Intelligent mapping method for natural resource rights confirmation based on three-dimensional working base map

Abstract

The present invention belongs to the field of electronic digital data processing technology, and specifically relates to an intelligent mapping method for natural resource rights confirmation based on a three-dimensional working base map. The method comprises: Step 1: Detecting and identifying natural resources on the three-dimensional working base map, dividing the three-dimensional working base map into multiple different natural resource areas; Step 2: Within each natural resource area, subdividing natural resource areas with terrain elevation conflicts along terrain elevation conflict lines; and Step 3: Determining an upper limit for the natural resource area. For each natural resource area, the area of the merged natural area must be less than the determined upper limit for the natural resource area. The present invention achieves high-precision detection of natural resource boundaries and continuous division.

Description

Technical Field

[0001] The present invention belongs to the technical field of electronic digital data processing, and in particular relates to an intelligent mapping method for natural resource rights confirmation based on a three-dimensional working base map. Background Art

[0002] Natural resource title confirmation and mapping is a core task in resource management, planning, and conservation. Its goal is to accurately identify and delineate natural resource areas, providing fundamental data support for scientific resource management and ownership demarcation. With the advancement of remote sensing, geographic information systems (GIS), and digital Earth technologies, natural resource title confirmation has shifted from traditional manual surveying and mapping to automated mapping based on spatial data. However, existing technologies still have many shortcomings when addressing complex terrain, diverse resource types, and the need for high-precision title confirmation, requiring further improvement and optimization.

[0003] Traditional natural resource mapping relies primarily on two-dimensional remote sensing imagery. These images capture the spectral reflectance characteristics of the earth's surface using multispectral or hyperspectral sensors and, combined with object classification algorithms (such as supervised and unsupervised classification), enable a preliminary classification of resource types. The advantages of two-dimensional imagery lie in their wide coverage and ease of data acquisition, particularly in flat areas and simple terrain. For example, vegetation cover extraction methods based on the Normalized Difference Vegetation Index (NDVI) have been widely used for the classification and monitoring of resources such as forests and grasslands. However, a limitation of two-dimensional imagery is that they only reflect the planar distribution characteristics of the earth's surface and cannot accurately depict the impact of topographical undulations on resource distribution. In areas with complex terrain, such as mountains, hills, or river valleys, two-dimensional methods often misjudge boundaries or confuse resource types due to their neglect of elevation information. Furthermore, two-dimensional imagery is susceptible to interference from factors such as shadows and cloud cover, limiting the accuracy and reliability of resource mapping. To address the limitations of two-dimensional remote sensing imagery, digital elevation models (DEMs) are widely used in natural resource mapping. DEM provides surface elevation data, which can assist in identifying terrain features (such as ridges, valleys, slopes, etc.) and introduce terrain dimensions in the demarcation of resource boundaries. For example, in land use classification in mountainous areas, DEM can be used to determine the slope limits of farmland, thereby distinguishing flat areas suitable for farming from unused land with excessive slopes. Although the introduction of DEM has improved mapping accuracy, its application is mostly limited to simple terrain analysis, such as slope calculation and aspect determination. Traditional methods find it difficult to deeply integrate elevation information with spectral features, resulting in the failure to fully realize the potential of DEM in complex terrain areas. In addition, the resolution and accuracy limitations of DEM also have an adverse impact on the fine-grained demarcation of natural resources. Summary of the Invention

[0004] In light of this, the primary purpose of this invention is to provide an intelligent mapping method for natural resource rights confirmation based on a three-dimensional working base map, enabling high-precision detection and continuous delineation of natural resource boundaries. This method is highly adaptable to complex terrain and diverse natural resource environments, effectively addressing existing issues such as insufficient fusion of multispectral and elevation information, discontinuous boundary detection, and poor adaptability to complex terrain. This significantly improves the accuracy, intelligence, and efficiency of natural resource mapping.

[0005] The technical solution adopted in the present invention is as follows:

[0006] An intelligent mapping method for natural resource rights confirmation based on a three-dimensional working base map, the method comprising:

[0007] Step 1: Detect and identify natural resources on a 3D working base map to generate preliminary boundaries. Based on these boundaries, the 3D working base map is divided into multiple natural resource areas. The 3D working base map is created using image data captured by remote sensing satellites and geographic data acquired by a GIS system.

[0008] Step 2: Perform terrain elevation conflict detection in each natural resource area to obtain the terrain elevation conflict line in each natural resource area, and redivide the natural resource areas with terrain elevation conflicts according to the terrain elevation conflict lines;

[0009] Step 3: Determine an upper limit for the natural resource area. For each natural resource area, calculate the average elevation difference between it and adjacent natural resource areas of different types. If the average elevation difference is within the set threshold, the two natural resource areas will be merged into one natural resource area. The area of the merged natural area must be smaller than the determined upper limit for the natural resource area. The average elevation difference is defined as the absolute value of the difference between the average altitude of one natural resource area and the average altitude of another natural resource area.

[0010] Furthermore, in step 2, the specific process of performing terrain elevation conflict detection in each natural resource area includes: obtaining a contour distribution map of each natural resource area from the three-dimensional working base map; in the contour distribution map, if the height distance difference between any two adjacent contour lines exceeds a set threshold, it is judged that there is a terrain elevation conflict, and the contour line with the lower height of the two adjacent contour lines is used as the terrain elevation conflict line; the types of natural resource areas include: rivers, wetlands, forests, grasslands, cultivated land and deserts.

[0011] Furthermore, step 1 specifically includes:

[0012] Step 1.1: Calculate the multispectral features of the 3D working base map; based on the multispectral features, calculate the local feature tensor of each pixel in the 3D working base map;

[0013] Step 1.2: Based on the multispectral features and the local feature tensor of each pixel, calculate the boundary feature strength of the 3D working base map; construct a boundary tracking function based on the boundary feature strength; and generate the final boundary set as the preliminary boundary according to the boundary tracking function;

[0014] Step 1.3: Based on the preliminary boundaries, divide the 3D working base map into several different natural resource areas.

[0015] Furthermore, the process of calculating the multispectral characteristics of the three-dimensional working base map in step 1.1 specifically includes: obtaining spectral data from the three-dimensional working map, wherein the spectral data specifically includes: near-infrared band reflectance, red band reflectance, short-infrared band reflectance, green band reflectance, and blue band reflectance; and then calculating the multispectral characteristics of the three-dimensional working base map using the following formula:

[0016] ;

[0017] in, Indicates the coordinate position is Multispectral characteristic value of the pixel at ; Indicates the X-axis coordinate; Indicates the Y-axis coordinate; Indicates the Z-axis coordinate; is the surface temperature; is the surface emissivity; is the reflectivity in the near-infrared band; is the reflectivity of red light band; is the reflectivity in the short infrared band; is the reflectivity of green light band; is the reflectivity of blue light band; The upper limit of the wavelength of remote sensing satellites; is the lower limit of the wavelength of remote sensing satellites; is the wavelength integration variable; is the spectral reflectance distribution function, which means the wavelength is Spectral reflectance at .

[0018] Furthermore, the spectral reflectance distribution function Calculated by the following formula:

[0019] ;

[0020] in, is the radiance value of the remote sensing satellite; is the atmospheric radiation path value, and the calculation formula is:

[0021] ;

[0022] in, is the Rayleigh scattering reflectivity; is the aerosol scattering reflectivity; is the observation zenith angle; and The atmospheric transmittance in the observation direction and the sun direction respectively: is the solar spectrum irradiance, and the calculation formula is:

[0023] ;

[0024] in, is the solar constant spectral distribution; is the mean distance between the Earth and the Sun; is the actual distance between the sun and the earth; is the atmospheric diffuse irradiance, and the calculation formula is:

[0025] ;

[0026] in, is the atmospheric diffuse transmittance; is the average surface reflectivity; is the albedo of the large balloon surface.

[0027] Furthermore, the process of calculating the local feature tensor of each pixel in the three-dimensional working base map based on the multispectral features in step 1.1 specifically includes:

[0028] ;

[0029] in, Indicates the coordinate position is The local eigenvector of the pixel at ; is the radius of curvature of the earth's surface; is the set reference curvature radius.

[0030] Furthermore, in step 1.2, the boundary feature intensity of the three-dimensional working base map is calculated based on the multispectral features and the local feature tensor of each pixel using the following formula:

[0031] ;

[0032] in, Indicates the coordinate position is The boundary feature intensity of the pixel at ; for gradient; is the absolute value operator.

[0033] Furthermore, in step 1.2, a boundary tracking function is constructed based on the boundary feature strength using the following formula:

[0034] ;

[0035] in, is the boundary tracking value; if Within the set threshold range, the coordinates are located at The pixel with coordinates is located at The adjacent pixels are connected to form a boundary segment; when all the pixels are connected, all the boundary segments are obtained, and each boundary segment is regarded as a boundary vector; is the geodesic curvature; is the normal curvature; is the normalized height value set; Indicates the coordinate position is The boundary feature intensity of the pixel at ; Indicates the coordinate position is The boundary feature intensity of the pixel at ; Indicates the coordinate position is Multispectral characteristic value of the pixel at ; Indicates the coordinate position is Multispectral characteristic value of the pixel at ; The coordinates are located at The pixel with coordinates is located at The distance between adjacent pixels.

[0036] Furthermore, in step 1.2, the final boundary set is generated according to the boundary tracking function. The process of the preliminary boundary specifically includes: among all boundary vectors, if the starting coordinates or the ending coordinates of any boundary vector are the same as the starting coordinates or the ending coordinates of any boundary vector, the two boundary vectors are summarized as one boundary. After traversing all boundary vectors, several boundaries are obtained; if the starting coordinates or the ending coordinates of any boundary vector are different from the starting coordinates or the ending coordinates of any other boundary vector, it is regarded as an isolated boundary and discarded; each retained boundary is taken as an element to generate the final boundary set.

[0037] Utilizing the above technical solutions, the present invention achieves the following beneficial effects: The multispectral feature analysis method effectively extracts the unique characteristics of different natural resource types in spectral space, such as the significant differences in reflectance between forests and grasslands in the near-infrared and green bands. Furthermore, by introducing a local feature tensor, the present invention is able to capture subtle variations in multispectral features in three-dimensional space. In particular, at resource boundaries, the gradient of the tensor provides a highly accurate description of boundary characteristics. Combined with a boundary tracking function, the present invention further optimizes the consistency and accuracy of boundary extraction, thereby ensuring highly accurate mapping results. For example, in complex terrain areas (such as mountains or hills), traditional methods often lead to broken or misjudged boundaries due to varying elevations. This present invention successfully overcomes this problem by employing a curvature correction factor and elevation normalization techniques. The final boundary set generation process removes isolated and redundant boundary segments through merging and filtering operations, further improving the integrity and accuracy of the mapping results. The present invention utilizes a combination of boundary feature strength and a boundary tracking function for boundary detection, addressing the issues of insufficient boundary detection accuracy and poor continuity in existing techniques. Boundary feature intensity, through a normalized combination of the determinant of the local feature tensor and the spectral gradient, not only accurately captures the significance of resource boundaries but also reflects their directional characteristics. This method can precisely locate resource boundaries in complex terrain, avoiding the boundary blurring caused by sudden elevation changes or insufficient spectral variation in traditional methods. Furthermore, the boundary tracking function further improves the consistency of boundary tracking results by introducing curvature correction and a height normalization factor. The resulting boundary set is then merged and isolated boundary removal is performed, ensuring geometric continuity and consistency, providing a reliable boundary foundation for resource ownership confirmation. The presence of isolated boundaries is a challenge in traditional mapping methods. This invention, through a comprehensive traversal and filtering mechanism, effectively removes isolated boundaries, ensuring that the resulting boundary set contains only those boundaries that are meaningful for resource demarcation. This filtering mechanism detects whether the start and end points of boundary vectors are connected to other boundaries and removes isolated boundaries, thereby improving the overall quality of the boundary set. Furthermore, by merging adjacent boundary vectors, the present invention further optimizes the compactness of the boundary set, making the mapping results clearer and more concise, providing efficient data support for resource classification and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic diagram of the method flow of an intelligent mapping method for natural resource rights confirmation based on a three-dimensional working base map provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] All features disclosed in this specification, or all steps in the disclosed methods or processes, except mutually exclusive features and / or steps, can be combined in any manner.

[0040] Any feature disclosed in this specification (including any appended claims and abstract), unless otherwise stated, may be replaced by other equivalent or similar features. In other words, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

[0041] Example 1: Reference Figure 1 , a natural resource rights confirmation intelligent mapping method based on a three-dimensional working base map, the method comprising:

[0042] Step 1: Detect and identify natural resources on a 3D working base map to generate preliminary boundaries. Based on these boundaries, the 3D working base map is divided into multiple natural resource areas. The 3D working base map is created using image data captured by remote sensing satellites and geographic data acquired by a GIS system.

[0043] The construction of a 3D working basemap is based on remote sensing satellite imagery. Multispectral and hyperspectral imagery extracts surface feature information, such as vegetation, water bodies, and bare land. This information, based on differences in spectral reflectance, allows for a clearer separation of different types of features. Furthermore, the resolution, texture characteristics, and multi-temporal variation of remote sensing imagery provide crucial insights for dynamic monitoring. Furthermore, the introduction of a GIS system further enhances the spatial accuracy of the basemap. GIS not only integrates geographic coordinates and topological relationships but also comprehensively models terrain elevation data using digital elevation models (DEMs) and digital surface models (DSMs), expanding 2D image information into 3D models and enabling visualization of terrain undulations, slopes, and elevation changes. This deep fusion of multi-source data gives the 3D working basemap unparalleled advantages when handling complex terrain, significantly improving the ability to identify and classify natural resources in complex terrain. Based on the constructed 3D working basemap, the present invention applies advanced image processing and machine learning technologies to detect and identify natural resources. These technologies use automated analysis processes to classify and label natural resource types within the basemap. In specific implementations, deep learning algorithms, such as convolutional neural networks (CNNs), are used to extract and classify features from remote sensing imagery. These features include the texture, shape, and spectral reflectance characteristics of land features. Through multi-layer feature extraction and learning, the CNN model can accurately distinguish different natural resource types, such as water bodies, forests, and farmland. Furthermore, based on the elevation information in the 3D basemap, natural resource classification can be further refined. For example, for vegetation types, elevation information can be used to distinguish between trees and shrubs; for water types, the direction and extent of the water system can be determined based on topographic relief. This classification approach, combining 2D imagery with 3D elevation, not only significantly improves detection accuracy but also provides a more scientific basis for the demarcation of natural resource ownership. Another key step in natural resource detection in this invention is the automatic generation of boundaries. Using image segmentation technology, boundary lines for different types of natural resources can be extracted from the 3D working basemap. These boundary lines are delineated based on feature variations in the remote sensing imagery and the spatial distribution in the GIS data, ensuring that their positions are consistent with surface texture characteristics and maintain reasonable continuity in the elevation dimension. For example, at the interface between water and vegetation, the system can quickly generate preliminary boundary lines based on spectral differences, while also incorporating elevation data to eliminate misjudgments caused by image shadows or reflections, resulting in more accurate segmentation results. This automated boundary generation method reduces manual intervention and improves mapping efficiency, a particularly significant advantage in large-scale regional natural resource rights verification tasks.

[0044] Step 2: Perform terrain elevation conflict detection in each natural resource area to obtain the terrain elevation conflict line in each natural resource area, and redivide the natural resource areas with terrain elevation conflicts according to the terrain elevation conflict lines;

[0045] Step 2 detects and subdivides terrain elevation conflicts within the natural resource area, ensuring consistency in three-dimensional space and providing accurate and scientific demarcation results for subsequent land rights confirmation and mapping. This process, built on the robust data support of a 3D working basemap, systematically addresses potential sudden elevation changes or inconsistencies within the area through in-depth analysis of the elevation characteristics of the natural resource area. The core principle is to detect elevation conflicts within the area and, based on these conflicts, generate elevation conflict lines to refine the natural resource area. This process not only improves the accuracy of natural resource area demarcation but also addresses the problem of demarcation errors caused by the lack of elevation information in traditional 2D mapping methods. The 3D working basemap plays a key role in this step. The elevation data it contains not only reflects the surface topography but also provides a comprehensive description of elevation variations within the area. After the initial demarcation of the natural resource area is completed, each area may include complex terrain features, such as hills, mountains, or plains. In traditional 2D mapping, these elevation variations are often ignored, resulting in the area demarcation not accurately reflecting the actual geographical conditions. However, the present invention can accurately capture elevation mutations within a region by performing terrain elevation conflict detection on each natural resource area. This elevation mutation is usually manifested as a significant difference in elevation between different locations within the region, such as an area containing both ridges and valleys, or an area with an elevation span that is too large, exceeding the reasonable range of natural resource classification. By detecting these conflict points, the present invention can effectively identify elevation anomalies within the region and provide a scientific basis for subsequent detailed division. The detection of terrain elevation conflicts relies on in-depth analysis of elevation data. In the three-dimensional working base map, the elevation data of each natural resource area is extracted and statistically analyzed, including parameters such as the minimum, maximum, and average elevation values and the rate of elevation change. These parameters comprehensively reflect the elevation characteristics within the region and provide a basis for judging elevation conflicts. For example, when the rate of elevation change of a natural resource area is significantly higher than the average, this indicates that there may be a large elevation mutation in the area. At the same time, by analyzing the continuity of the elevation distribution within the region, the specific location and range of the elevation mutation can be further identified. This elevation analysis method utilizes the spatial stereo information of the three-dimensional working base map, and can detect elevation conflicts with higher accuracy and reliability, thus providing support for subsequent detailed division.

[0046] Once elevation conflicts are detected within a natural resource area, the present invention generates elevation conflict lines based on elevation mutation points and uses these conflict lines to subdivide the original area. The generation of elevation conflict lines is based on an analysis of the gradient of elevation data changes. Specifically, the system searches for points along the elevation change trajectory within the area where the rate of elevation change exceeds a set threshold and connects these points into lines to form complete elevation conflict lines. Such conflict lines typically correspond to significant boundaries in the actual terrain, such as ridgelines, valley lines, or scarp boundaries. These terrain boundaries are not only important features of natural landforms, but also potential dividing lines between different types of natural resources. For example, a ridgeline may separate a forest area from a grassland area, while a valley line may mark the watershed boundary of different water bodies. By generating conflict lines based on elevation data, the present invention can scientifically handle areas with complex terrain and provide a basis for the rational division of natural resources. During the division process, the system not only focuses on the consistency of elevation within the area, but also considers the integrity and rationality of the divided area. For example, a natural resource area may be divided into multiple sub-areas due to elevation conflicts, but these sub-areas still need to meet the requirements of relative consistency in elevation, and also maintain clear boundaries with other adjacent areas. This division method effectively avoids the problem of blurred boundaries or division errors caused by complex terrain in traditional methods. In addition, through the dynamic adjustment of elevation conflict lines, the present invention can adapt to the specific needs of different natural resource types. For example, for forest areas, division may pay more attention to changes in slope and altitude, while for agricultural land, it may pay more attention to terrain flatness and drainage conditions. This dynamic adaptability makes the division results more in line with the actual natural resource characteristics and management needs.

[0047] Step 3: Determine an upper limit for the natural resource area. For each natural resource area, calculate the average elevation difference between it and adjacent natural resource areas of different types. If the average elevation difference is within the set threshold, the two natural resource areas will be merged into one natural resource area. The area of the merged natural area must be smaller than the determined upper limit for the natural resource area. The average elevation difference is defined as the absolute value of the difference between the average altitude of one natural resource area and the average altitude of another natural resource area.

[0048] The average elevation difference (AED) is a key metric for measuring the vertical consistency between two adjacent natural resource areas. The core of this approach is to use elevation data from a 3D working basemap to calculate the average elevation value for each area, then compare the average elevation differences between the two adjacent areas. The elevation data in the 3D working basemap is highly accurate and fully reflects the topographical undulations and vertical variations of the natural resource area. Therefore, the average elevation difference calculated based on AED is not only mathematically reliable but also highly correlated with the actual distribution characteristics of the natural resources. Using this metric, the present invention can scientifically assess whether two natural resource areas have the potential for merging based on their topographic characteristics. For example, if two adjacent natural resource areas appear independent in plan view but their AED falls within a set threshold, it can be inferred that they may belong to the same type of natural resource or be amenable to merging. This assessment overcomes the shortcomings of traditional methods that divide areas based solely on planar distribution and texture features by incorporating vertical information into the rules for area merging, resulting in a more comprehensive and scientific mapping of natural resources. Setting a threshold plays a key role in the area merging process. This threshold reflects the allowable range of AED, essentially reflecting the acceptable degree of vertical consistency between natural resource areas. The threshold setting needs to be dynamically adjusted based on the specific natural resource type and geographical environment. For example, in flat agricultural areas, the threshold can be lower to ensure that only areas with very similar elevations are merged; in mountainous or hilly areas, the threshold can be appropriately relaxed to accommodate the undulating characteristics of the terrain. By dynamically adjusting the threshold, the present invention can adapt to the mapping needs of different natural resource types, making regional merging both scientific and reasonable, and highly flexible. This merging principle based on elevation difference thresholds, combined with the precise elevation data of the three-dimensional working base map, makes the regional division of natural resources more consistent with actual geographical conditions and can significantly improve the accuracy and reliability of land rights confirmation. In addition to the average elevation difference, the present invention also introduces the concept of an upper limit on the area of natural resource regions to constrain the results of regional merging. The setting of an upper limit on the area is based on the actual needs of resource management and land rights confirmation accuracy. Excessively large natural resource areas can not only lead to reduced mapping accuracy, but also bring difficulties to subsequent management and land rights confirmation work. By setting a reasonable upper limit on the area, the present invention can ensure that even if multiple natural resource areas meet the merging conditions, the area of the merged area will not exceed the management requirements. For example, in some nature reserves, the size of individual areas may need to be controlled within certain limits to facilitate ecological monitoring and resource management. In land rights confirmation, setting an area cap can effectively prevent the emergence of large, non-compliant plots, thereby improving the practicality and scientific nature of land rights confirmation. The area cap, combined with the elevation difference threshold, provides a scientific constraint framework for area merging, ensuring that the merged areas not only meet topographic consistency requirements but also meet practical management needs.The process of regional merging is an intelligent optimization process based on three-dimensional spatial information. Its goal is to optimize the division of natural resource regions by comprehensively considering the consistency of elevation and rationality of area of adjacent regions. In actual operation, the present invention accurately locates the boundaries and elevation data of adjacent natural resource regions through a three-dimensional working base map, and then uses an algorithm to calculate the average elevation difference and the area after merging. When the average elevation difference between two natural resource regions is within the set threshold range and the area of the merged region is less than the upper limit of the area, the system will automatically complete the merging of the regions. This intelligent merging method not only reduces the need for manual intervention, but also significantly improves the mapping efficiency and the accuracy of the results. Through such an optimization process, the present invention can complete the intelligent division of large-scale natural resources in a short period of time, providing reliable technical support for the implementation of property rights confirmation work.

[0049] In existing technologies, remote sensing satellite data serves as the fundamental source of three-dimensional working base maps. Images of the Earth's surface are acquired through multispectral and hyperspectral sensors carried on satellites. These image data cover information such as the spectral reflectance characteristics of the Earth's surface, terrain texture, and the distribution of land features. Through the different orbit designs and resolution settings of remote sensing satellites, high-precision image data of different times and geographical areas can be obtained. The core advantages of remote sensing data lie in its macroscopic and real-time nature. It can cover a large area of the Earth's surface in a relatively short time interval, providing rich data support for dynamic monitoring and terrain modeling. However, raw image data is often affected by factors such as the Earth's curvature, sensor distortion, and atmospheric scattering. Therefore, it needs to undergo a series of preprocessing steps, including radiometric correction, atmospheric correction, geometric correction, and orthorectification, to ensure that the data truly reflects the actual characteristics of the geographic space.

[0050] GIS systems, by acquiring geospatial data, provide key information for 3D basemaps, including geographic coordinates, terrain elevation, and topological relationships. Terrain elevation data typically comes from digital elevation models (DEMs) or digital surface models (DSMs), which describe the surface's undulating characteristics. DEMs are typically acquired through LiDAR (Light Detection and Ranging) technology or photogrammetry, which analyzes multiple overlapping images and extracts elevation information based on stereo geometry. DEMs store elevation values for each geographic location in a regular grid or triangulated network, providing the core elevation support for basemap modeling. GIS systems not only store this terrain elevation data but also integrate it with vector data (such as roads, water systems, and building boundaries) and attribute data (such as land use types and geological features), providing a multi-dimensional information foundation for 3D modeling. During the data integration phase, remote sensing imagery and GIS data are fused through spatial overlay and coordinate unification. Because remote sensing and GIS data often come from different sources and may use different coordinate reference systems, coordinate system conversion and registration are required. The goal of registration is to ensure that every pixel in the remote sensing imagery corresponds exactly to a geospatial point in the GIS data. After completing spatial matching, the fusion stage combines the texture and spectral information of the remote sensing image with the elevation and attribute information of the GIS data to generate a basic three-dimensional model.

[0051] To achieve three-dimensional visualization, the fused data requires further processing. Texture information from remote sensing images is typically mapped onto a terrain elevation model (such as a DEM or DSM), a process known as texture mapping. Through texture mapping, the spectral characteristics of the surface, combined with elevation information, form a visual representation of the three-dimensional terrain. Complex terrain features (such as buildings and vegetation) can be refined by combining vector data with three-dimensional modeling techniques. For example, by constructing three-dimensional building models (CityGML) or vegetation cover models, the basemap becomes more realistic and practical. Furthermore, to improve the applicability of the basemap, existing technologies often use multi-resolution representations of three-dimensional models to support application scenarios with varying precision requirements. For example, in large-scale terrain analysis, a low-resolution model can be used to reduce computational burden, while fine-grained regional modeling requires switching to a high-resolution mode to obtain more accurate geographic information. This multi-resolution representation is achieved through techniques such as mesh simplification algorithms, quadtree segmentation, or viewpoint-dependent rendering.

[0052] Example 2: In step 2, the specific process of performing terrain elevation conflict detection in each natural resource area includes: obtaining a contour distribution map of each natural resource area from a three-dimensional working base map; in the contour distribution map, if the height distance difference between any two adjacent contour lines exceeds a set threshold, it is judged that there is a terrain elevation conflict, and the contour line with the lower height of the two adjacent contour lines is used as the terrain elevation conflict line; the types of natural resource areas include: rivers, wetlands, forests, grasslands, cultivated land and deserts.

[0053] Specifically, the 3D working base map, as a comprehensive representation of geographic information, includes a digital elevation model (DEM) that provides the data foundation for generating contour maps. In practice, the system extracts elevation data from the DEM and generates contour maps of natural resource areas at predefined contour intervals. These contour lines represent horizontal slices of the surface at specific elevation intervals, providing a visual representation of topographic undulations. For example, areas with dense contour lines typically represent steep slopes or large elevation differences, while areas with sparse contour lines reflect flat terrain or less elevation variation. These characteristics make contour lines a key tool for analyzing terrain elevation conflicts. After the contour map is generated, the system analyzes the elevation difference between each pair of adjacent contour lines. Specifically, the system calculates the difference between the elevation values corresponding to two adjacent contour lines and compares this difference with a preset threshold. This threshold reflects the allowable elevation variation within the natural resource area. For example, in flat areas like farmland or wetlands, the threshold may be lower to ensure the precision of the segmentation results; in areas with large terrain fluctuations, such as forests or deserts, the threshold can be appropriately relaxed to adapt to the actual terrain conditions. If the height difference between two adjacent contour lines exceeds this threshold, the system determines that there is a terrain elevation conflict in that area.

[0054] In order to further clarify the location and scope of the conflict, the system marks a lower contour line as the terrain elevation conflict line. This choice is based on the characteristics of natural resources and topographic principles, because areas with lower altitudes are usually more able to reflect the dividing points of the terrain. For example, the bottom of a river or valley is often the point where the conflict appears. By clearly marking the terrain elevation conflict line, the present invention can accurately locate the conflict area, thereby providing a basis for the subsequent detailed division of natural resource areas. The type of natural resources also has an important impact on the detection rules of terrain elevation conflicts. In different types of natural resource areas, the elevation characteristics and conflict manifestations are different. For example, in river areas, contour lines may appear parallel and relatively dense, and conflict points often appear near river valleys; in wetland areas, contour lines are usually sparse and evenly distributed, and conflict points may be related to local terrain uplift; in forest and grassland areas, the range of elevation changes may be larger, and conflict points are mostly concentrated on ridge lines or places where the slope changes suddenly. Cultivated land is typically distributed on relatively flat terrain, so elevation conflicts are often associated with artificially reclaimed slopes or landform modification. Elevation conflicts in desert areas can be related to wind-eroded landforms or the distribution of sand dunes. These resource-specific detection rules enable the method to adapt to diverse natural resource environments while also demonstrating high detection accuracy and practicality in each environment.

[0055] Example 3: Step 1 specifically includes:

[0056] Step 1.1: Calculate the multispectral features of the 3D working base map; based on the multispectral features, calculate the local feature tensor of each pixel in the 3D working base map;

[0057] Step 1.2: Based on the multispectral features and the local feature tensor of each pixel, calculate the boundary feature strength of the 3D working base map; construct a boundary tracking function based on the boundary feature strength; and generate the final boundary set as the preliminary boundary according to the boundary tracking function;

[0058] Step 1.3: Based on the preliminary boundaries, divide the 3D working base map into several different natural resource areas.

[0059] Specifically, in step 1.1, the multispectral features of the 3D working basemap are calculated using multispectral or hyperspectral data from remote sensing satellite imagery. Multispectral data provides surface reflectance information across different wavelengths. Different natural resource types (such as forests, cultivated land, and grasslands) exhibit significant differences in their multispectral characteristics. For example, vegetation typically has higher reflectance in the near-infrared band, while water has lower reflectance in the visible band. These multispectral features are converted into spatial representations using specific algorithms. For example, spectral indices (such as the Normalized Difference Vegetation Index (NDVI)) can visually display vegetation coverage. To further improve spatial resolution and feature representation, this step also calculates a local feature tensor for each pixel in the 3D working basemap. The local feature tensor is a mathematical representation that describes the spatial relationship around a pixel. It integrates spectral and spatial texture features. By calculating the difference between each pixel and its surrounding pixels in multispectral space, it can capture subtle local variations in the surface. This tensor representation effectively enhances boundary clarity, particularly at the intersection of different natural resource types. Next, in step 1.2, the boundary feature intensity of the 3D working base map is further calculated based on the multispectral and local feature tensors calculated in the previous step. Boundary feature intensity is a metric used to measure the degree of feature difference between pixels. Its purpose is to locate locations where significant changes in natural resource types occur. Calculation methods typically include gradient analysis or edge detection algorithms. For example, by calculating the gradient amplitude of the multispectral features, an intensity map can be obtained that reflects the location of boundaries. Higher boundary feature intensity indicates that the location may be the boundary between different natural resource types. After obtaining the boundary feature intensity, a boundary tracking function is constructed based on its value. The boundary tracking function connects possible boundary points in the intensity map into a continuous boundary line. This function combines global optimization and local tracking strategies to accurately extract boundaries in areas with drastic intensity changes, while avoiding boundary breakage or shifts caused by noise or local anomalies. Finally, by solving the boundary tracking function, a preliminary boundary set is generated, identifying the boundaries between different natural resource types. Finally, in step 1.3, the 3D working base map is divided into multiple natural resource areas based on this preliminary boundary set. The process of regional division is based on the spectral consistency and spatial connectivity of natural resource types, ensuring that pixels within the same area have high similarity in spectral characteristics, while forming a complete connected area in space. For example, a divided forest area may cover a plot of land covered with dense vegetation, while its boundary is clearly distinguished from the adjacent grassland or cultivated land. Through this boundary feature-based division method, the present invention can realize the automatic identification and preliminary division of natural resource areas, laying an important foundation for subsequent terrain elevation conflict detection and regional merging.

[0060] Example 4: The process of calculating the multispectral characteristics of the three-dimensional working base map in step 1.1 specifically includes: obtaining spectral data from the three-dimensional working map, the spectral data specifically including: near-infrared band reflectivity, red light band reflectivity, short infrared band reflectivity, green light band reflectivity, and blue light band reflectivity; and then calculating the multispectral characteristics of the three-dimensional working base map using the following formula:

[0061] ;

[0062] in, Indicates the coordinate position is Multispectral characteristic value of the pixel at ; Indicates the X-axis coordinate; Indicates the Y-axis coordinate; Indicates the Z-axis coordinate; is the surface temperature; is the surface emissivity; is the reflectivity in the near-infrared band; is the reflectivity of red light band; is the reflectivity in the short infrared band; is the reflectivity of green light band; is the reflectivity of blue light band; The upper limit of the wavelength of remote sensing satellites; is the lower limit of the wavelength of remote sensing satellites; is the wavelength integration variable; is the spectral reflectance distribution function, which means the wavelength is Spectral reflectance at .

[0063] Specifically, the formula (near-infrared reflectivity) and (Red light band reflectivity) combination This reflects the basic principle of the vegetation index. The near-infrared band is highly sensitive to vegetation, and areas with dense vegetation have a higher reflectivity in this band, while the reflectivity of the red band reflects more about the health and density of vegetation. By calculating the difference between the near-infrared and red light reflectivity and normalizing them, it is possible to effectively distinguish high-density vegetation from other surface types, such as water bodies or bare land. The introduction of further enhances the sensitivity to soil moisture and vegetation moisture content, making the combination not only able to identify vegetation coverage, but also to distinguish different types of vegetation and soil conditions. Utilizes the green light band With blue light band The spectral characteristics of surface vegetation are captured by using the reflectance ratio of the green band to the blue band. The green band is generally closely related to the greenness and health of vegetation, while the blue band is significantly affected by atmospheric scattering and water reflection. This ratio calculation effectively suppresses the effects of atmospheric interference and water reflection on vegetation detection, thereby improving the accuracy of vegetation boundary detection. This exponential function not only enhances the nonlinear expression of features but also further strengthens the distinction between different natural resource types through ratio operations, allowing the formula to maintain high-precision recognition capabilities even in complex environments.

[0064] Integral Item Represents the integral of the spectral reflectance distribution function within the wavelength range of the remote sensing satellite. This part provides the overall response characteristics of the surface material to different wavelength spectra by accumulating the reflectance within the entire wavelength range. This integral term not only integrates the information of multiple bands, but also further enhances the recognition ability of complex surface types through the overall analysis of the spectral reflectance. For example, in diverse natural resource areas such as forests, grasslands and cultivated land, the overall distribution patterns of spectral reflectance are different. These differentiated features can be effectively extracted through integral operations, thereby achieving more accurate resource classification and boundary demarcation. Finally, the surface temperature and surface emissivity combination of Thermal infrared information is introduced to reflect the thermal characteristics of the surface. The combination of surface temperature and emissivity not only reveals the thermal inertia and heat dissipation characteristics of surface materials, but also helps distinguish different types of natural resources. For example, water bodies usually exhibit lower surface temperatures due to their high specific heat capacity and low emissivity, while urban buildings and bare land may exhibit higher surface temperatures. By incorporating these thermal characteristics into the calculation of multispectral features, the formula not only expands the dimensionality of multispectral features, but also enhances the ability to distinguish different types of natural resources, thereby improving the accuracy and reliability of natural resource boundary detection.

[0065] Example 5: Spectral reflectance distribution function Calculated by the following formula:

[0066] ;

[0067] in, is the radiance value of the remote sensing satellite; is the atmospheric radiation path value, and the calculation formula is:

[0068] ;

[0069] in, is the Rayleigh scattering reflectivity; is the aerosol scattering reflectivity; is the observation zenith angle; and The atmospheric transmittance in the observation direction and the sun direction respectively: is the solar spectrum irradiance, and the calculation formula is:

[0070] ;

[0071] in, is the solar constant spectral distribution; is the mean distance between the Earth and the Sun; is the actual distance between the sun and the earth; is the atmospheric diffuse irradiance, and the calculation formula is:

[0072] ;

[0073] in, is the atmospheric diffuse transmittance; is the average surface reflectivity; is the albedo of the large balloon surface.

[0074] Specifically, the formula The core principle is to use the radiance observed by remote sensing satellites at the top of the atmosphere , by deducting the atmospheric path radiation The influence of the sun's irradiance and the atmospheric transmittance is combined to calculate the surface's reflection characteristics for light of a specific wavelength. Atmospheric path radiation is caused by Rayleigh scattering and aerosol scattering, which are interference factors that cannot be ignored in remote sensing observations. The calculation formula shows that atmospheric path radiation is related to The relationship between the two is controlled by the Rayleigh scattering reflectivity and the aerosol scattering reflectivity, which are respectively expressed in the formula and The combination of the two reflects how particles and air molecules in the atmosphere affect the transmission and scattering of solar radiation. By correcting the atmospheric path radiation, the surface contribution can be extracted from the total radiation to ensure the accuracy of the spectral reflectance calculation. Next, the calculation of solar irradiance further reflects the dynamic radiation relationship between the earth and the sun. Solar irradiance Spectral distribution of the solar constant This correction is made by taking into account the elliptical shape of the Earth's orbit, which causes the Earth-Sun distance to change over time. , which can adjust for differences in solar radiation intensity caused by distance changes, thereby obtaining the actual solar irradiance. This processing ensures that surface radiation data observed by remote sensing satellites at different times are comparable, avoiding errors introduced by natural variations in solar radiation intensity.

[0075] In addition, the atmospheric diffuse irradiance The calculation formula describes the diffuse behavior of solar radiation when it passes through the atmosphere. Diffuse radiation refers to the radiation part of sunlight that is scattered in all directions by atmospheric molecules. The atmospheric diffuse transmittance is introduced into the formula. , average surface reflectivity and the large balloon surface albedo , which together describe the complex radiation exchange relationship between the atmosphere and the surface. In particular represents the average reflectance characteristics of the surface, and It describes the reflectivity of the atmosphere itself. These parameters make the calculation of diffuse radiation more consistent with the actual atmospheric and surface conditions, ensuring the accuracy of the final reflectivity calculation. Finally, the transmittance parameter in the formula and The transmittance in the direction of observation and the direction of the sun is respectively. Transmittance is a key factor in remote sensing data correction. It reflects the degree of attenuation of radiation due to molecular absorption, scattering, etc. when passing through the atmosphere. Transmittance in the direction of observation Combined with observation zenith angle To describe the attenuation of radiation in the transmission path, and the transmittance in the direction of the sun It shows the loss of solar radiation after entering the Earth's atmosphere before reaching the Earth's surface. These transmittance parameters directly affect the denominator in the spectral reflectance calculation, ensuring that the corrected data accurately reflects the actual spectral characteristics of the Earth's surface.

[0076] Example 6: The process of calculating the local feature tensor of each pixel in the three-dimensional working base map based on the multispectral features in step 1.1 specifically includes:

[0077] ;

[0078] in, Indicates the coordinate position is The local eigenvector of the pixel at ; is the radius of curvature of the earth's surface; is the set reference curvature radius.

[0079] Specifically, the basis of the formula is the characteristic tensor matrix , whose structure is composed of multispectral eigenvalues These partial derivatives describe the pixel in different spatial directions ( ) and their mutual correlation. Each item in the feature tensor matrix, such as , indicating that the pixel is The second-order change in direction, that is, the acceleration characteristic of the local change. Similarly, the cross terms (such as ) reflects the joint changes between the two directions. This tensor representation can capture subtle changes in multispectral features in three-dimensional space, especially at the junction of natural resource types, where the gradient of feature changes tends to increase significantly. By calculating these second-order derivatives, the feature tensor matrix describes the local characteristics of the area where the pixel is located, including smooth areas, boundary areas, and mutation points. For example, in areas with flat terrain, the diagonal terms of the tensor matrix (that is, the values of the second-order partial derivatives in a single direction) are close to zero, while in areas with steep boundaries or significant changes in natural resource types, these values will increase rapidly. In addition, the cross terms can further capture the synergy of multi-directional changes, providing a more comprehensive description for boundary detection of complex terrain or natural resource types.

[0080] Curvature modulation factor The influence of surface geometry information is introduced into the formula, where is the radius of curvature of the earth's surface, is the reference curvature radius. The curvature radius reflects the degree of curvature of the surface at a specific point. A large curvature radius corresponds to a relatively flat terrain, while a small curvature radius indicates a steep area or an area with a sharp change in height. Through this exponential function, the value of the local eigenvalue tensor matrix will be nonlinearly adjusted according to the magnitude of the curvature. In flat areas ( When the coefficient of variation is large, the modulation factor approaches 1 and has little effect on the characteristic tensor; in areas with large curvature (such as ridges or valleys), the modulation factor decreases rapidly, thereby amplifying the value of the characteristic tensor matrix. This nonlinear correction makes the formula more sensitive to complex terrain and can more accurately describe local features. The final result of the characteristic tensor matrix is a comprehensive local descriptor, which not only captures the subtle changes in pixels in multispectral space, but also combines the surface curvature characteristics, enabling it to effectively distinguish different types of natural resources and complex terrain features. For example, at the junction of forest and grassland, the gradient of the characteristic tensor changes significantly, while in the transition zone between wetland and cultivated land, its change is relatively gentle. This detailed descriptive capability provides important data support for the intelligent mapping of natural resource rights.

[0081] Example 7: In step 1.2, the boundary feature intensity of the three-dimensional working base map is calculated based on the multispectral features and the local feature tensor of each pixel using the following formula:

[0082] ;

[0083] in, Indicates the coordinate position is The boundary feature intensity of the pixel at ; for gradient; is the absolute value operator.

[0084] Specifically, the determinant of the local feature tensor is It is the core part of the formula. Local feature tensor It is a matrix composed of the second-order partial derivatives of multispectral features, each component of which describes the change characteristics of the pixel in a certain direction in three-dimensional space. For example, Indicates that the pixel is The local curvature characteristics of the direction, and the cross terms such as It describes and The determinant calculation compresses the overall information of the tensor matrix into a scalar value, the size of which reflects the comprehensive intensity of the local feature changes. When the pixel is located at the boundary of natural resources, the value of the feature tensor will increase significantly due to the drastic change of the multispectral features at the boundary, resulting in a larger determinant value. By taking the cubic root of the determinant The formula nonlinearly scales the determinant, resulting in a smoother distribution of feature intensities. This processing not only suppresses unusual spikes caused by noise but also enhances responsiveness to large-scale boundary changes. For example, at the border between forest and grassland, the determinant of the tensor matrix increases significantly due to the significant difference in their multispectral characteristics. The cubic root operation can adjust this variation to a range suitable for subsequent boundary detection.

[0085] Secondly, the gradient part of the formula It is the gradient of the multispectral feature, which represents the multispectral feature value of the pixel The direction and intensity of the change in space. The size of the gradient It reflects the change amplitude of the pixel in space, and the gradient direction points to the direction of the fastest change. In the formula, through the normalization operation , the directional information of the gradient is extracted, so that the feature intensity can highlight the position and direction of the boundary. The role of gradient normalization is to enhance the expression of directionality, so that the boundary feature can not only describe the intensity, but also reflect the location attribute of the boundary. For example, when the gradient direction is perpendicular to the boundary, its normalized result will significantly enhance the boundary feature intensity, while when the gradient direction is parallel to the boundary, the feature intensity is relatively weak. This characteristic enables the formula to effectively distinguish boundary features of different directions, thereby achieving more accurate boundary detection in scenes with complex terrain or diverse natural resource types. Combining these two parts, the formula as a whole constructs a comprehensive boundary feature intensity descriptor. The determinant of the local feature tensor provides a global description of the boundary significance, while the normalization of the gradient introduces local directional information, so that the feature intensity can reflect both the intensity of the boundary change and the directionality of the boundary. For example, at the junction of wetlands and cultivated land, the multispectral feature values show significant changes in the gradient direction, and the value of the local feature tensor also increases due to the severity of this change, resulting in the boundary feature intensity. The formula also has strong noise immunity. Because the determinant operation can smooth out small-scale outliers and the gradient normalization operation can further reduce the impact of noise on directionality, the formula can still maintain high robustness when processing remote sensing data containing high noise or subtle disturbances. This feature is particularly important when dealing with complex terrain and diverse natural resource types, because natural resource boundaries are often accompanied by various interference factors, such as irregular changes in surface reflectivity or the influence of atmospheric conditions.

[0086] Example 8: In step 1.2, a boundary tracking function is constructed based on the boundary feature strength using the following formula:

[0087] ;

[0088] in, is the boundary tracking value; if Within the set threshold range, the coordinates are located at The pixel with coordinates is located at The adjacent pixels are connected to form a boundary segment; when all the pixels are connected, all the boundary segments are obtained, and each boundary segment is regarded as a boundary vector; is the geodesic curvature; is the normal curvature; is the normalized height value set; Indicates the coordinate position is The boundary feature intensity of the pixel at ; Indicates the coordinate position is The boundary feature intensity of the pixel at ; Indicates the coordinate position is Multispectral characteristic value of the pixel at ; Indicates the coordinate position is Multispectral characteristic value of the pixel at ; The coordinates are located at The pixel with coordinates is located at The distance between adjacent pixels.

[0089] Specifically, the beginning of the formula is a curvature combination term used to measure the local characteristics of the surface geometry where the pixel is located. represents the geodesic curvature, which reflects the curvature of the surface along the tangent direction around the pixel, and represents the normal curvature, which describes the curvature of the surface along the normal direction. The sum of the squares of the two reflects the comprehensive complexity of the geometric morphology of the area. The introduction of curvature in the boundary tracking function enables the system to sensitively capture areas where the surface curvature changes significantly, such as steep slopes, ridges or valleys, thereby enhancing the terrain adaptability of boundary detection. is the normalized height difference correction term, which is used to weaken the adverse effect of height difference on the correlation of boundary features. represents the vertical height difference between two pixels, and This is a preset normalization scale used to control sensitivity to height differences. Its exponential form ensures that small height differences have a minimal impact on boundary tracking, while the effects of large height differences are suppressed exponentially. This is particularly important when the boundary crosses areas of varying elevation, such as on hillsides or terraced terrain. By reducing the impact of height differences, the boundary continuity can be tracked more stably.

[0090] Then, the boundary feature strength term Describes the difference in boundary intensity between two pixels. This difference reflects the degree of significant change in boundary characteristics in a local area. The greater the intensity difference, the more significant the difference in boundary characteristics between the two pixels, and the larger the tracking function value, the more likely they are to be identified as part of the boundary. The combination of multi-spectral eigenvalues and gradients is introduced to further enhance the characterization of spectral feature similarity. Here, and is the multispectral gradient of two pixels, which represents the rate of change of spectral characteristics. The product of the absolute values of the gradients in the denominator reflects the comprehensive amplitude of the multispectral changes of the two pixels, while the product of the spectral eigenvalues in the numerator describes the spectral correlation of the pixels. Through this normalization process, the formula can dynamically adjust the sensitivity of the boundary tracking function to the spectral eigenvalues and gradients, thereby more accurately capturing the boundaries of different natural resource types. The integral operation in the formula globally quantifies the entire tracking path, and the path length Indicates that from arrive The integration ensures that boundary tracking can comprehensively consider all local characteristic changes on the path, rather than just the characteristics of the starting and ending points. Overall, the boundary tracking function comprehensively quantifies the boundary correlation between pixels by integrating geometric curvature, multispectral features, gradient information and height normalization correction. This multidimensional method that combines geometric and spectral features can achieve high-precision boundary detection and tracking in environments with complex terrain and diverse natural resource types. For example, at the junction of wetlands and cultivated land, due to the large spectral characteristic gradient of the two and the significant change in boundary intensity, the value of the tracking function will be higher, thereby accurately identifying the continuity of the boundary. After the calculation is completed, the system filters the boundary tracking value by setting a threshold. If If the threshold value is within the range, the two pixels are connected to form a boundary segment. After traversing all pixels, these boundary segments eventually form a complete boundary vector set, which provides the basis for the accurate demarcation and ownership confirmation of natural resource areas.

[0091] Example 9: In step 1.2, the final boundary set is generated according to the boundary tracking function. The process of the preliminary boundary specifically includes: among all boundary vectors, if the starting coordinates or end coordinates of any boundary vector are the same as the starting coordinates or end coordinates of any boundary vector, the two boundary vectors are summarized as one boundary, and after traversing all boundary vectors, several boundaries are obtained; if the starting coordinates or end coordinates of any boundary vector are different from the starting coordinates or end coordinates of any other boundary vector, it is regarded as an isolated boundary and discarded; each retained boundary is taken as an element to generate the final boundary set.

[0092] Specifically, all boundary vectors are traversed, and the coordinates of each boundary vector's start or end point are checked one by one to see if they match those of any other boundary vector. If two boundary vectors have the same start and end point, or if the end point of one boundary vector coincides with the start point of another, the two boundary segments are considered spatially continuous and should be merged into a longer boundary segment. The core of the merging operation is to combine these adjacent boundary vectors based on their spatial connectivity, thereby generating a larger and more complete boundary segment. This process is similar to splicing spatial paths. By checking the consistency of coordinate points, the boundary segments are ensured to be geometrically coherent and possible discontinuities are eliminated. However, when traversing all boundary vectors, some boundary vectors may not match their start and end points with any other boundary vectors. These vectors are considered isolated boundaries, often due to detection noise or local errors, and are therefore meaningless. The presence of isolated boundaries interferes with the clarity and practicality of the boundary set, and therefore they need to be removed from the final boundary set. The rules for removing isolated boundaries are very clear: the start and end points of each boundary vector are checked to see if they match other vectors. If not, they are discarded. This screening step effectively removes meaningless isolated boundaries, making the resulting boundary set more concise and practically valuable. After merging and removing isolated boundaries, the remaining boundary vectors constitute the final boundary set. Each boundary segment has a clear spatial location and continuity, fully describing the boundaries between different natural resource areas. These boundary sets provide a basic framework for preliminary delineation of natural resources, forming a boundary network in three-dimensional space that reflects the distribution patterns of natural resources. This process not only ensures geometric coherence but also accurately reflects the differences in multispectral characteristics and terrain elevation of natural resources. For example, at the junction of wetlands and grasslands, the generated boundary set accurately captures the transition zone between different vegetation types; at the junction of mountains and plains, the boundary set can reflect the changes in resource distribution caused by terrain elevation differences. This generation process is further optimized with the support of a 3D working basemap. The 3D working base map provides accurate terrain elevation and multispectral data, enabling the boundary tracing function to capture more detailed boundary characteristics, while the merging and filtering operations ensure the consistency and integrity of the generated boundary set in 3D space by combining 3D spatial coordinates. The final boundary set not only has clear boundaries on the 2D plane, but also reflects the distribution patterns of resources in 3D space, providing a scientific basis for the division of natural resources in complex terrain. Through this process, the final boundary set provides high-quality data support for the intelligent mapping of natural resource rights. These boundary sets can be used to define the preliminary boundaries of different resource areas, providing a basis for subsequent regional merging, optimization, and rights confirmation, while also ensuring the scientific and practical nature of the mapping results.In a complex natural resource environment, this method of generating boundary sets through traversal, merging and screening can effectively cope with diverse resource types and complex terrain conditions, and improve the accuracy and efficiency of natural resource division.

[0093] Although specific embodiments of the present invention have been described above, those skilled in the art will appreciate that these specific embodiments are merely illustrative, and that those skilled in the art may omit, substitute, and modify the details of the methods and systems described above without departing from the principles and spirit of the present invention. For example, combining the above method steps to perform substantially the same functions in substantially the same manner to achieve substantially the same results falls within the scope of the present invention. Accordingly, the scope of the present invention is limited solely by the appended claims.

Claims

1. An intelligent mapping method for natural resource rights confirmation based on a three-dimensional working base map, characterized by: The method comprises: Step 1: Detect and identify natural resources on a 3D working base map to generate preliminary boundaries. Based on these boundaries, the 3D working base map is divided into multiple natural resource areas. The 3D working base map is created using image data captured by remote sensing satellites and geographic data acquired by a GIS system. Step 2: Perform terrain elevation conflict detection in each natural resource area to obtain the terrain elevation conflict line in each natural resource area, and redivide the natural resource areas with terrain elevation conflicts according to the terrain elevation conflict lines; Step 3: Determine an upper limit for the natural resource area. For each natural resource area, calculate the average elevation difference between it and adjacent natural resource areas of different types. If the average elevation difference is within the set threshold, merge the two natural resource areas into one natural resource area. The area of the merged natural area must be smaller than the determined upper limit for the natural resource area. The average elevation difference is defined as the absolute value of the difference between the average altitude of one natural resource area and the average altitude of another natural resource area. Step 1 specifically includes: Step 1.1: Calculate the multispectral features of the 3D working base map; based on the multispectral features, calculate the local feature tensor of each pixel in the 3D working base map; Step 1.2: Based on the multispectral features and the local feature tensor of each pixel, calculate the boundary feature strength of the 3D working base map; construct a boundary tracking function based on the boundary feature strength; and generate the final boundary set as the preliminary boundary according to the boundary tracking function; Step 1.3: Divide the 3D working base map into different natural resource areas based on the preliminary boundaries; The process of calculating the multispectral characteristics of the three-dimensional working base map in step 1.1 specifically includes: obtaining spectral data from the three-dimensional working map, wherein the spectral data specifically includes: near-infrared band reflectance, red band reflectance, short-infrared band reflectance, green band reflectance, and blue band reflectance; and then calculating the multispectral characteristics of the three-dimensional working base map using the following formula: ； in, Indicates the coordinate position is Multispectral characteristic value of the pixel at ; Indicates the X-axis coordinate; Indicates the Y-axis coordinate; Indicates the Z-axis coordinate; is the surface temperature; is the surface emissivity; is the reflectivity in the near-infrared band; is the reflectivity of red light band; is the reflectivity in the short infrared band; is the reflectivity of green light band; is the reflectivity of blue light band; The upper limit of the wavelength of remote sensing satellites; is the lower limit of the wavelength of remote sensing satellites; is the wavelength integration variable; is the spectral reflectance distribution function, which means the wavelength is Spectral reflectance at .

2. The method for intelligent mapping of natural resource rights confirmation based on a three-dimensional working base map according to claim 1, characterized in that: In step 2, the specific process of performing terrain elevation conflict detection in each natural resource area includes: obtaining a contour distribution map of each natural resource area from the three-dimensional working base map; in the contour distribution map, if the height distance difference between any two adjacent contour lines exceeds a set threshold, it is determined that there is a terrain elevation conflict, and the contour line with the lower height of the two adjacent contour lines is used as the terrain elevation conflict line; the types of natural resource areas include: rivers, wetlands, forests, grasslands, cultivated land and deserts.

3. The method for intelligent mapping of natural resource rights confirmation based on a three-dimensional working base map according to claim 2, characterized in that: Spectral reflectance distribution function Calculated by the following formula: ； in, is the radiance value of the remote sensing satellite; is the atmospheric radiation path value, and the calculation formula is: ； in, is the Rayleigh scattering reflectivity; is the aerosol scattering reflectivity; is the observation zenith angle; and The atmospheric transmittance in the observation direction and the sun direction respectively: is the solar spectrum irradiance, and the calculation formula is: ； in, is the solar constant spectral distribution; is the mean distance between the Earth and the Sun; is the actual distance between the sun and the earth; is the atmospheric diffuse irradiance, and the calculation formula is: ； in, is the atmospheric diffuse transmittance; is the average surface reflectivity; is the albedo of the large balloon surface.

4. The method for intelligent mapping of natural resource rights confirmation based on a three-dimensional working base map according to claim 3, characterized in that: The process of calculating the local feature tensor of each pixel in the 3D working base map based on multispectral features in step 1.1 specifically includes: ； in, Indicates the coordinate position is The local eigenvector of the pixel at ; is the radius of curvature of the earth's surface; is the set reference curvature radius.

5. The method for intelligent mapping of natural resource rights confirmation based on a three-dimensional working base map according to claim 4 is characterized in that: In step 1.2, the boundary feature intensity of the 3D working base map is calculated based on the multispectral features and the local feature tensor of each pixel using the following formula: ； in, Indicates the coordinate position is The boundary feature intensity of the pixel at ; for gradient; is the absolute value operator.

6. The method for intelligent mapping of natural resource rights confirmation based on a three-dimensional working base map according to claim 5, characterized in that: In step 1.2, the boundary tracking function is constructed based on the boundary feature strength using the following formula: ； in, is the boundary tracking value; if Within the set threshold range, the coordinates are located at The pixel with coordinates is located at The adjacent pixels are connected to form a boundary segment; when all the pixels are connected, all the boundary segments are obtained, and each boundary segment is regarded as a boundary vector; is the geodesic curvature; is the normal curvature; is the normalized height value set; Indicates the coordinate position is The boundary feature intensity of the pixel at ; Indicates the coordinate position is The boundary feature intensity of the pixel at ; Indicates the coordinate position is Multispectral characteristic value of the pixel at ; Indicates the coordinate position is Multispectral characteristic value of the pixel at ; The coordinates are located at The pixel with coordinates is located at The distance between adjacent pixels.

7. The method for intelligent mapping of natural resource rights confirmation based on a three-dimensional working base map according to claim 6, characterized in that: In step 1.2, the final boundary set is generated according to the boundary tracing function. The process of the preliminary boundary specifically includes: among all boundary vectors, if the starting coordinates or the ending coordinates of any boundary vector are the same as the starting coordinates or the ending coordinates of any boundary vector, the two boundary vectors are summarized as one boundary. After traversing all boundary vectors, several boundaries are obtained; if the starting coordinates or the ending coordinates of any boundary vector are different from the starting coordinates or the ending coordinates of any other boundary vector, it is regarded as an isolated boundary and discarded; each retained boundary is taken as an element to generate the final boundary set.

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