Land satellite image law enforcement inspection pattern spot compliance examination method

Abstract

The invention provides a land satellite image law enforcement inspection pattern spot compliance review method, and belongs to the technical field of land law enforcement supervision, and the method comprises the steps: S1, converting an SHP file into GeoJson data; s2, processing and classifying the GeoJson data structure; s3, pattern spot data features are extracted and processed through a spatial data processing technology; s4, the analyzed pattern spots, the land supply and the approved pattern layers are subjected to nesting and plotting, the land defense film law enforcement inspection pattern spot compliance reviewing method solves the problems that land official documents and land supply official documents are multiple in file and need to be manually opened and looked up respectively, and the efficiency is low, the efficiency is high, and the method is suitable for large-scale popularization and application of the land defense film law enforcement inspection pattern spot compliance reviewing. In addition, the huge investment of time cost and labor cost is greatly reduced when images are formed according to land parcel coordinates and exploration coordinates, coordinate conversion is needed for non-one coordinate system, then monitoring image spots are sleeved, spatial overlay analysis is utilized, the area of the overlapped part with the monitoring image spots is counted, and the overlapping rate is calculated so as to judge the legality of the image spots.

Description

Technical Field

[0001] This invention belongs to the field of land law enforcement and supervision technology, and specifically relates to a method for reviewing the compliance of land satellite imagery for law enforcement inspection. Background Technology

[0002] Land satellite imagery enforcement refers to an enforcement method that utilizes satellite remote sensing technology to acquire surface images, and through image processing, change detection, and other means, monitors land use and identifies and punishes illegal land use. In recent years, with the continuous improvement of satellite remote sensing technology, the resolution of the acquired surface images has increased from 2 meters to 0.75, 0.5, and 0.3 meters, acting like "eyes in the sky." These satellite images, with their keen observation, monitor land use and identify and punish illegal land use.

[0003] Its working principle is as follows: by acquiring satellite remote sensing images at different time periods, and using techniques such as spectral analysis to compare images from different periods, land use changes are detected. Change areas are then extracted, generating monitoring patches. These patches are then distributed level by level by the natural resources authorities to the natural resources departments of each district and county, who are instructed to organize and conduct verification of the patches. The district and county natural resources departments verify each monitored patch by reviewing and retrieving legal land use data within their jurisdictions, providing evidence and submitting results. During the verification process, the district and county natural resources departments perform tasks such as finding legal land use data for the monitored patches, mapping, data overlay, compiling overlay maps, and uploading supporting materials until the monitoring patches are determined and the results are submitted.

[0004] The existing methods for auditing and monitoring data patches have at least the following problems: 1. There are many land approval documents and land supply approval documents, which need to be opened and checked manually, which is inefficient; 2. The review of each monitoring patch requires a lot of work: maps are generated separately based on the coordinates of the land parcel and the boundary survey coordinates. Since they are not in the same coordinate system, coordinate transformation is also required. Then, the monitoring patches are overlaid, and spatial overlay analysis is used to count the area of ​​the overlapping part with the monitoring patch and calculate the overlap rate to determine the legality of the patch. The time and manpower costs are huge. Therefore, a method for land satellite imagery law enforcement inspection of patch compliance review is needed to solve this problem. Summary of the Invention

[0005] To achieve the above objectives, the present invention provides a method for reviewing the compliance of land satellite imagery for law enforcement inspections, comprising the following steps: S1: Convert SHP files to GeoJson data; S2: Process and classify the GeoJson data structure; S3: Then, feature extraction and spatial processing are performed on the GeoJSON data to extract the features of the map patch data, and then the data is processed using spatial data processing techniques. S4: Overlay and merge the analyzed map features, land supply layers, and approval layers to generate a map.

[0006] Further, step S1 converts the SHP file into GeoJson data, specifically including the following steps: S11: The user selects an SHP compressed file; S12: Verify file type and size; S13: Create a FormData object; S14: Add file and region information; S15: Call the shpCoverGeoJson API; S16: Parse into GeoJSON format data; When parsing is successful, obtain shpFileld and then prepare to process the GeoJSON data; S17: If parsing fails, display an error message, and then repeat steps S11-S16 above.

[0007] Furthermore, the backend processing of calling the shpCoverGeoJson API in step S15 specifically includes the following steps: S151: Receive SHP files through backend processing; S152: Decompress the SHP compressed file; S153: Read SHP elements; S154: Convert to GeoJSON; S155: Save to database; S156: Returns shpFileld.

[0008] Furthermore, the algorithm for extracting the geometric features of the image patches and calculating the center point in step S3 includes the following steps: S311: Parse GeoJSON formatted patch data; S312: Analyze the polygonal geometry of the polygonal features; S313: Select the best center point representation, and then extract the key geometric features of area and perimeter by analyzing the distribution characteristics of polygon vertices; S314: By analyzing the extreme values ​​of boundary coordinates, spatial positioning characteristics of the map features are established, providing basic data support for subsequent spatial relationship calculations and view rendering.

[0009] Furthermore, the algorithm for calculating spatial relationships and labeling attributes of map features in step S3 is based on spatial geometric relationships and azimuth theory to achieve intelligent determination of labeling positions, specifically including the following steps: S321: Collect the center point and centroid data of the map patches, calculate the azimuth angle of each map patch centroid relative to the center point of the total map patches using turf.bearing, and then establish coordinate quadrant partitions; S322: Use turf.envelope to obtain the boundary encapsulation of the patch, and then combine it with turf.rhumbDistance to calculate the optimal distance from the centroid to the boundary; S323: Based on the calculated azimuth and distance, use the turf.destination function to determine the position of the marker points to avoid overlapping markers; S324: Create a connection line from the centroid of the patch to the annotation point, and add the patch number and attribute information at the annotation point.

[0010] Furthermore, the intelligent annotation algorithm for determining the intelligent annotation location specifically includes the following steps: S331: Calculate the center of the total map patch; S332: Calculate the relative azimuth angle; S333: Analyze quadrant positions.

[0011] Furthermore, the adaptive boundary calculation and view rendering algorithm for visualization technology in step S4 specifically includes the following steps: S411: Merge the multi-layer GeoJSON feature sets to form a unified feature set; S412: Use the turf.bbox function to calculate the minimum bounding box (bbox) of all patches and obtain the coordinate range [minX, minY, maxX, maxY]; S413: Set appropriate padding parameters for the calculated bounding box and call the map engine's fitBounds method for view adaptive positioning; S414: Combine the density and distribution characteristics of the polygons to dynamically calculate the most suitable zoom level to ensure that all polygons are fully visible in the view and occupy an appropriate proportion; S415: Performs view adaptation (fitBounds), using the map engine's animation capabilities to smoothly transition to the target view state.

[0012] Furthermore, the boundary optimization in step S412 specifically includes the following steps: S421: Construct the boundary rectangle; S422: Analyze the distribution density of the map patches; S423: Adjust view margin parameters.

[0013] Furthermore, the automatic generation and annotation algorithm for intelligent patch overlay in step S4 specifically includes the following steps: S431: Set up the map drawing buffer, save the map state, and then get the current view canvas of the map; S432: Create an overlay composite canvas, then draw the map base map content; S433: Add title, legend and compass elements, and dynamically adjust the annotation content and style according to the type of map feature; S434: Apply the title text centering and border drawing algorithm in the layout of graphic elements to ensure the professionalism and standardization of graphic layout; S435: Finally, convert the synthesized Canvas into an image format and upload and save it.

[0014] The advantages of this invention are: 1. By using a method for extracting geometric features of map features and calculating center points: based on multi-center point calculation technology implemented in turf.js, the problem of accurate positioning of complex map features is solved; 2. Through spatial relationship calculation and intelligent annotation method of map features: automatic calculation technology of annotation position based on azimuth and distance to avoid overlapping annotation of multiple map features; 3. Through adaptive boundary calculation and view rendering methods: dynamically analyze the distribution of polygons to automatically calculate the optimal view range; 4. Automatic generation and standardized output of overlay maps: Based on Canvas's professional map automatic generation technology, the standard maps required for land law enforcement inspections can be produced in batches.

[0015] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of step S1 of the present invention.

[0017] Figure 2 This is a schematic diagram of the algorithm for extracting geometric features of map patches and calculating center points according to the present invention.

[0018] Figure 3 This is a schematic diagram of the algorithm for calculating spatial relationships and labeling attributes of map features in this invention.

[0019] Figure 4 This is a schematic diagram of the adaptive boundary calculation and view rendering algorithm of the present invention.

[0020] Figure 5 This is a schematic diagram of the algorithm for automatic generation and attribute annotation of overlay diagrams in this invention.

[0021] Figure 6 This is a schematic diagram of the geospatial data structure system of the present invention. Detailed Implementation

[0022] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the specific implementation methods, structural features and effects of the present invention are described in detail below with reference to the accompanying drawings and embodiments.

[0023] 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.

[0024] In the description of this invention, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "aligned", "overlapping", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0025] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature; in the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0026] Example 1

[0027] This embodiment provides, for example Figures 1-6 The present invention provides a method for reviewing the compliance of land satellite imagery for law enforcement inspections, comprising the following steps: S1: Convert SHP files to GeoJson data, enabling the import of SHP files and the conversion of GeoJson data. SHP files are a commonly used vector data format in Geographic Information Systems (GIS) for storing and exchanging spatial data. The system first implemented an automated process for users to upload SHP compressed files to the server and parse them into GeoJSON format. GeoJSON is a JSON-based data format specifically designed for storing geospatial data, such as points, lines, and polygons. Then, data conversion and storage are achieved through API interfaces, providing basic data support for subsequent patch overlay analysis; S2: Process and classify the GeoJson data structure; By designing a special GeoJSON data structure to address the specific needs of land satellite imagery enforcement map overlay, the system includes two core types: supply map patches and approval map patches, along with their related attributes, enabling structured storage and processing of different map patch types. S3: Extract features from the map patch data and process them using spatial data processing techniques; The system extracts features and performs spatial processing on GeoJSON data, enabling geometric feature analysis, spatial relationship calculation, and attribute information extraction of map features, thus providing a foundation for subsequent overlay analysis. S4: Overlay and generate a map from the analyzed map features, land supply layers, and approval layers; The system utilizes intelligent patch overlay and visualization technology to achieve spatial overlay between different layers, display visual differences, and intuitively present the overlay results, providing strong decision support for law enforcement personnel.

[0028] Among them, the two core types, namely, map patches and map patches for approval, and their related attributes are as follows: xzqdm: County-level administrative division code; xzqmc: Name of a county-level administrative region; jcbh: The satellite imagery plot number involved; gdwh: Land grant document number; tdzh: Land certificate number; pzwh: Approval number; pzxmmc: Approved project name; pzmj: Approved land area in mu (a Chinese unit of area); zgdmj: Approved cultivated land area in mu (a Chinese unit of land area); jydmj: Net land area; bz: Other situations requiring further explanation.

[0029] Furthermore, step S1 involves an automated process from the user uploading the SHP compressed file to the server and parsing it into GeoJSON format, specifically including the following steps: S11: The user selects an SHP compressed file; S12: Verify file type and size; S13: Create a FormData object; S14: Add file and region information; S15: Call the shpCoverGeoJson API; S16: Parse into GeoJSON format data; When parsing is successful, obtain shpFileld and then prepare to process the GeoJSON data; S17: If parsing fails, display an error message, and then repeat steps S11-S16 above.

[0030] The user first selects an SHP compressed file, then verifies the file type and size, then creates a FormData object, adds file and region information, and then calls the shpCoverGeoJson API. The backend receives the SHP file, decompresses it, reads the SHP features, converts it to GeoJSON, saves it to the database, and returns a shpFileld. If parsing is successful, the shpFileld is retrieved, and the GeoJSON data is prepared for processing. If parsing fails, an error message is displayed, and then the above steps are repeated sequentially.

[0031] Furthermore, the backend processing of calling the shpCoverGeoJson API in step S15 specifically includes the following steps: S151: Receive SHP files through backend processing; S152: Decompress the SHP compressed file; S153: Read SHP elements; S154: Convert to GeoJSON; S155: Save to database; S156: Returns shpFileld.

[0032] Furthermore, the algorithm for extracting the geometric features of the image patches and calculating the center point in step S3 includes the following steps: S311: Parse GeoJSON formatted patch data; S312: Analyze the polygonal geometry of the polygonal features; For regular polygons, the geometric center of the polygon is calculated using turf.center and used as the center point. For irregular or non-convex polygons, the centroid of the polygon is calculated using turf.centerOfMass and used as the center point. S313: Select the best center point representation, and then extract the key geometric features of area and perimeter by analyzing the distribution characteristics of polygon vertices; S314: By analyzing the extreme values ​​of boundary coordinates, spatial positioning characteristics of the map features are established, providing basic data support for subsequent spatial relationship calculations and view rendering.

[0033] The algorithm for extracting geometric features and calculating center points of geographic features combines various geometric operation functions from the turf.js spatial analysis library to achieve accurate analysis of complex geographic features. The turf.js library is a geospatial analysis library. The algorithm first parses the geographic feature data in GeoJSON format and uses turf.center to calculate the geometric center of the feature as a global reference point. For irregular or non-convex polygons, turf.centerOfMass is applied to calculate the centroid, improving the accuracy of the center point representation. Simultaneously, by analyzing the distribution characteristics of polygon vertices, key geometric properties such as area and perimeter are extracted. Finally, through boundary coordinate extreme value analysis, spatial positioning features of the geographic features are established, providing basic data support for subsequent spatial relationship calculations and view rendering. This algorithm effectively solves the problem of accurate center point positioning for geographic features of different shapes, ensuring the accuracy of spatial analysis.

[0034] Among them, 1. The calculation formula for turf.center: Calculate the arithmetic mean of the coordinates of all vertices:

[0035] geojson: Input Feature or FeatureCollection, supporting geometric types such as points, lines, and polygons.

[0036] Note: The result may not be inside the polygon, but only represents the geometric center. 2. The formula for calculating turf.centerOfMass: Based on the polygon centroid formula (area-weighted average):

[0037] A: Area of ​​the polygon geojson: Enter GeoJSON, which supports single polygons or collections.

[0038] options.properties: Allows passing properties to the result point.

[0039] Problem: There may be calculation errors for polygon sets.

[0040] Furthermore, the algorithm for calculating spatial relationships and labeling attributes of map features in step S3 is based on spatial geometric relationships and azimuth theory to achieve intelligent determination of labeling positions, specifically including the following steps: S321: Calculate the azimuth angle turf.bearing using the center point and centroid data of the map patch, and establish coordinate quadrant partitions; S322: Use turf.envelope to obtain the boundary encapsulation of the patch, and combine it with turf.rhumbDistance to calculate the optimal distance from the centroid to the boundary; S323: Based on the calculated azimuth and distance, use the turf.destination function to determine the position of the marker points to avoid overlapping markers; S324: Create a connection line from the centroid of the patch to the annotation point, and add the patch number and attribute information at the annotation point.

[0041] Among them, 1. The formula for calculating turf.bearing is: Based on the azimuth angle of a spherical great circle route (Haversine formula):

[0042] from / to: Coordinates of the starting and ending points ([lon,lat]) or Point elements.

[0043] options.units: Output units (default).

[0044] 2. turf.envelope Calculation formula: Calculate the minimum bounding rectangle (MBR):

[0045] geojson: Input features, supports any geometry type.

[0046] Returns: Rectangular Polygon Feature <polygon>.

[0047] 3. Formula for calculating turf.rhumbDistance: Constant-direction distance (equiangular route):

[0048] : represents the average latitude.

[0049] Parameter description: •from / to: starting point and ending point.

[0050] options.units: Distance units (default kilometers).

[0051] 4. turf.destination calculation formula: Calculation of endpoint coordinates based on the Haversine formula:

[0052] origin: starting point coordinates or Point.

[0053] distance / bearing: distance and azimuth.

[0054] options.units: Distance units (default kilometers).

[0055] The algorithm for calculating spatial relationships and labeling attributes of map features is based on spatial geometry and azimuth theory to achieve intelligent labeling location determination. The algorithm first calculates the azimuth angle of each feature's centroid relative to the center point of the total map feature using `turf.bearing`, establishing coordinate quadrant partitions. Then, it uses `turf.envelope` to obtain the feature boundary encapsulation and combines it with `turf.rhumbDistance` to calculate the optimal distance from the centroid to the boundary. Based on the calculated azimuth angle and distance, the `turf.destination` function is applied to determine the precise location of the labeling point to avoid label overlap. Finally, a connection line is created from the feature centroid to the labeling point, and feature number and attribute information are added to the labeling point location. This method effectively solves the labeling location conflict problem in multi-feature scenarios through precise spatial geometric calculations, improving the clarity and readability of feature identification. Furthermore, the intelligent annotation algorithm for determining the intelligent annotation location specifically includes the following steps: S331: Calculate the center of the total map patch; S332: Calculate the relative azimuth angle; S333: Analyze quadrant positions; S334: Avoid overlapping annotations.

[0056] Furthermore, the visualization technology in step S4 includes adaptive boundary calculation and view rendering algorithms, which specifically include the following steps: S411: Merge the multi-layer GeoJSON feature sets to form a unified feature set; S412: Use the turf.bbox function to calculate the minimum bounding box (bbox) of all patches and obtain the coordinate range [minX, minY, maxX, maxY]; S413: Set appropriate padding parameters for the calculated bounding box and call the map engine's fitBounds method for view adaptive positioning; S414: Combine the density and distribution characteristics of the polygons to dynamically calculate the most suitable zoom level to ensure that all polygons are fully visible in the view and occupy an appropriate proportion; S415: Performs view adaptation (fitBounds), using the map engine's animation capabilities to smoothly transition to the target view state.

[0057] The formula for calculating turf.bbox is as follows: Similar to envelope, but returns bounding boxes in array format:

[0058] geojson: Input features.

[0059] options.recompute: Whether to ignore existing bbox properties.

[0060] Application: Commonly used for fast spatial query optimization.

[0061] The adaptive boundary calculation and view rendering algorithm achieves optimal view display by dynamically analyzing the distribution range of polygons. First, it merges and processes the GeoJSON features of all relevant layers to form a unified feature set. Then, it applies the `turf.bbox` function to calculate the minimum bounding box (bbox) of all polygons, obtaining the coordinate range [minX, minY, maxX, maxY]. Appropriate padding parameters are set for the calculated bounding boxes, and the map engine's `fitBounds` method is called for adaptive view positioning. Simultaneously, considering the density and distribution characteristics of the polygons, the algorithm dynamically calculates the most suitable zoom level to ensure that all polygons are fully visible and occupy an appropriate proportion in the view. Finally, through the map engine's animation capabilities, it smoothly transitions to the target view state. This algorithm effectively solves the problem of automatic adaptation of polygons of different sizes and distributions, improving the map interaction experience. Furthermore, the boundary optimization in step S412 specifically includes the following steps: S421: Construct the boundary rectangle; S422: Analyze the distribution density of the map patches; S423: Adjust view margin parameters.

[0062] Furthermore, the automatic generation and annotation algorithm for intelligent patch overlay in step S4 specifically includes the following steps: S431: Set up the map drawing buffer, save the map state, and then get the current view canvas of the map; The algorithm for centering text and drawing borders is applied to ensure the professionalism and standardization of the graphic layout; finally, the composite Canvas is converted into an image format and uploaded and saved via API. S432: Create an overlay composite canvas, then draw the map base map content; S433: Add title, legend and compass elements, and dynamically adjust the annotation content and style according to the type of map feature; S434: Apply algorithms to center title text, calculate legend position, place compass and scale bar, and lay out map attribute information and draw borders in the layout of map elements to ensure the professionalism and standardization of map layout. S435: Finally, convert the Canvas to an image format and upload and save it.

[0063] The automatic generation and annotation algorithm for overlay maps is based on Canvas drawing technology and spatial data visualization principles to achieve standardized map production. The algorithm first sets up a drawing buffer to save the map state and obtains the current view's map canvas; it creates a new Canvas element as a composite canvas, setting appropriate sizes and backgrounds; it draws the map content onto the composite canvas as the base map of the overlay map; it adds standardized map elements, including titles, patch numbers, scale bars, north arrows, and legends; it dynamically adjusts the annotation content and style according to the patch type (land supply or approval); it applies text centering and border drawing algorithms to ensure the professionalism and standardization of the map layout; finally, it converts the composite Canvas into an image format and uploads and saves it via API. This algorithm combines GIS spatial data processing and graphic design principles to automatically generate high-quality overlay maps that meet land management regulations, significantly improving map production efficiency and consistency.

[0064] like Figure 6 As shown, this is the specific attribute data returned by the backend after analyzing the shapefile; 1. First, RootObject has 5 properties: These are gytb (geojson data of land supply plots); bptd (geojson data of land approval plots); tdgy (land supply data); tdbp (land approval data); tb (the geojson data of the entire patch); 2. Secondly, there is the FeatureCollection data format in GeoJSON, which is mainly used to store data formats for geospatial feature collections; 3. The properties attribute in FeatureCollection is mainly used to store information in the shapefile, namely the two core types of map patches and approval patches and their related attributes.

[0065] In summary, this invention provides a method for reviewing the compliance of land satellite imagery for law enforcement inspection. It not only solves the problem of numerous land approval documents and land supply approval documents requiring manual review, which is inefficient, but also addresses the need for separate mapping based on parcel coordinates and boundary coordinates, coordinate transformation due to the non-uniform coordinate systems, and then overlaying the monitoring images. By utilizing spatial overlay analysis to calculate the area of ​​overlapping portions with the monitoring images and the overlap rate, the invention significantly reduces the substantial time and manpower costs required for determining the legality of the images.

[0066] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.< / polygon>

Claims

1. A method for reviewing the compliance of land satellite imagery for law enforcement inspection, characterized in that: Includes the following steps: S1: Convert SHP files to GeoJson data; S2: Process and classify the GeoJson data structure; S3: Then, feature extraction and spatial processing are performed on the GeoJSON data to extract the features of the map patch data, and then the data is processed using spatial data processing techniques. S4: Using intelligent map patch overlay and visualization technology, the analyzed map patches, land supply and approval layers are overlaid and mapped.

2. The method for reviewing the compliance of land satellite imagery enforcement inspection patches as described in claim 1, characterized in that: Step S1 converts the SHP file into GeoJson data, specifically including the following steps: S11: The user selects an SHP compressed file; S12: Verify file type and size; S13: Create a FormData object; S14: Add file and region information; S15: Call the shpCoverGeoJson API; S16: Parse into GeoJSON format data; When parsing is successful, obtain shpFileld and then prepare to process the GeoJSON data; S17: If parsing fails, display an error message, and then repeat steps S11-S16 above.

3. The method for reviewing the compliance of land satellite imagery enforcement inspection plots as described in claim 2, characterized in that: The backend processing of calling the shpCoverGeoJson API in step S15 specifically includes the following steps: S151: Receive SHP files through backend processing; S152: Decompress the SHP compressed file; S153: Read SHP elements; S154: Convert to GeoJSON; S155: Save to database; S156: Returns shpFileld.

4. The method for reviewing the compliance of land satellite imagery enforcement inspection patches as described in claim 1, characterized in that: The algorithm for extracting the geometric features of the patch and calculating the center point in step S3 includes the following steps: S311: Parse GeoJSON formatted patch data; S312: Analyze the polygonal geometry of the polygonal features; S313: Select the best center point representation, then analyze the distribution characteristics of polygon vertices, and extract key geometric features of area and perimeter; S314: Establish spatial positioning features of map features through boundary coordinate extreme value analysis.

5. The method for reviewing the compliance of land satellite imagery enforcement inspection patches as described in claim 4, characterized in that: Step S3, the algorithm for calculating spatial relationships and labeling attributes of map features, specifically includes the following steps: S321: Collect the center point and centroid data of the map patches, calculate the azimuth angle of each map patch centroid relative to the center point of the total map patches using turf.bearing, and then establish coordinate quadrant partitions; S322: Use turf.envelope to obtain the boundary encapsulation of the patch, and then combine it with turf.rhumbDistance to calculate the optimal distance from the centroid to the boundary; S323: Use the turf.destination function to determine the location of the marker point based on the calculated azimuth and distance; S324: Create a connection line from the centroid of the patch to the annotation point, and add the patch number and attribute information at the annotation point.

6. The method for reviewing the compliance of land satellite imagery enforcement inspection patches as described in claim 5, characterized in that: The intelligent annotation algorithm for determining the intelligent annotation location includes the following steps: S331: Calculate the center of the total map patch; S332: Calculate the relative azimuth angle; S333: Analyze quadrant positions.

7. The method for reviewing the compliance of land satellite imagery enforcement inspection plots as described in claim 1, characterized in that: The adaptive boundary calculation and view rendering algorithm for visualization technology in step S4 specifically includes the following steps: S411: Merge the multi-layer GeoJSON feature sets to form a unified feature set; S412: Use the turf.bbox function to calculate the minimum bounding box (bbox) of all patches and obtain the coordinate range [minX,minY, maxX, maxY], and then perform the boundary optimization in step S412; S413: Set appropriate padding parameters for the calculated bounding box and call the map engine's fitBounds method for view adaptive positioning; S414: Combine the density and distribution characteristics of the polygons to dynamically calculate the most suitable zoom level to ensure that all polygons are fully visible in the view and occupy an appropriate proportion; S415: Performs view adaptation (fitBounds), using the map engine's animation capabilities to smoothly transition to the target view state.

8. The method for reviewing the compliance of land satellite imagery enforcement inspection plots as described in claim 7, characterized in that: The boundary optimization in step S412 specifically includes the following steps: S421: Construct the boundary rectangle; S422: Analyze the distribution density of the map patches; S423: Adjust view margin parameters.

9. The method for reviewing the compliance of land satellite imagery enforcement inspection plots as described in claim 1, characterized in that: The automatic generation and annotation algorithm for intelligent patch overlay in step S4 specifically includes the following steps: S431: Set up the map drawing buffer, save the map state, and then get the current view canvas of the map; S432: Create an overlay composite canvas and draw the map base map content; S433: Add title, legend and compass elements, and dynamically adjust the annotation content and style according to the type of map feature; S434: Algorithm for centering title text and drawing borders in the layout of applied graphic elements; S435: Finally, convert the synthesized Canvas into an image format and upload and save it.

Images