to solve the parameters and calculate the Gaussian curvature K and mean curvature H at the point. The local elevation distribution curvature C is defined as: For example, the calculated result of the local elevation distribution curvature of a terrain distribution point at the edge of a valley within a sub-block is 0.05, while the curvature value of the point in the flat area is close to 0. By calculating point by point, the local elevation distribution curvature of all terrain distribution points within the sub-block with equidistant elevation boundaries of the complex terrain is finally obtained, forming a curvature distribution map with a spatial resolution of 0.5 meters.

[0151] Preferably, the elevation mutation points are screened based on the local elevation distribution curvature corresponding to each terrain distribution point in the complex terrain equidistant elevation boundary sub-block. If the local elevation distribution curvature corresponding to a terrain distribution point is greater than or equal to the mean of the local elevation distribution curvature in the 8 neighborhoods around the terrain distribution point, the elevation mutation point is selected. When it reaches 2 times, it is determined as the elevation distribution mutation point within the complex terrain equidistant elevation boundary sub-block, and the process continues until all terrain distribution points are determined, thereby obtaining the elevation distribution mutation points corresponding to each complex terrain equidistant elevation boundary sub-block;

[0152] In the embodiment of the present invention, the elevation mutation points are screened according to the local elevation distribution curvature corresponding to each terrain distribution point in the complex terrain equidistant elevation boundary sub-block. For each terrain distribution point, the mean value μ of the local elevation distribution curvature in the 8 neighborhoods around it is calculated, and the threshold condition is set: if the local elevation distribution curvature of a point is with C i ≥2μ, the point is marked as a sudden change point of elevation distribution. Taking a point P in the sub-block as an example, its local elevation distribution curvature C P =0.08, the curvature values ​​of the 8 neighboring points are [0.01, 0.02, 0.01, 0.03, 0.02, 0.01, 0.02, 0.01], and the calculated mean μ = 0.0175. P =0.08>2μ=0.035, point P is determined to be an elevation mutation point. When processing the terrain at the junction of mountains and plains, the elevation changes dramatically in such areas, and a large number of points meet the mutation point conditions. By traversing all 1 million terrain distribution points in the sub-block, a total of 25,000 elevation distribution mutation points are identified, accounting for 2.5% of the total number of points. These mutation points are mainly concentrated in the mountain edges, river terraces, and artificial excavation areas, forming a discrete mutation point set.

[0153] Preferably, corresponding complex terrain elevation distribution mutation boundary lines are generated according to the elevation distribution mutation points corresponding to each complex terrain equidistant elevation boundary sub-block, and based on the complex terrain elevation distribution mutation boundary lines, elevation mutation range analysis is performed on the complex terrain survey area plots in urban and rural planning to obtain the elevation mutation boundary range of the complex terrain plots.

[0154] In an embodiment of the present invention, a complex terrain elevation distribution mutation boundary line is generated based on the elevation distribution mutation points corresponding to each complex terrain equidistant elevation boundary sub-block. The Delaunay triangulation algorithm is used to construct the mutation point set into an irregular triangulated network (TIN), generate about 50,000 triangles, calculate the side length and internal angle of each triangle, and screen out triangles with a side length less than 2 meters and a minimum internal angle greater than 20° to form a high-quality triangulated network. In the triangulated network, the common edges of adjacent triangles are searched. If the elevation difference between the two sides of the edge exceeds 5 meters (i.e., half of the equidistant interval), the edge is marked as a boundary candidate edge. All candidate edges are traced and connected, and a depth-first search algorithm is used to search for the edge from any boundary candidate. Starting from edge selection, adjacent candidate edges are connected in sequence to form closed polygonal rings. The boundary lines are optimized through morphological operations. First, an expansion operation is performed (the structural element is a 3×3 meter rectangle) to fill the small gaps in the boundary lines; then an erosion operation is performed (the same structural element) to restore the original position of the boundary. Finally, the boundary lines of complex terrain elevation distribution mutations are generated. The total length of the boundary lines is 15 kilometers, containing 50 independent closed polygons, which accurately delineate the elevation mutation ranges within the complex terrain survey areas of urban and rural planning, such as steep slope areas and artificial fill areas. The average width of these boundary ranges is 10 meters, and the overlap with the actual terrain mutation area reaches 95%. Finally, the elevation mutation boundary ranges of complex terrain plots are obtained.

[0155] Furthermore, step S34 includes the following steps:

[0156] Step S341: performing spatial obstacle perception analysis based on the terrain obstacle distribution corresponding to each building, vegetation, and water element within the complex terrain of the urban and rural planning area to generate a terrain spatial obstacle perception distribution map corresponding to the complex terrain of the urban and rural planning area;

[0157] In an embodiment of the present invention, spatial obstacle perception analysis is performed based on the terrain obstacle distribution corresponding to each building, vegetation and water element in the complex terrain of the urban and rural planning. The terrain obstacle distribution data of the building, vegetation and water element are imported into the geographic information system (GIS) platform, and the plot is divided into 0.5 meters × 0.5 meters grid units. Each grid is traversed to determine whether there are obstacle elements in the grid. For the grid containing building obstacles, if the building height exceeds 2 meters and the horizontal projection covers the grid, the grid is marked as a building obstacle area; for vegetation obstacles, when the tree height exceeds 10 meters and the crown covers more than 50% of the grid area, or the density of shrubs and grass makes it difficult to pass (such as per square meter), the grid is marked as a building obstacle area. For water obstacles, grids with a water depth of more than 3 meters and a bank slope greater than 45° are used as the marking basis. In order to intuitively display the distribution of obstacles, the obstacle areas are divided into three levels: high risk (red), medium risk (yellow), and low risk (orange). For example, in a high-rise residential area in the center of the survey area, the corresponding grid is marked as a high-risk building obstacle area because the buildings are dense and the height exceeds 20 meters; in a dense forest on the east side of the plot, the relevant grids are marked as a high-risk vegetation obstacle area because the trees are tall and the crowns are staggered. The final generated terrain spatial obstacle perception distribution map clearly presents the obstacle distribution status of the entire plot.

[0158] Step S342: Based on the terrain spatial obstacle perception distribution map corresponding to the complex terrain parcel in urban and rural planning, combined with the boundary constraints of the three-dimensional mapping of the complex terrain parcel, terrain constraint superposition division is performed, so as to combine the distribution of buildings, vegetation, and water obstacles within the complex terrain parcel in urban and rural planning with the boundary constraints to delineate corresponding accessible areas and inaccessible areas, thereby generating a terrain constraint access division area corresponding to the complex terrain parcel in urban and rural planning;

[0159] In an embodiment of the present invention, terrain constraints are superimposed and divided based on the terrain spatial obstacle perception distribution map corresponding to the complex terrain blocks in urban and rural planning and combined with the three-dimensional surveying and mapping boundary constraints of the complex terrain blocks. The three-dimensional surveying and mapping boundary constraints of the complex terrain blocks are determined by the previous surveying and mapping planning, including the boundary range of the blocks, prohibited entry areas (such as military restricted areas, private territories) and other information. The boundary constraint data is imported into the GIS platform in the form of a vector layer and superimposed and analyzed with the terrain spatial obstacle perception distribution map. For each grid, it is judged whether it is within the boundary constraint range, and a comprehensive judgment is made in combination with the obstacle level. If the grid is both within the boundary constraint range and belongs to a high-risk or medium-risk obstacle area, it is designated as inaccessible. If the grid is within the boundary constraint and is a low-risk obstacle area, or there is no obstacle element, it is delineated as a traversable area. For example, there is a river with a width of 10 meters on the south side of the plot, which is a high-risk water obstacle area. At the same time, this area is also within the surveying and mapping boundary. Therefore, the grids with a certain buffer distance (set to 5 meters) around the river are delineated as inaccessible areas. On the other hand, there is an open grassland on the west side of the plot. Although there is a small amount of low vegetation, it is a low-risk obstacle area and is within the boundary, so it is delineated as a traversable area. In this way, the terrain-constrained traversable demarcation area corresponding to the complex terrain plot in urban and rural planning is finally generated, which clearly distinguishes between traversable and inaccessible areas.

[0160] Step S343: Based on the accessible area within the terrain-constrained access demarcation area corresponding to the complex terrain plot in urban and rural planning, a surveying and mapping path access planning analysis is performed on the complex terrain survey area plot in urban and rural planning, so as to fully consider the impact of obstacles corresponding to buildings, vegetation and water bodies and terrain constraints, calculate the shortest distance, minimum time consumption and minimum energy consumption of the corresponding surveying and mapping path, and determine the optimal surveying and mapping path based on the shortest distance, minimum time consumption and minimum energy consumption of the surveying and mapping path, and generate the terrain-constrained surveying and mapping planning path corresponding to the complex terrain plot in urban and rural planning.

[0161] In an embodiment of the present invention, a mapping path access planning analysis is performed on the urban and rural planning complex terrain survey area blocks by dividing the accessible areas within the terrain constraint access areas corresponding to the urban and rural planning complex terrain blocks. In the GIS platform, the grids within the accessible areas are abstracted as nodes. If adjacent nodes are accessible, connections are established to form edges to construct a graph network. The weights between the nodes are set, and the influence of obstacles corresponding to buildings, vegetation and water bodies and terrain constraints are considered. The calculation is performed by the following formula: Weight = α × distance + β × time consumption + γ × energy consumption, where α = 0.4, β = 0.3, and γ = 0.3; the distance is calculated based on the straight-line distance between the nodes (unit: meter); the time consumption is calculated based on the walking speed (set to 1.5 meters / second) and the distance; and the energy consumption is calculated based on A comprehensive estimate is made based on factors such as terrain slope (energy consumption increases by 10% for every 10° increase in slope) and obstacle detours. For example, the weight coefficient is: α = 0.4, which means that the distance factor accounts for 40% of the weight calculation, indicating that distance has a greater impact on path selection, and under the same conditions, a shorter path is preferred; β = 0.3, which means that the time consumption factor accounts for 30%, reflecting the degree of consideration of time efficiency when planning a path; γ = 0.3, which means that the energy consumption factor accounts for 30%, reflecting the importance of considering equipment energy consumption for path planning in complex terrain; the distance is calculated based on the straight-line distance between nodes, in meters, and the straight-line distance is calculated by the Pythagorean theorem as the difference between the coordinates of two nodes in three-dimensional space (x1, y1, z1) and (x2, y2, z2), and the formula is: The time consumption is calculated based on the walking speed and distance. The walking speed is set to 1.5 m / s, and the calculation formula is time consumption = distance / 1.5. The energy consumption is estimated based on factors such as the terrain slope and the obstacle detour. For every 10° increase in the terrain slope, the energy consumption increases by 10%. Assuming that the basic energy consumption is E0 when there is no slope, when the slope is θ, the energy consumption calculation formula is If there are obstacles and detours, energy consumption is increased proportionally based on the increased detour distance. For example, if the detour distance increases by d meters, energy consumption increases by the original distance d × E0 × 0.2. At the same time, the Dijkstra algorithm is used to search for the shortest path from the starting point to the end point in the graph network. This path is the optimal mapping path that meets the requirements of shortest distance, minimum time consumption, and lowest energy consumption. For example, within a traversable area of ​​the survey area, there are multiple target points that need to be mapped. By calculating the weights between each node, the Dijkstra algorithm searches for a path with the minimum total weight. This path avoids high-risk obstacle areas and reasonably detours medium- and low-risk areas. Ultimately, it generates a terrain-constrained mapping planning path corresponding to the complex terrain of the urban and rural planning area, providing accurate path guidance for actual mapping work.

[0162] Furthermore, the present invention also provides an automated mapping system for complex terrain plots in urban and rural planning, which is used to execute the automated mapping method for complex terrain plots in urban and rural planning as described above. The automated mapping system for complex terrain plots in urban and rural planning includes:

[0163] The complex terrain pre-detection module is used to obtain satellite remote sensing data and digital elevation models corresponding to the urban and rural planning survey area, and perform terrain pre-detection of the survey area based on the satellite remote sensing data and digital elevation models corresponding to the urban and rural planning survey area, thereby generating plots of complex terrain survey areas for urban and rural planning;

[0164] The feature terrain obstacle analysis module is used to obtain the corresponding complex terrain block 3D point cloud data from the urban and rural planning complex terrain survey area and construct the 3D model of the urban and rural planning complex terrain block; perform feature semantic recognition and segmentation on the 3D model of the urban and rural planning complex terrain block, so as to obtain the terrain obstacle distribution corresponding to each building, vegetation and water element in the urban and rural planning complex terrain block;

[0165] The mapping path obstacle planning module is used to obtain the corresponding three-dimensional mapping boundary constraints of the complex terrain plots in the urban and rural planning complex terrain survey area through the plots. Based on the distribution of terrain obstacles corresponding to each building, vegetation and water element in the complex terrain plots in the urban and rural planning complex terrain, combined with the three-dimensional mapping boundary constraints of the complex terrain plots, the mapping path obstacle planning is performed for the plots in the urban and rural planning complex terrain survey area, thereby generating a terrain-constrained mapping planning path corresponding to the complex terrain plots in the urban and rural planning;

[0166] The automated navigation and mapping module is used to perform automated navigation and mapping processing on the complex terrain survey area blocks in urban and rural planning based on the terrain constraint surveying and mapping planning path corresponding to the complex terrain blocks in urban and rural planning, so as to generate surveying and mapping data for the complex terrain blocks in urban and rural planning.

[0167] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0168] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. An automated mapping method for complex terrain plots in urban and rural planning, characterized by: The following steps are involved: Step S1: obtaining satellite remote sensing data and digital elevation models corresponding to the urban and rural planning survey area, and performing terrain pre-detection of the survey area based on the satellite remote sensing data and digital elevation models corresponding to the urban and rural planning survey area to generate plots of complex terrain survey areas for urban and rural planning; Step S2: Obtain corresponding 3D point cloud data of complex terrain blocks through the urban and rural planning complex terrain survey area and construct a 3D model of the complex terrain blocks in the urban and rural planning; perform element semantic recognition and segmentation on the 3D model of the complex terrain blocks in the urban and rural planning to obtain the terrain obstacle distribution corresponding to each building, vegetation and water element in the complex terrain blocks in the urban and rural planning; Step S3: Obtaining the corresponding 3D mapping boundary constraints of the complex terrain plots from the complex terrain survey area plots in the urban and rural planning, and performing mapping path obstacle planning for the complex terrain survey area plots in the urban and rural planning based on the terrain obstacle distribution corresponding to each building, vegetation, and water element within the complex terrain plots in the urban and rural planning, combined with the 3D mapping boundary constraints of the complex terrain plots, to generate a terrain-constrained mapping planning path corresponding to the complex terrain plots in the urban and rural planning; Step S4: performing automated navigation and mapping processing on the urban and rural planning complex terrain survey area block based on the terrain constraint surveying and mapping planning path corresponding to the urban and rural planning complex terrain block to generate urban and rural planning complex terrain block surveying and mapping data.

2. The automated mapping method for complex terrain plots in urban and rural planning according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire satellite remote sensing data corresponding to the urban and rural planning survey area; Step S12: obtaining a digital elevation model corresponding to the urban and rural planning survey area; Step S13: Obtaining the topographic distribution, building density, and traffic network layout corresponding to the urban and rural planning survey area; Step S14: Based on the digital elevation model corresponding to the urban and rural planning survey area and in combination with the corresponding topographic distribution, building density, and transportation network layout, the satellite remote sensing data corresponding to the urban and rural planning survey area is subjected to spatial terrain fusion modeling to generate a spatial terrain distribution fusion model corresponding to the urban and rural planning survey area; Step S15: Preliminary terrain detection of the urban and rural planning survey area is performed based on the spatial terrain distribution fusion model corresponding to the urban and rural planning survey area to generate a plot of complex terrain survey area for the urban and rural planning survey area.

3. The method for automatic mapping of complex terrain blocks for urban and rural planning according to claim 2, characterized in that: Step S15 includes the following steps: Step S151: Divide the spatial terrain distribution fusion model corresponding to the urban and rural planning survey area into terrain blocks according to a preset block size to generate terrain distribution sub-blocks for each urban and rural planning survey area; Step S152: performing terrain distribution gradient statistics on each of the urban and rural planning survey area terrain distribution sub-blocks to obtain the terrain distribution gradient corresponding to each of the urban and rural planning survey area sub-blocks; Step S153: Obtaining the building distribution density and pipeline network distribution density corresponding to each urban and rural planning survey area sub-block through each urban and rural planning survey area terrain distribution sub-block, and estimating the terrain distribution density of the corresponding urban and rural planning survey area terrain distribution sub-block based on the building distribution density and pipeline network distribution density corresponding to each urban and rural planning survey area sub-block, so as to obtain the terrain distribution density corresponding to each urban and rural planning survey area sub-block; Step S154: performing terrain complexity assessment based on the terrain distribution gradient and terrain distribution density corresponding to each urban and rural planning survey area sub-block to obtain the terrain distribution complexity corresponding to each urban and rural planning survey area sub-block; Step S155: Preliminary terrain detection of the urban and rural planning survey area is performed based on the terrain distribution complexity corresponding to each urban and rural planning survey area sub-block, so as to compare and judge the terrain distribution complexity corresponding to the urban and rural planning survey area sub-block according to a preset terrain complexity threshold, and mark the urban and rural planning survey area sub-blocks corresponding to terrain distribution complexity greater than or equal to the preset terrain complexity threshold as terrain complex sub-blocks, and aggregate the surrounding terrain complex sub-blocks corresponding to the corresponding terrain complex sub-blocks together and use morphological operations to fuse them to generate corresponding regional plots, thereby generating urban and rural planning complex terrain survey area plots.

4. The method for automatic mapping of complex terrain blocks for urban and rural planning according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: 3D laser scanning including UAV-mounted LiDAR, ground mobile measurement system and fixed monitoring station is used to measure the complex terrain survey area of ​​urban and rural planning to obtain corresponding 3D point cloud data of the complex terrain plot; Step S22: performing a combination of straight-through filtering and statistical filtering to remove noise from the three-dimensional point cloud data of the complex terrain block, so as to obtain denoised point cloud data of the complex terrain block; Step S23: performing a three-dimensional spatial coordinate system conversion on the denoised point cloud data of the complex terrain block to generate corresponding spatial point cloud data of the complex terrain block in the three-dimensional spatial coordinate system; Step S24: performing three-dimensional spatial modeling of the complex terrain plot for urban and rural planning based on the corresponding complex terrain plot spatial point cloud data in the three-dimensional spatial coordinate system to generate a three-dimensional model of the complex terrain plot for urban and rural planning; Step S25: performing element semantic recognition and segmentation on the three-dimensional model of the complex terrain block in urban and rural planning, and obtaining the terrain obstacle distribution corresponding to each building, vegetation and water element in the complex terrain block in urban and rural planning.

5. The method for automatic mapping of complex terrain blocks for urban and rural planning according to claim 4 is characterized in that: Step S25 includes the following steps: Step S251: Obtaining the outline, material characteristics, and spatial layout of the corresponding building from the urban and rural planning complex terrain survey area, and performing building element semantic segmentation on the building area within the three-dimensional model of the urban and rural planning complex terrain area based on the building outline, material characteristics, and spatial layout combined with geometric morphology and structural analysis to obtain each building area element within the urban and rural planning complex terrain area; Step S252: Obtaining the greening degree and growth space characteristics of corresponding vegetation from the plots of complex terrain in urban and rural planning, and performing semantic segmentation of vegetation elements within the 3D model of the plots of complex terrain in urban and rural planning based on the greening degree and growth space characteristics of the vegetation, thereby evaluating the volume and distribution density of the vegetation elements in the 3D space to determine the corresponding vegetation element distribution area, and obtaining various vegetation area elements within the plots of complex terrain in urban and rural planning; Step S253: Obtaining the distribution area of ​​the corresponding water body through the urban and rural planning complex terrain survey area block, and performing water body element semantic segmentation on the water body area within the three-dimensional model of the urban and rural planning complex terrain block based on the distribution area of ​​the water body, so as to obtain each water body area element within the urban and rural planning complex terrain block; Step S254: Based on the terrain distribution range restrictions corresponding to each building area element, vegetation area element and water area element in the complex terrain block of urban and rural planning, the element obstacle distribution three-dimensional model of the complex terrain block of urban and rural planning is analyzed to obtain the terrain obstacle distribution corresponding to each building, vegetation and water element in the complex terrain block of urban and rural planning.

6. The automated mapping method for complex terrain plots in urban and rural planning according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: obtaining the corresponding complex terrain plot slope size through the complex terrain survey area of ​​urban and rural planning; Step S32: analyzing the elevation mutation range of the complex terrain survey area in urban and rural planning to obtain the elevation mutation boundary range of the complex terrain block; Step S33: performing a surveying boundary constraint analysis on the complex terrain survey area plots in urban and rural planning based on the slope size of the complex terrain plots and the boundary range of the complex terrain plots with sudden elevation changes, so as to generate a three-dimensional surveying boundary constraint for the complex terrain plots; Step S34: Based on the terrain obstacle distribution corresponding to each building, vegetation and water element in the complex terrain block of urban and rural planning and the three-dimensional mapping boundary constraints of the complex terrain block, the mapping path obstacle planning is performed on the complex terrain survey area block of urban and rural planning, and the terrain constraint mapping planning path corresponding to the complex terrain block of urban and rural planning is generated.

7. The automated mapping method for complex terrain plots in urban and rural planning according to claim 6, characterized in that: Step S32 includes the following steps: By dividing the boundary area corresponding to the complex terrain survey area of ​​urban and rural planning into equally spaced elevation profile lines, the equally spaced elevation profile lines of the boundaries of each complex terrain plot are generated; Based on the equidistant elevation profile lines of the boundaries of each complex terrain block, the urban and rural planning complex terrain survey area blocks are divided into equidistant elevation sub-blocks to obtain the equidistant elevation boundary sub-blocks of each complex terrain; Based on the equidistant elevation boundary sub-blocks of each complex terrain, the elevation mutation range of the complex terrain survey area of ​​urban and rural planning is analyzed to obtain the elevation mutation boundary range of the complex terrain block.

8. The method for automatic mapping of complex terrain blocks for urban and rural planning according to claim 7, characterized in that: The method of performing elevation mutation range analysis on the complex terrain survey area plots for urban and rural planning based on each complex terrain equidistant elevation boundary sub-block comprises the following steps: By determining the elevation distribution corresponding to each terrain distribution point in the complex terrain equidistant elevation boundary sub-block, and calculating the local elevation curvature of each terrain distribution point in the corresponding complex terrain equidistant elevation boundary sub-block based on the elevation distribution corresponding to each terrain distribution point, the local elevation distribution curvature corresponding to each terrain distribution point in the complex terrain equidistant elevation boundary sub-block is obtained; The elevation mutation points are screened based on the local elevation distribution curvature corresponding to each terrain distribution point in the complex terrain equidistant elevation boundary sub-block. If the local elevation distribution curvature corresponding to a terrain distribution point is greater than or equal to the mean of the local elevation distribution curvature in the 8 neighborhoods around the terrain distribution point, the elevation mutation point is selected. When it reaches 2 times, it is determined as the elevation distribution mutation point within the complex terrain equidistant elevation boundary sub-block, and the process continues until all terrain distribution points are determined, thereby obtaining the elevation distribution mutation points corresponding to each complex terrain equidistant elevation boundary sub-block; According to the elevation distribution mutation points corresponding to each complex terrain equidistant elevation boundary sub-block, the corresponding complex terrain elevation distribution mutation boundary line is generated, and based on the complex terrain elevation distribution mutation boundary line, the elevation mutation range of the urban and rural planning complex terrain survey area plots is analyzed to obtain the elevation mutation boundary range of the complex terrain plots.

9. The automated mapping method for complex terrain plots in urban and rural planning according to claim 6, characterized in that: Step S34 includes the following steps: Step S341: performing spatial obstacle perception analysis based on the terrain obstacle distribution corresponding to each building, vegetation, and water element within the complex terrain of the urban and rural planning area to generate a terrain spatial obstacle perception distribution map corresponding to the complex terrain of the urban and rural planning area; Step S342: Based on the terrain spatial obstacle perception distribution map corresponding to the complex terrain parcel in urban and rural planning, combined with the boundary constraints of the three-dimensional mapping of the complex terrain parcel, terrain constraint superposition division is performed, so as to combine the distribution of buildings, vegetation, and water obstacles within the complex terrain parcel in urban and rural planning with the boundary constraints to delineate corresponding accessible areas and inaccessible areas, thereby generating a terrain constraint access division area corresponding to the complex terrain parcel in urban and rural planning; Step S343: Based on the accessible area within the terrain-constrained access demarcation area corresponding to the complex terrain plot in urban and rural planning, a surveying and mapping path access planning analysis is performed on the complex terrain survey area plot in urban and rural planning, so as to fully consider the impact of obstacles corresponding to buildings, vegetation and water bodies and terrain constraints, calculate the shortest distance, minimum time consumption and minimum energy consumption of the corresponding surveying and mapping path, and determine the optimal surveying and mapping path based on the shortest distance, minimum time consumption and minimum energy consumption of the surveying and mapping path, and generate the terrain-constrained surveying and mapping planning path corresponding to the complex terrain plot in urban and rural planning.

10. An automated mapping system for complex terrain plots in urban and rural planning, characterized by: The method for automatically mapping complex terrain plots for urban and rural planning according to claim 1 is used to implement the automated mapping method for complex terrain plots for urban and rural planning, and the automated mapping system for complex terrain plots for urban and rural planning comprises: The complex terrain pre-detection module is used to obtain satellite remote sensing data and digital elevation models corresponding to the urban and rural planning survey area, and perform terrain pre-detection of the survey area based on the satellite remote sensing data and digital elevation models corresponding to the urban and rural planning survey area, thereby generating plots of complex terrain survey areas for urban and rural planning; The feature terrain obstacle analysis module is used to obtain the corresponding complex terrain block 3D point cloud data from the urban and rural planning complex terrain survey area and construct the 3D model of the urban and rural planning complex terrain block; perform feature semantic recognition and segmentation on the 3D model of the urban and rural planning complex terrain block, so as to obtain the terrain obstacle distribution corresponding to each building, vegetation and water element in the urban and rural planning complex terrain block; The mapping path obstacle planning module is used to obtain the corresponding three-dimensional mapping boundary constraints of the complex terrain plots in the urban and rural planning complex terrain survey area through the plots. Based on the distribution of terrain obstacles corresponding to each building, vegetation and water element in the complex terrain plots in the urban and rural planning complex terrain, combined with the three-dimensional mapping boundary constraints of the complex terrain plots, the mapping path obstacle planning is performed for the plots in the urban and rural planning complex terrain survey area, thereby generating a terrain-constrained mapping planning path corresponding to the complex terrain plots in the urban and rural planning; The automated navigation and mapping module is used to perform automated navigation and mapping processing on the complex terrain survey area blocks in urban and rural planning based on the terrain constraint surveying and mapping planning path corresponding to the complex terrain blocks in urban and rural planning, so as to generate surveying and mapping data for the complex terrain blocks in urban and rural planning.

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