Multi-source remote sensing data gridding construction and processing method and system, terminal and medium

Abstract

The invention belongs to the technical field of spatial data processing, and particularly discloses a multi-source remote sensing data gridding construction and processing method and system, a terminal and a medium. Comprising the following steps: acquiring remote sensing image data, vector data, point cloud data and TIN data to form a standardized data set; performing three-dimensional layered mesh generation on the target area to generate an initial multi-level mesh set; performing geometric superposition and coverage judgment on the standardized data set to form an element-grid mapping set; performing double-engine coding and decoding on the grid units in the mapping set, generating a unique grid code, and establishing a code-space range mapping table; constructing a three-dimensional space topological structure; obtaining a space intersection result, a space union result and a space difference result; a multi-scale grid set is generated, and a unified gridding integrated data set is formed. Through gridding fusion and self-adaptive multi-scale processing of multi-source data, integrated expression and efficient organization of multi-source remote sensing data in a three-dimensional space are realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of spatial data processing, and particularly relates to a multi-source remote sensing data gridding construction and processing method, system, terminal and medium. BACKGROUND

[0002] Remote sensing data is an important source of geographic spatial data, which has various forms, including remote sensing images, point clouds, vector maps, TIN models and other multi-source heterogeneous data. Efficient organization and processing of these massive remote sensing data is a key problem in the current GIS and digital twin fields.

[0003] Traditional GIS applications usually adopt a "database + file system" storage mode to record spatial data in the form of vector or raster files. Although this method maintains the original details of the data, it has obvious shortcomings when processing large-scale remote sensing data. In order to improve data loading efficiency, the industry has introduced chunking technologies such as raster tiles and vector tiles to slice the original data by level. However, this method does not fundamentally reorganize the data content, and the efficiency of calculation and analysis is still limited by the diversity and complexity of geographic features.

[0004] In addition, there are common needs and value of result comparison and fusion between different remote sensing tasks, but due to the inconsistency of data accuracy, coordinate reference system and projection method used by each task, it is very difficult to align, compare and fuse cross-source data. The existing data organization form cannot unify the data model and operation mode of different tasks at the application level, and the processing method of each task cannot maximize the release of the analysis value and computing efficiency of the data. SUMMARY

[0005] The application provides a multi-source remote sensing data gridding construction and processing method, system, terminal and medium to solve the problem that the existing data organization form in the background art cannot unify the data model and operation mode of different tasks at the application level, and the processing method of each task cannot maximize the release of the analysis value and computing efficiency of the data.

[0006] The technical scheme adopted by the application is as follows: In a first aspect, the application provides a multi-source remote sensing data gridding construction and processing method, which includes the following steps: Step S1, acquiring remote sensing image data, vector data, point cloud data and TIN data in a data source list, performing coordinate reference unification and format standardization on each data to obtain a standardized data set D0; Step S2, based on a G-DGGS engine and a GeoSOT engine, performing hierarchical mesh subdivision on a three-dimensional space within a target region boundary to generate an initial multi-level three-dimensional mesh set G0; Step S3, perform geometric-grid overlay and coverage determination on the points, lines, and surfaces in the normalized dataset D0 and the TIN geometric elements, map each element to the corresponding grid cell in the initial multi-level three-dimensional grid set G0, respectively obtain the remote sensing image grid cell set M1, the vector data grid cell set M2, the point cloud data grid cell set M3, and the TIN data grid cell set M4, and merge the remote sensing image grid cell set M1, the vector data grid cell set M2, the point cloud data grid cell set M3, and the TIN data grid cell set M4 to obtain the element-grid mapping set M0; Step S4, perform grid coding on each grid cell in the element-grid mapping set M0 to generate a unique grid code C, and perform decoding on the grid code to obtain a three-dimensional spatial range, and establish a coding-spatial range mapping table E; Step S5, perform hierarchical relationship and neighborhood relationship calculation on the grid cells based on the initial multi-level three-dimensional grid set G0 to obtain the parent-child relationship table P and the neighborhood relationship table N; Step S6, perform intersection operation, union operation, and complement operation on the grid cell sets Mi corresponding to different data sources two by two to obtain the spatial intersection result set I, the spatial union result set U, and the spatial difference result set D; Step S7, perform refinement subdivision on the mixed grid cells in the spatial intersection result set I and the spatial union result set U, perform hierarchical aggregation on the grid cells corresponding to the coverage sparse area with an effective grid density lower than a preset threshold to generate a multi-scale grid set Gms based on the refinement and aggregation results; Step S8, perform spatial index construction and data block hanging based on the grid code C on the multi-scale grid set Gms to generate a unified grid index table Index, and encapsulate Index, E, P, N, I, U, D, and Gms into a grid integration dataset R.

[0007] Further, step S2 includes the following steps: Step S2-1, call the G-DGGS engine to perform preliminary subdivision on the target region space according to the discrete grid division rule to generate a first-level grid set G11 to G1n; Step S2-2, call the GeoSOT engine to perform secondary subdivision on the first-level grid set according to the regional standard geographic grid coding rule to generate a second-level grid set G21 to G2n; Step S2-3, based on the mapping relationship between the grid code output by the G-DGGS engine and the geographic grid code output by the GeoSOT engine, establish a double-engine grid correspondence table T; Step S2-4: Based on the dual-engine mesh correspondence table T, perform consistency detection and merging operations on duplicate or overlapping mesh cells to obtain an initial multi-level three-dimensional mesh set G0 that is compatible with both encoding systems.

[0008] Furthermore, in step S3, the geometric-mesh overlay and coverage determination of points, lines, polygons, and TIN geometric features in the normalized dataset D0 includes: For point features, the center point of the grid cell is determined based on their spatial coordinates, and the point features that fall within the range of a certain grid cell are assigned to that grid cell. For linear features, the intersection of line segments and grid cell boundaries is determined based on their node sequence. Line features that intersect with grid cells are segmented and assigned to the corresponding grid cells. For face features, perform polygon and grid cell area overlap determination based on their boundary polygons, and assign face features with an overlap area ratio exceeding a set threshold to the corresponding grid cell. For TIN elements, the volume intersection of the 3D bounding box and the mesh cell is determined based on the spatial range of the triangular facets. Triangular facets with an intersection volume ratio exceeding a set threshold are assigned to the corresponding mesh cell.

[0009] Furthermore, step S4 includes the following steps: Step S4-1: Based on the hierarchical partitioning rules of the G-DGGS engine, generate the first code C1 for each grid cell; Step S4-2: Based on the geolocation division rules of the GeoSOT engine, generate a second code C2 for each grid cell; Step S4-3: According to the dual-engine grid correspondence table in claim 2, perform correspondence matching between C1 and C2 to generate a unique grid code C; Step S4-4: Perform decoding operation on C to parse the longitude, latitude and elevation range, and generate the corresponding three-dimensional spatial bounding box data; Steps S4-5: Summarize the grid code C of each grid cell and its 3D spatial bounding box data, and establish the code-spatial range mapping table E.

[0010] Furthermore, step S5 includes the following steps: Step S5-1: Determine the level L of each grid cell according to the hierarchical position of the grid code C, and obtain the parent grid cell identifier by extracting the position of the previous level, and summarize them to form a parent-child relationship table P; Step S5-2: Within level L, based on the three-dimensional spatial range of the encoding-spatial range mapping table E, retrieve the grid cells that are adjacent in the X, Y, and Z directions, and summarize them to form a neighborhood relationship table N; Step S5-3: Perform consistency check and deduplication on the parent-child relationship table P and the neighborhood relationship table N, and output the corrected parent-child relationship table P and neighborhood relationship table N.

[0011] Furthermore, step S6 includes the following steps: Step S6-1: Filter grid cell pairs with spatial adjacency or hierarchical association based on the parent-child relationship table P and the neighborhood relationship table N; Step S6-2: Perform spatial intersection operation on the selected grid cell pairs to generate spatial intersection result set I; Step S6-3: Perform a spatial union operation on the grid cell set Mi of each data source to generate a spatial union result set U; Step S6-4: Perform a difference operation on the spatial union result set U and the spatial intersection result set I to generate the spatial difference result set D.

[0012] Furthermore, step S7 includes the following steps: Step S7-1: Based on the spatial overlap ratio of the grid cells in the spatial intersection result set I, select those with an overlap ratio exceeding a set threshold. For the region, perform the next level of subdivision on the grid cells within that region to generate a refined grid set F; Step S7-2: Based on the effective grid density of the grid cells in the spatial union result set U, determine whether the effective grid density is lower than a preset threshold. The region is a sparsely covered area, and hierarchical aggregation is performed on the grid cells within this region to generate an aggregated grid set A; Step S7-3: Merge the refined mesh set F and the aggregated mesh set A in spatial order to form a multi-scale mesh set Gms; Step S7-4: Recalculate the parent-child hierarchy and neighborhood relationships for the grid cells in Gms, and update the corresponding parent-child relationship table P and neighborhood relationship table N.

[0013] Secondly, this application provides a multi-source remote sensing data gridding construction and processing system for implementing the multi-source remote sensing data gridding construction and processing method as described in the first aspect. The system includes: The data acquisition unit is used to acquire remote sensing image data, vector data, point cloud data and TIN data from the data source list, and to perform coordinate benchmark unification and format standardization on each data to obtain a standardized dataset D0. Mesh building unit, used to perform three-dimensional spatial hierarchical meshing within the target region boundary based on the G-DGGS engine and GeoSOT engine, to generate an initial multi-level three-dimensional mesh set G0; The feature mapping unit is used to perform geometric-mesh overlay and coverage determination on the points, lines, polygons and TIN geometric features in the normalized dataset D0, generate mesh cell sets M1, M2, M3 and M4 for various types of data, and merge them to form the feature-mesh mapping set M0; The encoding processing unit is used to generate a unique grid code C for each grid cell according to the dual-engine grid correspondence table, and to perform decoding operations to obtain the three-dimensional spatial range and establish an encoding-spatial range mapping table E. The topology calculation unit is used to calculate the parent-child relationship and the proximity relationship based on the grid code C, and generate the parent-child relationship table P and the neighborhood relationship table N; The set operation unit is used to perform spatial intersection, spatial union, and difference operations on grid cell sets Mi from different data sources based on the parent-child relationship table P and the neighborhood relationship table N, generating spatial intersection result set I, spatial union result set U, and spatial difference result set D; Multi-scale building blocks are used to refine the mixed grid cells of the spatial intersection result set I and the spatial union result set U, perform hierarchical aggregation on the grid cells covering sparse regions, generate a multi-scale grid set Gms, and update the parent-child relationship table P and the neighborhood relationship table N. The index encapsulation unit is used to perform spatial index construction and data block attachment on the multi-scale grid set Gms based on grid encoding C, generate a unified grid index table Index, and encapsulate Index, E, P, N, I, U, D and Gms into a gridded unified dataset R.

[0014] Thirdly, this application provides a terminal, including: The memory is used to store the gridding construction and processing program for multi-source remote sensing data; The processor is used to implement the steps of the multi-source remote sensing data gridding construction and processing method as described in the first aspect when executing the multi-source remote sensing data gridding construction and processing apparatus.

[0015] Fourthly, this application provides a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the multi-source remote sensing data gridding construction and processing method as described in the first aspect.

[0016] As can be seen from the above technical solutions, the advantages of the present invention are: By introducing a multi-engine gridding and multi-scale dynamic optimization mechanism, unified representation and intelligent processing of multi-source remote sensing data in three-dimensional space are achieved. The parallel partitioning using G-DGGS and GeoSOT dual engines enables the generated initial multi-level 3D mesh set to combine the hierarchical resolution of discrete meshes with the coding compatibility of regional geographic meshes, thereby constructing a standardized, bidirectionally mappable spatial indexing framework. Based on the geometry-grid overlay algorithm, the spatial integration mapping of image, vector, point cloud and TIN data is realized, so that different types of remote sensing data have consistent spatial correlation attributes in the same grid system; A unique grid code C is generated through a dual encoding matching and decoding process, and an encoding-spatial range mapping table E is established, so that each spatial unit has a stable unique identifier and a traceable spatial boundary, which significantly improves the accuracy of data retrieval and indexing. The calculated parent-child relationship table P and neighborhood relationship table N construct a complete three-dimensional spatial topological relationship, providing structural support for hierarchical association and adjacency analysis between multi-source data; By using P and N to perform intersection, union, and difference operations, it is possible to quickly identify overlapping, overlapping, and different regions between different data sources, thereby achieving spatial automation of data fusion analysis. By introducing an effective grid density threshold and a spatial overlap ratio threshold, dense regions are adaptively refined and sparse regions are aggregated to generate a multi-scale grid set Gms, thereby reducing the number of redundant grids while ensuring spatial accuracy and improving overall computational and storage efficiency. Attached Figure Description

[0017] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a step diagram illustrating the multi-source remote sensing data gridding construction and processing method in the embodiment. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1 As shown, this invention provides a method for constructing and processing multi-source remote sensing data grids, including the following steps: Step S1: Obtain remote sensing image data, vector data, point cloud data and TIN data from the data source list, and perform coordinate benchmark unification and format standardization on each data to obtain a standardized dataset D0; In practical implementation, image data, vector data, and point cloud data can be imported from different satellite remote sensing platforms, UAV aerial survey systems, or geographic information databases through data acquisition interfaces, and unified to the same geographic reference system (such as the CGCS2000 coordinate system). Format conversion and projection correction are performed on the data of different formats using GDAL, PDAL, or a self-developed data parsing module, ensuring that the spatial accuracy, resolution, and data structure of all data are comparable. In this example, satellite imagery with a resolution of 5 meters, terrain vector data at a scale of 1:10000, and UAV point cloud data with an accuracy of 5 cm can be selected and, after unified coordinate reference and format normalization processing, formed a normalized dataset D0.

[0021] Step S2: Based on the G-DGGS engine and GeoSOT engine, perform hierarchical meshing in the three-dimensional space within the boundary of the target area to generate an initial multi-level three-dimensional mesh set G0. In practical implementation, the G-DGGS engine is used to perform hexahedral discretization, while the GeoSOT engine performs localized subdivision based on regional standard grid codes. The combination of the two allows for geographical subdivision at both scale and regional levels. By setting the minimum subdivision unit size within the spatial boundaries of the target area, the maximum resolution of the grid levels can be controlled. In this embodiment, an initial subdivision is performed with a first-level grid side length of 100 kilometers, followed by further subdivision at 10 kilometers, 1 kilometer, and 100 meters, forming a multi-level three-dimensional grid set G0, providing a spatial framework for subsequent data mapping and indexing.

[0022] Step S3: Perform geometry-mesh overlay and coverage determination on the points, lines, surfaces and TIN geometric features in the normalized dataset D0, and map each feature to the corresponding mesh cell in the initial multi-level 3D mesh set G0, to obtain the remote sensing image mesh cell set M1, vector data mesh cell set M2, point cloud data mesh cell set M3 and TIN data mesh cell set M4 respectively. Then merge the remote sensing image mesh cell set M1, vector data mesh cell set M2, point cloud data mesh cell set M3 and TIN data mesh cell set M4 to obtain the feature-mesh mapping set M0. In practical implementation, the system uses a geometric overlay algorithm to match each data type with the corresponding grid cell in G0. Image data is determined based on the overlap area between raster pixels and grid cells; vector data is assigned based on the intersection of line segments and grid boundaries; point cloud data is assigned based on point density statistics; and TIN data is matched based on the three-dimensional spatial intersection volume of triangular facets. In the embodiment, synchronous mapping of building vector data, road networks, DEM surfaces, and point cloud features can be achieved in areas containing urban features, thereby forming a structurally unified feature-grid mapping set M0.

[0023] Step S4: Perform grid encoding on each grid cell in the feature-grid mapping set M0 to generate a unique grid code C, and perform decoding on the grid code to obtain the three-dimensional spatial extent, and establish the code-spatial extent mapping table E; In practical implementation, the encoding module reads the G-DGGS and GeoSOT codes of each grid cell and generates a unique grid code C based on their mapping relationship. This encoding is compatible with multiple resolution levels and can be reverse-analyzed to latitude, longitude, and elevation boundaries. The system then establishes an encoding-spatial range mapping table E to store the spatial location, level number, and range boundary information of each grid cell. In this embodiment, a binary hierarchical encoding method can be used, so that the positional structure of C corresponds to the grid level depth, thereby achieving rapid multi-scale positioning and indexing.

[0024] Step S5: Based on the initial multi-level three-dimensional mesh set G0, perform hierarchical and proximity calculations on the mesh cells to obtain the parent-child relationship table P and the neighborhood relationship table N; In practical implementation, by analyzing the hierarchical information of each grid cell in the encoding-spatial extent mapping table E, the system can deduce the parent-child hierarchical correspondence and determine neighboring grids based on spatial coordinate comparison. The parent-child relationship table P reflects the inheritance structure of upper and lower levels, and the neighborhood relationship table N describes the adjacency of grids at the same level in three-dimensional space. In the embodiment, in the urban 3D modeling scenario, the parent layer grid can correspond to a plot-level area, the child layer grid can correspond to a building-level partition, and the neighborhood relationship table can be used to quickly calculate the spatial connectivity between adjacent buildings.

[0025] Step S6: Perform intersection, union, and complement operations on each pair of grid cell sets Mi corresponding to different data sources to obtain spatial intersection result set I, spatial union result set U, and spatial difference result set D; In practical implementation, the set operation module compares the grid cell sets from different data sources one by one, based on the parent-child relationship table P and the neighborhood relationship table N, to calculate their spatial overlap, coverage, and difference range. The intersection set I is used to identify areas shared by multiple data sources; the union set U represents the overall coverage range; and the difference set D is used to detect areas where there is offset or missing data between data sources. In the embodiment, by comparing the grid sets of UAV point clouds and satellite imagery, areas of newly constructed buildings or changes in ground features can be identified, providing data support for change detection and updated mapping.

[0026] Step S7: Refine the mixed mesh cells in the spatial intersection result set I and the spatial union result set U, and refine the mesh cells with effective mesh density below a preset threshold. Hierarchical aggregation is performed on the grid cells corresponding to the sparsely covered regions, and a multi-scale grid set Gms is generated based on the refinement and aggregation results. In practical implementation, the refinement subdivision involves detecting the coverage ratio of overlapping grids in I, and further subdividing grids with high overlap. The aggregation process, on the other hand, calculates the effective point cloud density or image pixel count of the grids in U, performing higher-level aggregation on sparse areas to reduce the number of redundant grids. The resulting multi-scale grid set Gms has higher resolution in high-density areas and maintains a larger scale in low-density areas, achieving adaptive optimization of the spatial data structure. In the embodiment, within a remote sensing area containing mixed mountain and urban terrain, this method can aggregate grids in sparse mountain areas to a 1km scale, while automatically refining the urban core area to a 50m scale, thereby significantly improving data processing and storage efficiency while maintaining spatial accuracy.

[0027] Step S8: Based on the grid encoding C, perform spatial index construction and data block attachment on the multi-scale grid set Gms to generate a unified grid index table Index. Encapsulate Index, E, P, N, I, U, D and Gms into a gridded integrated dataset R.

[0028] In its implementation, the system uses the grid code C as the primary index key to construct a spatial index for multi-scale grid units in Gms, establishing a connection with feature data blocks to achieve rapid positioning and hierarchical querying. The resulting unified grid index table (Index) is encapsulated with E, P, N, I, U, and D to form a unified gridded dataset R, providing a unified data structure foundation for subsequent 3D geographic analysis, spatiotemporal data mining, and digital twin scene rendering. In this embodiment, the unified dataset can be used for regional disaster monitoring or land use analysis, enabling rapid loading and spatial analysis of multi-source remote sensing data within a unified grid framework.

[0029] In some embodiments, step S2 includes the following steps: Step S2-1: Call the G-DGGS engine to perform preliminary subdivision of the target area space using discrete mesh partitioning rules, generating the first-level mesh set G11 to G1n; In practical implementation, the system reads the spatial boundary parameters of the target area through the G-DGGS engine and performs three-dimensional hexahedral subdivision according to the discrete grid division rules. The engine performs latitude and longitude projection conversion on the target area based on the Earth ellipsoid model parameters and determines the grid edge length and layer depth, thereby generating a first-level grid set G11 to G1n covering the entire target area. The grid size of this level can be automatically matched according to the analysis accuracy; for example, in a regional remote sensing scenario, it can be set to a 100-kilometer edge length to ensure a uniform subdivision granularity across the area. In this embodiment, the outer boundary of the study area can be input into the G-DGGS engine to generate a first-level grid set corresponding to the grid system, providing basic spatial units for subsequent localized subdivision.

[0030] Step S2-2: Call the GeoSOT engine to perform secondary subdivision of the first-level grid set using the regional standard geographic grid coding rules to generate the second-level grid set G21 to G2n; In practical implementation, after obtaining the first-level grid set, the system inputs the spatial extent of each grid unit into the GeoSOT engine and performs secondary subdivision according to the regional standard geogrid coding (GeoSOT) rules. The GeoSOT engine adopts a hierarchical binary coding mechanism, which can achieve fine resolution subdivision at the meter or even centimeter level based on the grid. The system generates the second-level grid set G21 to G2n layer by layer according to the preset resolution parameters. In the embodiment, if the first level is a 100-kilometer grid, the GeoSOT engine can further subdivide it into 10-kilometer or 1-kilometer levels, thereby achieving compatibility with regional geographic information standards.

[0031] Step S2-3: Based on the mapping relationship between the grid code output by the G-DGGS engine and the geogrid code output by the GeoSOT engine, establish a dual-engine grid correspondence table T; In practical implementation, the system extracts the grid coding sequence generated by G-DGGS and the local geocoding sequence generated by GeoSOT, and calculates the spatial overlap between the two using a coordinate matching algorithm. For each pair of codes, a correspondence determination is performed on the spatial centroid and coverage area, the correspondence is recorded, and a dual-engine grid correspondence table T is established. This table T serves as the core data structure for cross-engine indexing, allowing the system to perform mutual lookups and conversions between the system and the local coding system. In this embodiment, the system uses a spatial index matching strategy to map each G-DGGS code to multiple GeoSOT coding blocks, thereby achieving a hierarchical mapping from grid to regional grid.

[0032] Step S2-4: Based on the dual-engine mesh correspondence table T, perform consistency detection and merging operations on duplicate or overlapping mesh cells to obtain an initial multi-level three-dimensional mesh set G0 that is compatible with both encoding systems.

[0033] In its implementation, the system utilizes a dual-engine mesh mapping table T to perform spatial overlap detection on overlapping mesh cells generated by the two engines within the same region. When the overlap area ratio exceeds a preset threshold, it is marked as a duplicate cell, and the higher-precision encoding system is retained. Subsequently, the system performs a merging operation on adjacent mesh cells with the same hierarchical attributes to form a multi-level 3D mesh set G0 compatible with both encoding systems. This set retains the consistency of G-DGGS while possessing the local precision expression capability of GeoSOT. In the embodiment, if the overlap rate between the grid and the regional grid exceeds 80% in an urban area, the system automatically selects GeoSOT encoding for merging, thereby generating a unified 3D mesh data structure that can be bidirectionally converted.

[0034] In some embodiments, the geometric-mesh overlay and coverage determination of points, lines, polygons, and TIN geometric features in the normalized dataset D0 in step S3 includes: For point features, the center point of the grid cell is determined based on their spatial coordinates, and the point features that fall within the range of a certain grid cell are assigned to that grid cell. In practical implementation, the system reads the spatial coordinates (longitude, latitude, and elevation) of point elements and calculates the spatial location of the center point of its grid cell through coordinate index matching. When the coordinates of a point fall within the boundary range of a certain grid cell, it is determined that the point belongs to that cell. In the embodiment, after gridding and assigning the locations of UAV point clouds or GNSS measuring stations, the unique spatial identification and rapid query of ground sampling points in the three-dimensional grid system can be realized.

[0035] For linear features, the intersection of line segments and grid cell boundaries is determined based on their node sequence. Line features that intersect with grid cells are segmented and assigned to the corresponding grid cells. In practical implementation, the system discretizes line features into several line segments according to the node sequence, uses a spatial intersection detection algorithm to determine whether each line segment intersects with the grid boundary, and divides the intersecting parts according to the grid cell boundary. The divided line segments are then assigned to the corresponding grid cells. In the embodiments, for road or river vector data, the system can decompose continuous roads into cross-grid segments, thereby supporting topology analysis and hierarchical management of road networks at different grid scales.

[0036] For face features, perform polygon and grid cell area overlap determination based on their boundary polygons, and assign face features with an overlap area ratio exceeding a set threshold to the corresponding grid cell. In practical implementation, the system calculates the overlap ratio between the polygonal boundary of a surface feature and each grid cell. When the overlap ratio exceeds a threshold (e.g., 50%), the surface feature is determined to belong to that cell. In the embodiment, in land use classification, surface features such as buildings, cultivated land, and water bodies can be gridded and assigned to achieve grid-based land use statistics and area calculation.

[0037] For TIN elements, the volume intersection of the 3D bounding box and the mesh cell is determined based on the spatial range of the triangular facets. Triangular facets with an intersection volume ratio exceeding a set threshold are assigned to the corresponding mesh cell.

[0038] In practical implementation, the system extracts the spatial bounding box range of the TIN triangular facet and performs volume intersection calculations with the bounding boxes of each grid cell. When the intersection volume ratio exceeds a set threshold (e.g., 30%), the triangular facet is assigned to the corresponding grid cell. In this embodiment, for digital terrain models (DTM) or digital surface models (DSM), this method can be used to achieve accurate embedding of the three-dimensional surface structure within a multi-level grid framework, ensuring the continuity and integrity of terrain features at the refinement scale.

[0039] In some embodiments, step S4 includes the following steps: Step S4-1: Based on the hierarchical partitioning rules of the G-DGGS engine, generate the first code C1 for each grid cell; In practical implementation, the system traverses the spatial hierarchy of grid cells through the hierarchical management module of the G-DGGS engine, assigning a unique hierarchical number to each level of grid cell. This number increases sequentially according to the spatial segmentation depth, ensuring strict inheritance between upper and lower grid layers. When generating C1, the system uses a bitwise encoding method, merging longitude, latitude, and the hierarchical number to form a binary bit structure, ensuring that the encoding result can be directly reverse-analyzed. In this embodiment, the surface can be divided into 10 hierarchical levels, with the 1st to 10th bits of C1 representing the hierarchical identifier, achieving unified hierarchical management within the specified range.

[0040] Step S4-2: Based on the geolocation division rules of the GeoSOT engine, generate a second code C2 for each grid cell; In its implementation, the GeoSOT engine encodes each grid unit with latitude and longitude according to the regional standard geographic grid division rules. The system generates a decimal code C2 using the coordinate information of the grid's latitude and longitude center point. Each code contains two parts: an administrative division identifier and a geographic coordinate index, used for quickly indexing geographic elements within the local area. In this embodiment, C2 can be generated by multiplying the latitude and longitude coordinates by an amplification factor after integerization, giving the codes at different resolution levels a progressive characteristic and ensuring compatibility with the regional standard indexing system of geographic information data.

[0041] Step S4-3: According to the dual-engine grid correspondence table in claim 2, perform correspondence matching between C1 and C2 to generate a unique grid code C; In practical implementation, the system compares C1 and C2 one-to-one according to the dual-engine grid correspondence table T, and combines the spatial hierarchy and latitude / longitude information in the two coding structures through weighted fusion or bit segment splicing to generate a unique grid code C in a unified format. This code inherits the hierarchical positioning capability of G-DGGS while maintaining the regional compatibility characteristics of GeoSOT. In the embodiment, the system can construct C by using a prefix to represent the hierarchical structure and a suffix to represent the local code, realizing a continuous and traceable coding system down to the region, thereby supporting unified indexing of cross-scale, multi-source data.

[0042] Step S4-4: Perform decoding operation on C to parse the longitude, latitude and elevation range, and generate the corresponding three-dimensional spatial bounding box data; In practical implementation, after reading the grid code C, the system sequentially extracts the hierarchical identifier, latitude and longitude segments, and elevation segments using a preset encoding parsing function. It then calculates the minimum and maximum boundary points of the grid in three-dimensional space, thereby determining the spatial bounding box range. This bounding box serves as the basic unit for subsequent spatial analysis, rendering, and data mapping, accurately reflecting the three-dimensional boundary of the grid. In this embodiment, when the code C contains a 20-bit hierarchical identifier, the decoding result can achieve a spatial accuracy of approximately 5 meters, meeting the mapping requirements of high-resolution remote sensing images or point cloud data.

[0043] Steps S4-5: Summarize the grid code C of each grid cell and its 3D spatial bounding box data, and establish the code-spatial range mapping table E.

[0044] In practical implementation, the system sorts and summarizes the codes C of all mesh elements and their corresponding 3D bounding box ranges in hierarchical order, constructing a code-spatial range mapping table E. This mapping table records the one-to-one correspondence between codes and spatial coordinates, and supports direct retrieval of the 3D position range of the mesh through the code. In this embodiment, the mapping table E can be stored using a database index structure, where the primary key is the mesh code C, and the field value is the coordinate range of the spatial bounding box, enabling fast retrieval and visual mapping of tens of millions of mesh elements.

[0045] In some embodiments, step S5 includes the following steps: Step S5-1: Determine the level L of each grid cell according to the hierarchical position of the grid code C, and obtain the parent grid cell identifier by extracting the position of the previous level, and summarize them to form a parent-child relationship table P; In its implementation, the system determines the hierarchical position of a grid cell by parsing the hierarchical position field of the grid code C. Each grid cell's code C contains a hierarchical identifier; by extracting the code of its superior level, the parent cell identifier can be determined. After traversing all codes, the program automatically matches the correspondence between parent and child layers and summarizes this into a tabular table P. In this embodiment, when the coding hierarchy depth is 10 levels, the system can identify the inheritance relationship of 10^6 sub-grids, thus supporting cross-scale spatial hierarchical organization and laying the foundation for subsequent topology and adjacency calculations.

[0046] Step S5-2: Within level L, based on the three-dimensional spatial range of the encoding-spatial range mapping table E, retrieve the grid cells that are adjacent in the X, Y, and Z directions, and summarize them to form a neighborhood relationship table N; In practical implementation, the system uses the spatial coordinate range of each grid cell in Table E to calculate the positional relationship of the grid boundary in three-dimensional space. When the spatial distance between two grid cells in any direction is less than a set threshold and they do not overlap, the system defines them as adjacent cells and records their spatial direction and distance information. After traversal and comparison, all cell pairs that meet the conditions are summarized to form a neighborhood relationship table N. In the embodiment, at a resolution of 100 meters, the system can identify up to 26 three-dimensional directional neighboring cells for a single grid cell, thereby achieving efficient description of three-dimensional spatial topological relationships.

[0047] Step S5-3: Perform consistency check and deduplication on the parent-child relationship table P and the neighborhood relationship table N, and output the corrected parent-child relationship table P and neighborhood relationship table N.

[0048] In practical implementation, the system performs bidirectional matching checks on the P and N tables to ensure that each sub-grid corresponds to only one parent grid and that the adjacency relationships are symmetrical. For duplicate records or invalid boundary grids, the program performs cleanup and deduplication, ultimately generating a consistent P and N table. In this embodiment, this process can quickly verify the topological relationships of millions of grid cells using hash indexes, ensuring the uniqueness and reliability of spatial relationship data.

[0049] In some embodiments, step S6 includes the following steps: Step S6-1: Filter grid cell pairs with spatial adjacency or hierarchical association based on the parent-child relationship table P and the neighborhood relationship table N; In practical implementation, the system analyzes the data structure of tables P and N to filter out grid cell pairs with direct parent-child or adjacent relationships. These cell pairs represent areas where data interaction or geometric overlap may exist in space. In the embodiment, in a remote sensing image and point cloud fusion scenario, this filtering process can be used to quickly identify adjacent or overlapping observation areas, thereby reducing unnecessary computational load.

[0050] Step S6-2: Perform spatial intersection operation on the selected grid cell pairs to generate spatial intersection result set I; In practice, the system reads the spatial boundary information of each pair of cells and calculates their intersection region using three-dimensional spatial Boolean operations. When the three-dimensional boundary volumes of two grid cells overlap, the system extracts the intersection portion as an independent grid cell and stores it in set I. In this embodiment, in the land cover classification task, the spatial intersection set I can be used to identify target areas covered by multiple source data, such as overlapping areas of buildings or roads.

[0051] Step S6-3: Perform a spatial union operation on the grid cell set Mi of each data source to generate a spatial union result set U; In practical implementation, the system performs spatial range merging on the Mi set from different data sources, merging all overlapping and adjacent grids to form a complete coverage area set U. This set is used to represent the comprehensive spatial distribution of multi-source data. In the embodiment, by combining the grids of satellite imagery, aerial imagery, and ground scan point cloud, a regional-level comprehensive land cover map can be obtained, realizing the fusion expression of multi-source data under a unified spatial framework.

[0052] Step S6-4: Perform a difference operation on the spatial union result set U and the spatial intersection result set I to generate the spatial difference result set D.

[0053] In practical implementation, the system takes U and I as inputs and extracts areas not covered by the intersection through difference operations. These areas represent regions where there is spatial offset, missing data, or observational differences between different data sources. In the embodiments, in annual land cover change detection, the D set can be used to automatically identify newly built, demolished, or naturally changed surface areas, providing basic data support for geographic monitoring and change analysis. In some embodiments, step S7 includes the following steps: Step S7-1: Based on the spatial overlap ratio of the grid cells in the spatial intersection result set I, select those with an overlap ratio exceeding a set threshold. For the region, perform the next level of subdivision on the grid cells within that region to generate a refined grid set F; In practice, the system calculates the overlap ratio of each grid cell in the intersection set I. When the overlap ratio exceeds a threshold, the region is considered a high-complexity region and refined according to a higher level. The refinement operation improves spatial accuracy by generating the next level of sub-grid cells. In the embodiment, in urban core areas or areas with drastic terrain undulations, the original 100-meter grid can be refined to the 10-meter level to ensure that complex features are fully depicted.

[0054] Step S7-2: Based on the effective grid density of the grid cells in the spatial union result set U, determine whether the effective grid density is lower than a preset threshold. The region is a sparsely covered area, and hierarchical aggregation is performed on the grid cells within this region to generate an aggregated grid set A; In practical implementation, the system counts the number of effective pixels or point clouds in each grid unit of U. When the density is below a threshold, the region is determined to be a sparse region. In the aggregation process, such regions are formed into large-scale spatial blocks by merging higher-level grid units, reducing redundant data. In this embodiment, in mountainous or desert areas, the system can aggregate the original 1-kilometer grid to a 5-kilometer scale, thereby balancing spatial resolution and storage efficiency.

[0055] Step S7-3: Merge the refined mesh set F and the aggregated mesh set A in spatial order to form a multi-scale mesh set Gms; In practical implementation, the system integrates the refined high-resolution grid and the aggregated low-resolution grid into a unified multi-scale set Gms based on the spatial order of the grid code C. This set maintains spatial continuity and possesses variable resolution characteristics in terms of hierarchy. In the embodiments, Gms can simultaneously contain urban high-precision grids and suburban coarse-scale grids in the same scene, thereby achieving hierarchical management and visualization.

[0056] Step S7-4: Recalculate the parent-child hierarchy and neighborhood relationships for the grid cells in Gms, and update the corresponding parent-child relationship table P and neighborhood relationship table N.

[0057] In practical implementation, after generating Gms, the system retraces its grid encoding and recalculates parent-child and adjacency relationships based on the new hierarchical division. The updated P and N tables ensure topological consistency across multiple scales. In this embodiment, the update operation can establish a complete inheritance relationship between the newly added and existing levels, enabling the system to perform spatial queries and data indexing at any scale, thus achieving unified scheduling and management of multi-resolution remote sensing data.

[0058] In some embodiments, this application provides a multi-source remote sensing data gridding construction and processing system, the system comprising: The data acquisition unit is used to acquire remote sensing image data, vector data, point cloud data and TIN data from the data source list, and to perform coordinate benchmark unification and format standardization on each data to obtain a standardized dataset D0. Mesh building unit, used to perform three-dimensional spatial hierarchical meshing within the target region boundary based on the G-DGGS engine and GeoSOT engine, to generate an initial multi-level three-dimensional mesh set G0; The feature mapping unit is used to perform geometric-mesh overlay and coverage determination on the points, lines, polygons and TIN geometric features in the normalized dataset D0, generate mesh cell sets M1, M2, M3 and M4 for various types of data, and merge them to form the feature-mesh mapping set M0; The encoding processing unit is used to generate a unique grid code C for each grid cell according to the dual-engine grid correspondence table, and to perform decoding operations to obtain the three-dimensional spatial range and establish an encoding-spatial range mapping table E. The topology calculation unit is used to calculate the parent-child relationship and the proximity relationship based on the grid code C, and generate the parent-child relationship table P and the neighborhood relationship table N; The set operation unit is used to perform spatial intersection, spatial union, and difference operations on grid cell sets Mi from different data sources based on the parent-child relationship table P and the neighborhood relationship table N, generating spatial intersection result set I, spatial union result set U, and spatial difference result set D; Multi-scale building blocks are used to refine the mixed grid cells of the spatial intersection result set I and the spatial union result set U, perform hierarchical aggregation on the grid cells covering sparse regions, generate a multi-scale grid set Gms, and update the parent-child relationship table P and the neighborhood relationship table N. The index encapsulation unit is used to perform spatial index construction and data block attachment on the multi-scale grid set Gms based on grid encoding C, generate a unified grid index table Index, and encapsulate Index, E, P, N, I, U, D and Gms into a gridded unified dataset R.

[0059] In some embodiments, this application provides a terminal, including: The memory is used to store the gridding construction and processing program for multi-source remote sensing data; A processor is used to implement the steps of the multi-source remote sensing data gridding construction and processing method when executing the multi-source remote sensing data gridding construction and processing system.

[0060] In some embodiments, this application provides a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the multi-source remote sensing data gridding construction and processing method.

[0061] It is understood that the systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer, which can be a personal computer, a laptop computer, a personal digital assistant, a tablet computer, a wearable device, or any combination of these devices.

[0062] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0063] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0064] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0065] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0066] It should be understood that although the terms first, second, third, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of one or more embodiments of this specification, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "in response to a determination," or "when," or "in the event of a determination."

[0067] The above description is merely a preferred embodiment of one or more embodiments of this specification and is not intended to limit the scope of one or more embodiments of this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this specification should be included within the protection scope of one or more embodiments of this specification.

Claims

1. A method for constructing and processing gridded multi-source remote sensing data, characterized in that, Includes the following steps: Step S1: Obtain remote sensing image data, vector data, point cloud data and TIN data from the data source list, and perform coordinate benchmark unification and format standardization on each data to obtain a standardized dataset D0; Step S2: Based on the G-DGGS engine and GeoSOT engine, perform hierarchical meshing in the three-dimensional space within the boundary of the target area to generate an initial multi-level three-dimensional mesh set G0. Step S3: Perform geometry-mesh overlay and coverage determination on the points, lines, surfaces and TIN geometric features in the normalized dataset D0, and map each feature to the corresponding mesh cell in the initial multi-level 3D mesh set G0, to obtain the remote sensing image mesh cell set M1, vector data mesh cell set M2, point cloud data mesh cell set M3 and TIN data mesh cell set M4 respectively. Then merge the remote sensing image mesh cell set M1, vector data mesh cell set M2, point cloud data mesh cell set M3 and TIN data mesh cell set M4 to obtain the feature-mesh mapping set M0. Step S4: Perform grid encoding on each grid cell in the feature-grid mapping set M0 to generate a unique grid code C, and perform decoding on the grid code to obtain the three-dimensional spatial extent, and establish the code-spatial extent mapping table E; Step S5: Based on the initial multi-level three-dimensional mesh set G0, perform hierarchical and proximity calculations on the mesh cells to obtain the parent-child relationship table P and the neighborhood relationship table N; Step S6: Perform intersection, union, and complement operations on each pair of grid cell sets Mi corresponding to different data sources to obtain spatial intersection result set I, spatial union result set U, and spatial difference result set D; Step S7: Refine the mixed mesh cells in the spatial intersection result set I and the spatial union result set U, and refine the mesh cells with effective mesh density below a preset threshold. Hierarchical aggregation is performed on the grid cells corresponding to the sparsely covered regions, and a multi-scale grid set Gms is generated based on the refinement and aggregation results. Step S8: Based on the grid encoding C, perform spatial index construction and data block attachment on the multi-scale grid set Gms to generate a unified grid index table Index. Encapsulate Index, E, P, N, I, U, D and Gms into a gridded integrated dataset R.

2. The method for constructing and processing multi-source remote sensing data grids according to claim 1, characterized in that, Step S2 includes the following steps: Step S2-1: Call the G-DGGS engine to perform preliminary subdivision of the target area space using discrete mesh partitioning rules, generating the first-level mesh set G11 to G1n; Step S2-2: Call the GeoSOT engine to perform secondary subdivision of the first-level grid set using the regional standard geographic grid coding rules to generate the second-level grid set G21 to G2n; Step S2-3: Based on the mapping relationship between the grid code output by the G-DGGS engine and the geogrid code output by the GeoSOT engine, establish a dual-engine grid correspondence table T; Step S2-4: Based on the dual-engine mesh correspondence table T, perform consistency detection and merging operations on duplicate or overlapping mesh cells to obtain an initial multi-level three-dimensional mesh set G0 that is compatible with both encoding systems.

3. The method for constructing and processing multi-source remote sensing data grids according to claim 1, characterized in that, Step S3, which involves performing geometry-mesh overlay and coverage determination on points, lines, polygons, and TIN geometric features in the normalized dataset D0, includes: For point features, the center point of the grid cell is determined based on their spatial coordinates, and the point features that fall within the range of a certain grid cell are assigned to that grid cell. For linear features, the intersection of line segments and grid cell boundaries is determined based on their node sequence. Line features that intersect with grid cells are segmented and assigned to the corresponding grid cells. For face features, perform polygon and grid cell area overlap determination based on their boundary polygons, and assign face features with an overlap area ratio exceeding a set threshold to the corresponding grid cell. For TIN elements, the volume intersection of the 3D bounding box and the mesh cell is determined based on the spatial range of the triangular facets. Triangular facets with an intersection volume ratio exceeding a set threshold are assigned to the corresponding mesh cell.

4. The method for constructing and processing multi-source remote sensing data grids according to claim 1, characterized in that, Step S4 includes the following steps: Step S4-1: Based on the hierarchical partitioning rules of the G-DGGS engine, generate the first code C1 for each grid cell; Step S4-2: Based on the geolocation division rules of the GeoSOT engine, generate a second code C2 for each grid cell; Step S4-3: According to the dual-engine grid correspondence table in claim 2, perform correspondence matching between C1 and C2 to generate a unique grid code C; Step S4-4: Perform decoding operation on C to parse the longitude, latitude and elevation range, and generate the corresponding three-dimensional spatial bounding box data; Steps S4-5: Summarize the grid code C of each grid cell and its 3D spatial bounding box data, and establish the code-spatial range mapping table E.

5. The method for constructing and processing multi-source remote sensing data grids according to claim 4, characterized in that, Step S5 includes the following steps: Step S5-1: Determine the level L of each grid cell according to the hierarchical position of the grid code C, and obtain the parent grid cell identifier by extracting the position of the previous level, and summarize them to form a parent-child relationship table P; Step S5-2: Within level L, based on the three-dimensional spatial range of the encoding-spatial range mapping table E, retrieve the grid cells that are adjacent in the X, Y, and Z directions, and summarize them to form a neighborhood relationship table N; Step S5-3: Perform consistency check and deduplication on the parent-child relationship table P and the neighborhood relationship table N, and output the corrected parent-child relationship table P and neighborhood relationship table N.

6. The method for constructing and processing multi-source remote sensing data grids according to claim 5, characterized in that, Step S6 includes the following steps: Step S6-1: Filter grid cell pairs with spatial adjacency or hierarchical association based on the parent-child relationship table P and the neighborhood relationship table N; Step S6-2: Perform spatial intersection operation on the selected grid cell pairs to generate spatial intersection result set I; Step S6-3: Perform a spatial union operation on the grid cell set Mi of each data source to generate a spatial union result set U; Step S6-4: Perform a difference operation on the spatial union result set U and the spatial intersection result set I to generate the spatial difference result set D.

7. The method for constructing and processing multi-source remote sensing data grids according to claim 6, characterized in that, Step S7 includes the following steps: Step S7-1: Based on the spatial overlap ratio of the grid cells in the spatial intersection result set I, select those with an overlap ratio exceeding a set threshold. For the region, perform the next level of subdivision on the grid cells within that region to generate a refined grid set F; Step S7-2: Based on the effective grid density of the grid cells in the spatial union result set U, determine whether the effective grid density is lower than a preset threshold. The region is a sparsely covered area, and hierarchical aggregation is performed on the grid cells within this region to generate an aggregated grid set A; Step S7-3: Merge the refined mesh set F and the aggregated mesh set A in spatial order to form a multi-scale mesh set Gms; Step S7-4: Recalculate the parent-child hierarchy and neighborhood relationships for the grid cells in Gms, and update the corresponding parent-child relationship table P and neighborhood relationship table N.

8. A multi-source remote sensing data gridding construction and processing system, used to implement the multi-source remote sensing data gridding construction and processing method as described in any one of claims 1-7, characterized in that, The system includes: The data acquisition unit is used to acquire remote sensing image data, vector data, point cloud data and TIN data from the data source list, and to perform coordinate benchmark unification and format standardization on each data to obtain a standardized dataset D0. Mesh building unit, used to perform three-dimensional spatial hierarchical meshing within the target region boundary based on the G-DGGS engine and GeoSOT engine, to generate an initial multi-level three-dimensional mesh set G0; The feature mapping unit is used to perform geometric-mesh overlay and coverage determination on the points, lines, polygons and TIN geometric features in the normalized dataset D0, generate mesh cell sets M1, M2, M3 and M4 for various types of data, and merge them to form the feature-mesh mapping set M0; The encoding processing unit is used to generate a unique grid code C for each grid cell according to the dual-engine grid correspondence table, and to perform decoding operations to obtain the three-dimensional spatial range and establish an encoding-spatial range mapping table E. The topology calculation unit is used to calculate the parent-child relationship and the proximity relationship based on the grid code C, and generate the parent-child relationship table P and the neighborhood relationship table N; The set operation unit is used to perform spatial intersection, spatial union, and difference operations on grid cell sets Mi from different data sources based on the parent-child relationship table P and the neighborhood relationship table N, generating spatial intersection result set I, spatial union result set U, and spatial difference result set D; Multi-scale building blocks are used to refine the mixed grid cells of the spatial intersection result set I and the spatial union result set U, perform hierarchical aggregation on the grid cells covering sparse regions, generate a multi-scale grid set Gms, and update the parent-child relationship table P and the neighborhood relationship table N. The index encapsulation unit is used to perform spatial index construction and data block attachment on the multi-scale grid set Gms based on grid encoding C, generate a unified grid index table Index, and encapsulate Index, E, P, N, I, U, D and Gms into a gridded unified dataset R.

9. A terminal, characterized in that, include: The memory is used to store the gridding construction and processing program for multi-source remote sensing data; The processor is used to implement the steps of the multi-source remote sensing data gridding construction and processing method as described in claim 1 when executing the multi-source remote sensing data gridding construction and processing device.

10. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions. When the computer reads the computer instructions from the storage medium, the computer executes the multi-source remote sensing data gridding construction and processing method as described in claim 1.

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