A land greening space marking method, system, terminal and storage medium

Abstract

The application provides a land greening space marking method, system, terminal and storage medium, relates to the technical field of land surveying and mapping, and comprises the following steps: constructing a resource status database according to the latest land change survey results and the forest and grass wet survey monitoring results; converting the resource status database into a grid image, setting a seed point, merging similar pixel regions, and segmenting the grid image into land map patches; taking site factors as growth constraints of the land map patches to obtain initial plot data; performing vectorization processing on the initial plot data to obtain land greening land map patches; constructing a control boundary database and superimposing the control boundary database on the land greening land map patches, removing overlapping map patches in the superimposed image, and obtaining initial greening map patches; performing erosion and expansion processing on the initial greening map patches, and adaptively adjusting the size of a structural element of the initial greening map patches after the erosion and expansion processing to obtain vector achievement data; and the application has the advantages of improving the accuracy of land greening space marking.

Description

Technical Field

[0001] This application relates to the technical field of land surveying, and in particular to a method, system, terminal and storage medium for marking land greening spaces. Background Technology

[0002] Existing technologies have significant shortcomings in the basic data processing and land parcel segmentation stages of land greening spatial annotation. When constructing the resource database, the latest land use change survey results and forestry and wetland survey and monitoring results are not integrated, relying only on a single data source. As a result, the database cannot comprehensively reflect the dynamic changes in land use and the current status of vegetation and wetland resources, and the timeliness and completeness of the data are insufficient. When converting the database into raster images to segment land parcels, seed points are not set in uniform areas based on pixel spectral characteristics, nor are similar pixels merged through regional growth. Only simple grid division or manual segmentation is used, resulting in blurred boundaries and confused land use attributes of the segmented land parcels. Furthermore, no topological checks are performed to correct gaps and overlaps, making it impossible to accurately represent the actual distribution range of different land uses. This leads to low-quality basic land parcels provided for subsequent greening suitability screening.

[0003] Existing technologies have significant shortcomings in the suitability assessment and land parcel optimization stages for greening. When determining candidate greening sites, site factors such as soil type, slope, and aspect are not considered as growth constraints for land parcels. Instead, the determination of greenable areas relies solely on surface cover type identified through image recognition, ignoring the impact of natural conditions on greening. This results in selected sites potentially being unsuitable for actual greening due to issues such as poor soil or steep slopes. Furthermore, the construction of the control boundary database fails to integrate multiple vector ranges, including cultivated land and construction land, and lacks coordinate unification and format standardization. When overlaid onto greening land parcels, overlapping areas cannot be accurately identified, and overlapping and fragmented parcels are not removed. Finally, the initial greening parcels are not subjected to erosion and expansion treatment or adaptive adjustment of structural element dimensions. This results in small protrusions, internal holes, and fragmented areas at parcel boundaries. Consequently, the generated greening annotations are disconnected from actual land use attributes and natural conditions, exhibiting serious deficiencies in accuracy and practicality. Summary of the Invention

[0004] To improve the accuracy of land greening space labeling, this application provides a land greening space labeling method, system, terminal, and storage medium.

[0005] Firstly, this application provides a method for marking land green space, which adopts the following technical solution:

[0006] A method for marking land green space includes:

[0007] S1. Based on the latest land change survey results and forestry, grassland and wetland survey and monitoring results, construct a resource status database for national land space;

[0008] S2. Convert the resource status database into a raster image, set seed points, and merge similar pixel regions to segment the raster image into land patches;

[0009] S3. Using site factors as growth constraints for the land parcels, the initial land parcel data of the national land space is obtained;

[0010] S4. The initial land parcel data is vectorized to obtain the green land patches of the national land space;

[0011] S5. Construct a control boundary database for the aforementioned national land space based on the spatial vector ranges of cultivated land, construction land, nature reserves, and mining rights.

[0012] S6. Overlay the control boundary database onto the land greening land patch, and simultaneously remove overlapping patches in the overlaid image to obtain the initial greening patch of the land space;

[0013] S7. The initial greening patches are subjected to erosion and expansion processing, and the structural element size of the initial greening patches after erosion and expansion processing is adaptively adjusted to obtain vector result data of greening annotation in the land space.

[0014] By adopting the above technical solution, a resource status database is constructed based on the latest land change survey results and forestry, grassland and wetland survey and monitoring results. Then, various site factors in the site factor database are labeled onto the resource status data in the resource status database to extract land parcels that can be greened, thereby extracting spaces that can be greened under natural conditions. Finally, after screening the land parcels for greening according to the control boundary database, the results of land greening space labeling are obtained, thereby screening out spaces that can be used for greening based on land attributes, and thus improving the accuracy of land greening space labeling.

[0015] Optionally, the steps of labeling various site factors from the site factor database onto the resource status data in the resource status database to extract land parcels for land greening include:

[0016] Various site factors from the site factor database are labeled onto the resource status data in the resource status database to generate labeled resource status data.

[0017] Determine whether the labeled resource status data meets the requirements of the preset greenable land type;

[0018] If it does not meet the requirements, the corresponding labeled resource status data will be removed.

[0019] If the conditions are met, the corresponding labeled resource status data will be defined as resource data with appropriate type attributes.

[0020] Land parcels for land greening are extracted from suitable resource data based on type attributes.

[0021] By adopting the above technical solution, site factors are labeled to the resource status data in the resource status database to obtain labeled resource status data. Then, labeled resource status data that meet the requirements of greenable land type are selected as type attribute suitable resource data. Land parcels that can be greened are extracted from the type attribute suitable resource data, ensuring that the use attribute of the extracted parcels belongs to land that can be greened, thereby improving the accuracy of extracting land parcels that can be greened.

[0022] In a preferred embodiment, the steps of converting the resource status database into a raster image, setting seed points, and merging similar pixel regions to segment the raster image into land patches include:

[0023] Based on the land category attribute field in the resource status database, the vector format of the resource status database is converted into a raster image with a specified resolution;

[0024] Seed points are automatically set in a uniform region based on the spectral characteristics of the pixels in the raster image.

[0025] Using the seed point as the core, region growth is performed based on the feature similarity between adjacent pixels, while pixels that meet the similarity condition are merged.

[0026] When the growth of the region stops, the merged pixels are identified as the initial segmentation result, and the initial boundary is obtained;

[0027] A topological check is performed on the initial boundary to correct boundary areas with gaps or overlaps, thus obtaining the land patch.

[0028] In a preferred embodiment, the step of using site factors as growth constraints for the land parcels to obtain initial land parcel data for the national land space includes:

[0029] Obtain site factor data including soil type, slope, and aspect;

[0030] The attribute information in the site factor data is assigned to the corresponding land patch through attribute connection;

[0031] Based on the preset greening suitability rules, land parcels with site factor attributes are screened.

[0032] The land parcels that meet the greening suitability rules will be output as the initial land parcel data of the national land space.

[0033] In a preferred embodiment, the step of vectorizing the initial land parcel data to obtain the green land patches of the national land space includes:

[0034] Perform a boundary extraction operation on the initial land parcel data to generate the boundary lines of the initial land parcel data;

[0035] The boundary lines are topologically constructed to form closed polygonal surface features;

[0036] Redundant vertices are eliminated from the polygonal surface features to generate simplified vector boundaries;

[0037] The simplified vector boundary is associated with the attribute information of the initial land parcel data to establish a vector plot with a complete attribute structure;

[0038] Perform geometric consistency verification on the vector map features, and output the vector map features that have successfully passed the consistency verification as land greening land features.

[0039] In a preferred embodiment, the step of constructing the control boundary database of the national land space based on the spatial vector ranges of cultivated land, construction land, nature reserves, and mining rights includes:

[0040] Obtain the original vector datasets of the spatial vector ranges of cultivated land, construction land, nature reserves, and mining rights;

[0041] The original vector dataset is subjected to coordinate system and data format standardization processing to obtain a standardized vector data set;

[0042] Identify spatial overlaps and boundary conflicts between different vector ranges in the standardized vector data set to obtain the control boundary data of the national territory;

[0043] The control boundary data is subjected to attribute structuring processing to obtain the attribute table structure of the national land space;

[0044] Based on the attribute table structure and the control boundary data, a control boundary database for the national territory is constructed.

[0045] In a preferred embodiment, the step of overlaying the control boundary database onto the land greening patch and simultaneously removing overlapping patches from the overlaid image to obtain the initial greening patch of the land space includes:

[0046] Spatial overlay analysis is performed between the control boundary vector data in the control boundary database and the land greening land parcels to obtain the overlay result of the land space;

[0047] Based on the control attribute information in the overlay results, identify the land greening land parcels that spatially overlap with the cultivated land spatial vector range, the construction land vector range, the nature reserve vector range, and the mining rights vector range;

[0048] The identified land greening patches with spatial overlap were erased to obtain intermediate data with the overlapping areas removed.

[0049] Perform an integrity check on the remaining patches in the intermediate data and remove broken patches with too small an area caused by the erasure operation;

[0050] The integrity-checked patches are output as the initial greening patches for the land space.

[0051] In a preferred embodiment, the steps of performing erosion and expansion processing on the initial greening patches, and adaptively adjusting the structural element dimensions of the eroded and expanded initial greening patches to obtain vector result data of greening annotations in the national land space include:

[0052] Remove the small protrusions and isolated pixel areas at the boundary of the initial greening patch to obtain the intermediate patch after erosion.

[0053] After corrosion treatment, the pores inside the intermediate patch and the adjacent broken areas are filled to obtain the optimized patch;

[0054] Based on the geometric features of the optimized patch, the size of the structural element used for morphological processing is adaptively adjusted;

[0055] The optimized patch was subjected to secondary morphological filtering using the adjusted structural element size to obtain vector data of greening annotations in the land space.

[0056] Secondly, this application provides a land greening spatial labeling system, which adopts the following technical solution:

[0057] A land greening spatial labeling system, comprising:

[0058] The initial resource database construction module is used to construct a resource status database of the national land space based on the latest land change survey results and forestry, grassland and wetland survey and monitoring results;

[0059] The raster image segmentation module is used to convert the resource status database into a raster image, and to segment the raster image into land patches by setting seed points and merging similar pixel regions;

[0060] The initial land parcel data generation module is used to take the site factors as growth constraints for the land parcels to obtain the initial land parcel data of the national land space;

[0061] The image vectorization processing module is used to vectorize the initial land parcel data to obtain the green land patches of the national land space;

[0062] The boundary line generation module is used to construct a control boundary database of the national land space based on the spatial vector ranges of cultivated land, construction land, nature reserves, and mining rights.

[0063] The initial greening patch generation module is used to overlay the control boundary database onto the land greening patch, and simultaneously remove overlapping patches in the overlaid image to obtain the initial greening patch of the land space;

[0064] The vector result data acquisition module is used to perform erosion and expansion processing on the initial greening patches, and to adaptively adjust the structural element size of the initial greening patches after erosion and expansion processing, so as to obtain vector result data of greening annotation in the land space.

[0065] Thirdly, this application provides a smart terminal, which adopts the following technical solution:

[0066] A smart terminal includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any of the preceding claims, a method for marking land greening spaces.

[0067] By adopting the above technical solution, and through the operation of a smart terminal, the processor loads and executes a computer program stored in the memory for a land greening spatial labeling method. This constructs a resource status database based on the latest land change survey results and forestry and wetland survey monitoring results. Then, various site factors from the site factor database are labeled onto the resource status data in the resource status database to extract land parcels suitable for land greening. This extracts spaces that can be greened under natural conditions. Finally, after screening the land parcels suitable for land greening according to the control boundary database, the land greening spatial labeling results are obtained, thereby selecting spaces that can be used for greening based on land attributes, thus improving the accuracy of land greening spatial labeling.

[0068] Fourthly, this application provides a computer storage medium capable of storing corresponding programs, which facilitates improving the accuracy of land greening space labeling, and adopts the following technical solution:

[0069] A computer-readable storage medium storing a computer program that can be loaded by a processor and executed by any of the above-mentioned land greening spatial labeling methods.

[0070] By employing the above technical solution, a computer program for a land greening spatial annotation method is stored in a computer-readable storage medium. The processor loads and executes the computer program in the storage medium, thereby constructing a resource status database based on the latest land change survey results and forestry and grassland wetland survey monitoring results. The resource status database is converted into a raster image; seed points are set and similar pixel regions are merged to segment the raster image into land parcels. Site factors are used as growth constraints for the land parcels to obtain initial plot data. The initial plot data is vectorized to obtain land greening land parcels. A control boundary database is constructed and overlaid onto the land greening land parcels. Overlapping parcels in the overlaid image are removed to obtain initial greening parcels. The initial greening parcels are subjected to erosion and dilation processing, and the structural element sizes of the eroded and dilated initial greening parcels are adaptively adjusted to obtain vector result data. This improves the accuracy of land greening spatial annotation.

[0071] In summary, this application includes at least one of the following beneficial technical effects:

[0072] 1. This invention constructs a resource status database by integrating the latest land use change survey results and forestry, grassland, and wetland survey and monitoring results. This ensures that the database covers dynamic changes in land use and the current status of vegetation and wetland resources, providing a data foundation that is both timely and complete for subsequent annotation. After converting the database into a raster image of a specified resolution, seed points are set in uniform areas based on pixel spectral characteristics. Initial segmentation results are generated by merging similar pixels through regional growth. Then, topological checks are performed to correct boundary gaps and overlaps, resulting in land parcels with clear boundaries and accurate land use attributes, significantly improving the quality of basic parcels. Simultaneously, site factors including soil type, slope, and aspect are used as constraints for land parcel growth. Site attributes are assigned to parcels through attribute connections. Combined with greening suitability rules, initial plot data with suitable natural conditions are selected, ensuring the rationality of subsequent greening annotation from the perspective of natural attributes.

[0073] 2. This invention performs vectorization processing on initial land parcel data, including boundary extraction, topology construction, and redundant vertex elimination. It associates attribute information and verifies geometric consistency to obtain complete and geometrically accurate land greening land parcels. A control boundary database is constructed by integrating multiple vector ranges, including cultivated land and construction land. After unifying coordinates and formats, this database is overlaid onto the land greening land parcels. Overlapping areas are erased, and broken parcels are removed to obtain initial greening parcels with compliant land use attributes. Furthermore, the initial greening parcels undergo erosion and dilation processing to eliminate small protrusions and internal holes. The size of structural elements is adaptively adjusted based on the parcel's geometric features, and secondary morphological filtering is performed. Finally, smooth-boundary, structurally complete greening annotation vector data is generated, comprehensively improving the accuracy and practicality of land greening spatial annotation and providing reliable data support for land greening planning. Attached Figure Description

[0074] Figure 1 This is a flowchart of a land greening space labeling method in the embodiments of this application.

[0075] Figure 2 This is a schematic diagram of the structure of a land greening space marking in an embodiment of this application. Detailed Implementation

[0076] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figure 1 , Figure 2 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0077] Reference Figure 1 This application discloses a method for marking land green space, including the following steps:

[0078] S1. Based on the latest land change survey results and forestry, grassland and wetland survey and monitoring results, construct a resource status database for national land space;

[0079] Specifically, the latest land use change survey results are collected to obtain information such as the increase or decrease in area and location of land use types, as well as complete reports and data tables, to provide basic land data for the database; at the same time, the results of forest, grassland and wetland surveys and monitoring are collected to obtain detailed data such as the distribution and ecological status of forest, grassland and wetlands, which serve as the core source of the forest, grassland and wetland section.

[0080] Furthermore, the structural framework of the resource status database was determined, dividing it into primary categories such as land use, forestry and grassland resources, and wetland resources, as well as corresponding secondary subcategories, and clarifying the data items to be stored in each subcategory. The two types of survey data were standardized, with unified formats and measurement standards to ensure compliance with database storage specifications, before being categorized and stored in their respective subcategories.

[0081] Furthermore, standardized data is integrated into the database framework, and corresponding data tables are entered using data entry tools. Various data relationships are established based on location information to form an organic whole. Finally, the integrity and consistency of the database are verified, and missing data or incorrect associations are corrected to ensure that the data accurately reflects the current status of land and space resources, thus forming a complete resource status database.

[0082] In summary, building a resource status database based on the results of the two types of surveys can integrate the dynamics of land use and the real-time status of forests, grasslands and wetlands, cover complete information on multiple types of resources, avoid the incomplete representation caused by single data, and the data has both timeliness and comprehensiveness.

[0083] In summary, through standardization and integration, the resource data in the database forms an organic whole. Subsequent work can directly call the associated attributes without repeatedly obtaining data, providing accurate and efficient data support for land greening labeling and improving labeling accuracy from the source.

[0084] S2. Convert the resource status database into a raster image, set seed points, and merge similar pixel regions to segment the raster image into land patches;

[0085] In this embodiment of the invention, the steps of converting the resource status database into a raster image, setting seed points, and merging similar pixel regions to segment the raster image into land patches include:

[0086] Based on the land category attribute field in the resource status database, the vector format of the resource status database is converted into a raster image with a specified resolution;

[0087] Seed points are automatically set in a uniform region based on the spectral characteristics of the pixels in the raster image.

[0088] Using the seed point as the core, region growth is performed based on the feature similarity between adjacent pixels, while pixels that meet the similarity condition are merged.

[0089] When the growth of the region stops, the merged pixels are identified as the initial segmentation result, and the initial boundary is obtained;

[0090] A topological check is performed on the initial boundary to correct boundary areas with gaps or overlaps, thus obtaining the land patch.

[0091] Specifically, when converting a vector-formatted resource status database into a raster image with a specified resolution, based on the land use attribute fields in the resource status database, the land use attribute corresponding to each vector feature in the database, such as cultivated land, forest land, and wetland, is first determined. These land use attribute fields are the core basis for the conversion. The specified resolution of the raster image is then determined, set according to actual application requirements, to ensure that the converted raster image clearly reflects geographical distribution details. A vector-to-raster conversion process is then used, assigning the land use attribute of each vector feature to all raster cells within its coverage area, ensuring that each cell carries the corresponding land use attribute information while maintaining the cell size within the specified resolution requirements. This ultimately results in a raster image with the specified resolution where each cell contains the land use attribute.

[0092] Furthermore, based on the spectral characteristics of pixels in the raster image, when automatically setting seed points in uniform regions, the spectral characteristics of each pixel in the raster image are first analyzed, including pixel brightness, color, and other information. These characteristics reflect the differences in land use attributes corresponding to the pixels. Uniform regions in the raster image are identified, meaning that the spectral characteristics of all pixels within this region are minimally different, and their land use attributes are consistent. For example, a continuous area of ​​cultivated land has highly similar pixel spectral characteristics and is considered a uniform region. At the center of each identified uniform region or at a relatively evenly distributed location, one or more pixels are automatically selected as seed points to ensure that the seed points represent the spectral characteristics and land use attributes of the uniform region, thus completing the seed point setting.

[0093] Furthermore, using the seed point as the core, region growth is performed based on the feature similarity between adjacent pixels. When merging pixels that meet the similarity condition, each seed point is used as the starting point, and its surrounding adjacent pixels are checked one by one. The spectral features of adjacent pixels and the seed point are compared. If the difference in spectral features between the two is within a preset similarity range and the land use attributes are consistent, then the adjacent pixel is determined to meet the similarity condition and is included in the growth region where the seed point is located. The process of checking adjacent pixels continues to expand outward, and pixels that meet the similarity condition are continuously merged into the growth region until there are no more pixels that meet the similarity condition around the growth region, thus realizing region growth and pixel merging.

[0094] Furthermore, when region growth stops, the merged pixels are identified as the initial segmentation result. When obtaining the initial boundary, it is first confirmed that the region growth process corresponding to all seed points has stopped, meaning that no new pixels can be included in each growth region. All merged pixels within each growth region are identified as an independent segmentation unit, with each segmentation unit representing a region with consistent land use attributes. The edge contours of each segmentation unit are delineated using software tools or manual marking; these edge contours are the initial boundaries. All segmentation units and the initial boundaries together constitute the initial segmentation result.

[0095] Furthermore, when performing topological checks on the initial boundaries to obtain land parcels, the existence of boundary gaps or overlaps is first checked. Gaps are filled by assigning pixels to adjacent units based on their land use attributes and pixel characteristics, and boundary connections are adjusted accordingly. Overlapping areas are assigned based on pixel land use and spectral characteristics, and redundant boundaries are removed. After correction, land parcels with complete boundaries and no topological errors are obtained.

[0096] In summary, converting vector data into raster images of a specified resolution based on land use attributes can make abstract elements more intuitive, clearly present land use details, realize the correspondence between land use attributes and pixels, and provide an accurate image basis for patch segmentation.

[0097] In summary, setting seed points in uniform regions based on pixel spectral characteristics can accurately identify areas with consistent land use types, ensuring that seed points match actual land use types. This provides a reliable benchmark for region growth and guarantees that the segmented regions correspond to the land use types. Growing and merging pixels based on feature similarity with seed points as the core allows pixels of the same type to automatically aggregate, forming continuous regions that match actual land use types. This reduces subjective errors and redundant data, and clearly presents land use boundaries.

[0098] In summary, identifying the initial segmentation results yields initial boundaries, enabling rapid delineation of land use regions. This provides a framework for boundary optimization, visually reflecting the regional outline and avoiding the inefficiency of direct fine-tuning. Performing topological checks and correcting issues on the initial boundaries eliminates boundary connection and conflict problems, ensuring complete and error-free land parcel boundaries that conform to data standards. This provides an accurate spatial representation for subsequent work and guarantees the accuracy of green space labeling.

[0099] S3. Using site factors as growth constraints for the land parcels, the initial land parcel data of the national land space is obtained;

[0100] In this embodiment of the invention, the step of using site factors as growth constraints for the land parcels to obtain the initial land parcel data of the national territory includes:

[0101] Obtain site factor data including soil type, slope, and aspect;

[0102] The attribute information in the site factor data is assigned to the corresponding land patch through attribute connection;

[0103] Based on the preset greening suitability rules, land parcels with site factor attributes are screened.

[0104] The land parcels that meet the greening suitability rules will be output as the initial land parcel data of the national land space.

[0105] Specifically, when acquiring site factor data including soil type, slope, and aspect, first contact the soil survey department to obtain a soil type distribution map and a detailed survey report for the target area. The report should clearly identify the soil types in different areas, such as loam, clay, and sand, as well as the fertility, water retention, and other characteristics of each soil type. Then, conduct on-site measurements of the target area using topographic surveying equipment, recording the slope of each area, i.e., the angle of ground inclination, and determining the aspect of each area, i.e., the direction the slope faces, such as east, south, west, or north. The acquired soil type information, slope data, and aspect data are then compiled and summarized to form a complete set of site factor data, ensuring that the data covers the entire national land space and corresponds to the spatial range of land parcels.

[0106] Furthermore, when assigning site factor attributes through attribute connection, the shared spatial coordinates of the land parcels and site factor data are used as the basis for association. The software loads two types of data, selects the attribute connection function, and sets the coordinates as the connection condition. The software then automatically matches the corresponding site factors and assigns attributes such as soil type, slope, and aspect to the parcels, completing the attribute assignment.

[0107] Furthermore, when screening map patches based on preset greening suitability rules, the rules formulated according to plant growth needs are first clarified, such as suitable soil being loam or sandy loam, gentle to moderate slope, and south- or east-facing slope. Each map patch is checked to see if its soil type, slope, and slope aspect meet the rules. If all three are met, it is marked as meeting the conditions; if any one is not met, it is marked as not meeting the conditions, thus completing the screening.

[0108] Furthermore, when exporting land parcels that meet the greening suitability rules as initial land parcel data for national land space, the data processing software first filters out all land parcels marked as meeting the criteria. It then checks the completeness of the attribute information of these land parcels, ensuring that each compliant land parcel clearly carries site factor attributes such as soil type, slope, and aspect, as well as its original land category attributes. Using the software's output function, these compliant land parcels are exported in a preset data format. During the export process, the spatial relationships and attribute information of the land parcels remain unchanged. The exported land parcel data constitutes the initial land parcel data for national land space.

[0109] In summary, acquiring site factor data including soil type, slope, and aspect can collect key natural condition information that affects the growth of green plants, providing a core basis for judging the suitability of greening and avoiding one-sided judgments.

[0110] In summary, by assigning site factor attributes to corresponding land parcels through attribute connections, the spatial location of the parcels and natural conditions are established, solving the problem that parcels only contain land type information and providing attribute support for subsequent screening.

[0111] In summary, selecting land parcels based on greening suitability rules can eliminate unsuitable areas according to plant growth needs, ensuring that the initial land parcel data meets greening requirements from a natural condition perspective and reducing ineffective planning.

[0112] In summary, outputting map patches that meet the rules as initial plot data forms a set of lands naturally suitable for greening, laying the foundation for subsequent vectorization processing and improving the accuracy of the final greening labeling results.

[0113] S4. The initial land parcel data is vectorized to obtain the green land patches of the national land space;

[0114] In this embodiment of the invention, the step of vectorizing the initial land parcel data to obtain the green land patches of the national land space includes:

[0115] Perform a boundary extraction operation on the initial land parcel data to generate the boundary lines of the initial land parcel data;

[0116] The boundary lines are topologically constructed to form closed polygonal surface features;

[0117] Redundant vertices are eliminated from the polygonal surface features to generate simplified vector boundaries;

[0118] The simplified vector boundary is associated with the attribute information of the initial land parcel data to establish a vector plot with a complete attribute structure;

[0119] Perform geometric consistency verification on the vector map features, and output the vector map features that have successfully passed the consistency verification as land greening land features.

[0120] Specifically, when performing boundary extraction on the initial land parcel data to generate the boundary lines, the initial land parcel data is first loaded. This data contains the spatial extent and attribute information of multiple land parcels. Using the boundary extraction function in the data processing software, the boundary positions between each land parcel and its surrounding areas are identified, and continuous lines are drawn along these boundaries. These lines represent the boundaries of individual land parcels. The boundary lines of all land parcels are extracted one by one, ensuring that the boundary lines of each land parcel completely and accurately reflect its outline shape, ultimately forming a set of boundary lines for the initial land parcel data.

[0121] Furthermore, when constructing the boundary line topology to form a closed polygon, first check and connect the boundary line breakpoints to ensure continuity, then use the software to start the topology construction function and set rules to ensure that adjacent boundaries are accurately connected. Connect the end of the plot boundaries according to the rules to form a closed loop, and the enclosed area is the closed polygon surface feature.

[0122] Furthermore, when eliminating redundant vertices for polygonal features, the vertex distribution is first analyzed to identify redundant vertices that are too close together or whose connections are approximately straight lines. The software's vertex simplification function is then used to delete redundant vertices according to rules, ensuring that the boundary contour matches the actual area, ultimately generating a simplified vector boundary with fewer vertices and simpler lines.

[0123] Furthermore, when associating simplified vector boundaries with initial plot attributes, plot data containing site factors and land use attributes are first organized. Both types of data are loaded into the software, and based on plot identifiers, attributes are matched one by one to the simplified vector boundaries, ensuring that the boundaries carry complete attributes, thus forming vector plots containing both spatial boundaries and attributes.

[0124] Furthermore, when verifying the geometric consistency of vector map features, standards such as closed boundaries, no self-intersections, and reasonable areas are first set. Software verification functions are used to check and correct non-compliant features, and those that meet the standards are selected and exported; these are the land greening map features.

[0125] In summary, extracting boundary lines from the initial land parcel data can accurately delineate the outline of each parcel, clarify the spatial boundaries of the parcels, provide a clear line foundation for the subsequent construction of areal elements, and avoid the problem of unclear parcel boundaries due to blurred boundaries.

[0126] In summary, constructing topological relationships on boundary lines to form closed polygonal surface elements ensures that the boundaries of each plot are completely connected without any breaks, transforming plots from linear forms into surface areas with actual spatial significance. This meets the standardized requirements of land spatial data for plot morphology and provides a complete spatial carrier for subsequent attribute association.

[0127] In summary, eliminating redundant vertices of polygonal features to generate simplified vector boundaries can reduce the amount of data while preserving the core outline of the land parcel, thereby reducing the complexity of subsequent data processing. At the same time, it avoids boundary irregularities caused by redundant vertices and improves the simplicity and accuracy of vector boundaries.

[0128] In summary, simplifying the association between vector boundaries and initial plot data attribute information allows vector plots to simultaneously possess key information such as spatial location, site factors, and land use attributes, solving the problem of separation between spatial data and attribute data, and providing complete data support for subsequent greening suitability analysis and control boundary selection.

[0129] In summary, verifying the geometric consistency of vector plots and outputting qualified plots can eliminate plots with geometric errors such as non-closed boundaries and self-intersections, ensuring that the final land greening plots meet the standards in terms of geometric shape and data integrity. This lays a high-quality data foundation for subsequent overlay analysis with the control boundary database and generation of initial greening plots, further guaranteeing the accuracy of land greening spatial labeling.

[0130] S5. Construct a control boundary database for the aforementioned national land space based on the spatial vector ranges of cultivated land, construction land, nature reserves, and mining rights.

[0131] In this embodiment of the invention, the step of constructing the control boundary database of the national land space based on the spatial vector ranges of cultivated land, construction land, nature reserves, and mining rights includes:

[0132] Obtain the original vector datasets of the spatial vector ranges of cultivated land, construction land, nature reserves, and mining rights;

[0133] The original vector dataset is subjected to coordinate system and data format standardization processing to obtain a standardized vector data set;

[0134] Identify spatial overlaps and boundary conflicts between different vector ranges in the standardized vector data set to obtain the control boundary data of the national territory;

[0135] The control boundary data is subjected to attribute structuring processing to obtain the attribute table structure of the national land space;

[0136] Based on the attribute table structure and the control boundary data, a control boundary database for the national territory is constructed.

[0137] Specifically, when obtaining the original vector dataset, we connect with the departments responsible for farmland surveys, urban and rural planning, nature reserve management, and mining rights approval to obtain vector data containing farmland boundary ownership, construction land use, nature reserve boundary level, and mining rights scope and mining type in sequence. After integration, we form the original dataset with four types of vector ranges.

[0138] Furthermore, when processing the original vector dataset, a unified coordinate system conforming to the standards of land and space planning is first determined. Data processing software is then used to transform various data coordinates to ensure that the spatial positioning accuracy is without deviation. Next, commonly used standard data formats are selected, and batch format conversions are performed to correct issues such as missing attributes and structural anomalies, ultimately forming a vector data set with unified coordinates and standard format.

[0139] Furthermore, when identifying spatial overlaps and boundary conflicts, standardized vector data is loaded into the data processing software and spatial analysis functions are enabled. First, the overlap and boundary connection between cultivated land and construction land are analyzed. Then, the overlapping areas and boundary conflicts between cultivated land, construction land, nature reserves, and mining rights are investigated in turn. The corresponding vector range types are marked and recorded, and the data are integrated to form the national land spatial control boundary data.

[0140] Furthermore, when performing attribute structuring on control boundary data, the core information is first identified, including boundary type, location description, names of involved vector ranges, and boundary length. Then, corresponding attribute fields are designed based on this information, clarifying the data type and description specifications for each field. Finally, the fields are logically sorted to form a clearly structured land space attribute table.

[0141] Furthermore, when constructing the control boundary database, first create a new database with the same name in the database management system, create attribute tables according to the attribute table structure, and ensure that the fields and data types are consistent. Import the spatial geometry information of the control boundaries and associate it with the attribute tables to achieve integrated storage of spatial and attribute data. Finally, check the data integrity and correlation to ensure accurate data matching and complete the database construction.

[0142] In summary, obtaining the original vector datasets of the four types of vector ranges can comprehensively collect basic data on the spatial distribution of cultivated land, construction land, nature reserves, and mining rights in the national land space, covering key areas that conflict with land use attributes for greening. This provides a complete data source of conflict areas for subsequent construction of control boundaries and screening of suitable greening plots, avoiding conflicts between greening labels and other land use attributes due to the omission of key area data.

[0143] In summary, standardizing the coordinate system and format of the original vector dataset can eliminate spatial reference differences and format incompatibility issues between data from different sources, ensure that all vector data are aligned within the same spatial framework, avoid inaccurate identification of subsequent spatial overlap due to coordinate or format deviations, and provide a unified data foundation for accurate analysis of spatial relationships between different vector ranges.

[0144] In summary, identifying spatial overlaps and boundary conflicts of different vector ranges in standardized data to obtain control boundary data can clearly identify areas in the national land space where there are conflicts in land use attributes. These areas are the key areas to be avoided in national land greening, providing a clear control basis for subsequent screening of land parcels for national greening and reducing conflicts between greening planning and other land use functions.

[0145] In summary, attribute structuring of control boundary data yields an attribute table structure, which can standardize and store key information such as the type, scope, and location description of control boundaries. This ensures that control boundary data not only contains spatial geometric information but also has clear attribute descriptions, facilitating rapid identification of conflict types when overlaid with land greening patches and improving screening efficiency.

[0146] In summary, constructing a control boundary database based on attribute table structure and control boundary data enables integrated storage and management of control boundary spatial data and attribute data, forming a structured and callable database resource. When overlaying and filtering this database with land greening patches, conflict areas can be efficiently extracted and overlapping patches can be removed, ensuring the compliance of the initial greening patches from the land use attribute level and further improving the accuracy of land greening spatial labeling.

[0147] S6. Overlay the control boundary database onto the land greening land patch, and simultaneously remove overlapping patches in the overlaid image to obtain the initial greening patch of the land space;

[0148] In this embodiment of the invention, the steps of overlaying the control boundary database onto the land greening land patch and simultaneously removing overlapping patches in the overlaid image to obtain the initial greening patch of the land space include:

[0149] Spatial overlay analysis is performed between the control boundary vector data in the control boundary database and the land greening land parcels to obtain the overlay result of the land space;

[0150] Based on the control attribute information in the overlay results, identify the land greening land parcels that spatially overlap with the cultivated land spatial vector range, the construction land vector range, the nature reserve vector range, and the mining rights vector range;

[0151] The identified land greening patches with spatial overlap were erased to obtain intermediate data with the overlapping areas removed.

[0152] Perform an integrity check on the remaining patches in the intermediate data and remove broken patches with too small an area caused by the erasure operation;

[0153] The integrity-checked patches are output as the initial greening patches for the land space.

[0154] Specifically, when overlaying control boundary vector data with land greening patches, first load both types of data into the software and ensure that the coordinates are consistent. Then, enable the spatial overlay analysis function and select the "intersection" mode to allow the data to be completely overlaid. The software will automatically record the spatial relationship and retain the original attributes, ultimately forming a land space overlay result containing the overlay relationship and complete attributes.

[0155] Furthermore, to ensure consistency of coordinates between the two types of data in the data processing software, first check their respective coordinate systems. If they differ, unify them to the same system using the software's coordinate conversion function, ensuring positional accuracy during conversion. When identifying overlapping polygons, examine the control attribute information of the overlay results, which indicates the corresponding vector range type. Check the attributes associated with each polygon one by one. If it contains types such as cultivated land or construction land, mark these polygons to complete the identification.

[0156] Furthermore, when erasing overlapping patches, the software selects the marked overlapping patches, loads the corresponding overlap vector range boundary, and enables the erasure function to delete the overlapping parts of the patches at this boundary, retaining the non-overlapping areas. Finally, the erased patches are integrated with the unmarked patches to form intermediate data.

[0157] Furthermore, when checking the integrity of the remaining patches in the intermediate data, first determine the criteria for removing fragmented patches with excessively small areas. View the area of ​​each patch in the software, compare it with the minimum greening area standard, mark and delete fragmented patches smaller than the standard, and retain complete patches that meet the standard, forming a set of checked patches.

[0158] Furthermore, when outputting the initial greening patches, first check the patch attributes to ensure that site factors, land use attributes, and spatial location are preserved. In the software, select the output function, set it to the commonly used standard vector format, start the export, and keep the spatial topology and attribute structure unchanged. The final exported data is the initial greening patch.

[0159] In summary, overlaying control boundary vector data with land greening patches clearly reveals their spatial relationship and identifies overlaps, providing a basis for selecting compliant land parcels. Based on the control attribute information in the overlay results, greening patches overlapping with the control area can be accurately identified, ensuring no conflicting patches are missed.

[0160] In summary, erasing overlapping areas yields intermediate data, eliminating conflicting parts, preserving compliant areas, and avoiding conflicts between green space planning and other land uses. Checking the intermediate data and removing broken patches ensures the integrity and usability of the remaining patches, providing high-quality data for subsequent processing.

[0161] In summary, the initial greening patches are output, forming candidate plots that combine natural suitability and land use compliance, laying the foundation for generating the final greening labeling results.

[0162] S7. The initial greening patches are subjected to erosion and expansion processing, and the structural element size of the initial greening patches after erosion and expansion processing is adaptively adjusted to obtain vector result data of greening annotation in the land space.

[0163] In this embodiment of the invention, the steps of performing erosion and expansion processing on the initial greening patches, and adaptively adjusting the structural element size of the eroded and expanded initial greening patches to obtain vector result data of greening annotations in the national land space include:

[0164] Remove the small protrusions and isolated pixel areas at the boundary of the initial greening patch to obtain the intermediate patch after erosion.

[0165] After corrosion treatment, the pores inside the intermediate patch and the adjacent broken areas are filled to obtain the optimized patch;

[0166] Based on the geometric features of the optimized patch, the size of the structural element used for morphological processing is adaptively adjusted;

[0167] The optimized patch was subjected to secondary morphological filtering using the adjusted structural element size to obtain vector data of greening annotations in the land space.

[0168] Specifically, when removing small protrusions and isolated pixel areas at the boundaries of the initial greening patches to obtain the intermediate patches after erosion processing, the initial greening patch data is first loaded, and the boundary area of ​​each patch is magnified and viewed using an image observation tool to identify small protrusions with small areas on the boundary, as well as isolated pixel areas that are not connected to the main patch and exist alone.

[0169] Furthermore, using a morphological erosion tool, the processing direction is set to shrink inward along the boundary of the patch. The tool is applied to the boundary of the initial greening patch, eliminating the identified small protrusions one by one, while removing isolated pixel areas from the patch data. During the processing, boundary changes are continuously checked to ensure that only small protrusions and isolated pixels are removed, without changing the main outline and area of ​​the patch. The final result is an intermediate patch with smooth boundaries and no extra protrusions or isolated pixels after erosion processing.

[0170] Furthermore, when filling the holes inside the intermediate patch after corrosion treatment and connecting adjacent broken areas to obtain the optimized patch, the intermediate patch after corrosion treatment is first traversed. The internal region detection function is used to identify the unfilled holes that form closed blanks inside the patch. At the same time, adjacent broken areas that are separated from the main patch but are close to it due to the previous processing are marked.

[0171] Furthermore, a morphological dilation tool is used to set filling and connection rules from the inside of the patch towards the holes and from the main patch towards the broken areas. The hole areas are filled completely with attribute information consistent with the main patch, while adjacent broken areas are connected and merged with the main patch, making the originally separate broken areas part of the main patch. After processing, it is checked whether there are still unfilled holes inside the patch and whether adjacent broken areas are completely connected, ensuring the patch's internal integrity and overall continuity, resulting in an optimized patch with a complete structure.

[0172] Furthermore, when adaptively adjusting the size of structural elements used for morphological processing based on the geometric features of the optimized patch, the geometric features of the optimized patch are first analyzed, including the overall area size of the patch, the curvature of the boundary, and the compactness of the internal region. If the optimized patch has a large area, gently curving boundaries, and a compact interior, it indicates that the overall shape of the patch is regular, and a larger structural element needs to be selected to match the size of the patch; if the optimized patch has a small area, many curved boundaries, or complex internal details, a smaller structural element needs to be selected to preserve the detailed features of the patch.

[0173] Furthermore, based on these geometric feature analysis results, the size parameters of the structural elements are automatically adjusted to match the size of the structural elements with the morphological features of the optimized patch, ensuring that subsequent morphological processing can achieve a smooth optimization effect without destroying the key geometric information of the patch, thus forming a structural element size that is adapted to the current optimized patch.

[0174] Furthermore, when performing secondary morphological filtering on the optimized patches using the adjusted structural element dimensions to obtain vector data of greening annotations in the land space, the adjusted structural element dimensions are first imported into the morphological filtering tool, and the filtering direction is set to a two-way processing mode that balances patch boundary smoothing and internal detail preservation. The optimized patches are then input into the filtering tool, which, based on the adjusted structural element dimensions, further smooths the patch boundaries to eliminate any remaining minor irregularities. Simultaneously, it adjusts the uniformity of the patch's internal area to ensure consistent distribution of patch attribute information.

[0175] Furthermore, after the filtering process is completed, the geometric accuracy and attribute integrity of the patches are checked to confirm that the patches have clear boundaries, are internally complete, and have no morphological defects. Then, the processed patch data is converted into a standard vector format, retaining key information such as the greening attributes and spatial location of the patches, and finally forming vector result data of greening annotation in the national land space.

[0176] In summary, removing small bumps and isolated pixels from the initial green patch boundaries can make the boundaries of intermediate patches smoother and avoid unnecessary details interfering with annotation.

[0177] In summary, filling holes and connecting broken areas can repair the structure of landform patches, ensuring their complete spatial form and meeting actual greening needs. Adaptive adjustment of structural element sizes can fit the geometric characteristics of landform patches, avoiding over- or under-processing. Secondary morphological filtering further optimizes the landform patches, and the final vector data conforms to annotation standards, improving accuracy.

[0178] Based on the same inventive concept, embodiments of this application provide a land greening spatial labeling system, including:

[0179] The initial resource database construction module is used to construct a resource status database of the national land space based on the latest land change survey results and forestry, grassland and wetland survey and monitoring results;

[0180] The raster image segmentation module is used to convert the resource status database into a raster image, and to segment the raster image into land patches by setting seed points and merging similar pixel regions;

[0181] The initial land parcel data generation module is used to take the site factors as growth constraints for the land parcels to obtain the initial land parcel data of the national land space;

[0182] The image vectorization processing module is used to vectorize the initial land parcel data to obtain the green land patches of the national land space;

[0183] The boundary line generation module is used to construct a control boundary database of the national land space based on the spatial vector ranges of cultivated land, construction land, nature reserves, and mining rights.

[0184] The initial greening patch generation module is used to overlay the control boundary database onto the land greening patch, and simultaneously remove overlapping patches in the overlaid image to obtain the initial greening patch of the land space;

[0185] The vector result data acquisition module is used to perform erosion and expansion processing on the initial greening patches, and to adaptively adjust the structural element size of the initial greening patches after erosion and expansion processing, so as to obtain vector result data of greening annotation in the land space.

[0186] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0187] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as a method for marking up land green spaces.

[0188] Computer storage media include, for example, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media that can store program code.

[0189] Based on the same inventive concept, this application provides a smart terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded and executed by the processor to a method for marking land greening space.

[0190] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0191] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A land greening space marking method, characterized in that, include: S1. Based on the latest land change survey results and forestry, grassland and wetland survey and monitoring results, construct a resource status database for national land space; S2. Convert the resource status database into a raster image, set seed points, and merge similar pixel regions to segment the raster image into land patches; S3. Using site factors as growth constraints for the land parcels, initial land parcel data for the national land space is obtained, including: Obtain site factor data including soil type, slope, and aspect; The attribute information in the site factor data is assigned to the corresponding land patch through attribute connection; Based on the preset greening suitability rules, land parcels with site factor attributes are screened. The land parcels that meet the greening suitability rules will be output as the initial land parcel data of the national land space; S4. The initial land parcel data is vectorized to obtain land greening land patches in the national land space; S5. Construct a control boundary database for the aforementioned national land space based on the spatial vector ranges of cultivated land, construction land, nature reserves, and mining rights. S6. Overlay the control boundary database onto the land greening land patch, and simultaneously remove overlapping patches in the overlaid image to obtain the initial greening patch of the land space; S7. The initial greening patches are subjected to erosion and expansion processing, and the structural element sizes of the eroded and expanded initial greening patches are adaptively adjusted to obtain vector result data of greening annotations in the national land space, including: Remove the small protrusions and isolated pixel areas at the boundary of the initial greening patch to obtain the intermediate patch after erosion. After corrosion treatment, the pores inside the intermediate patch and the adjacent broken areas are filled to obtain the optimized patch; Based on the geometric features of the optimized patch, the size of the structural element used for morphological processing is adaptively adjusted; The optimized patch was subjected to secondary morphological filtering using the adjusted structural element size to obtain vector data of greening annotations in the land space.

2. The method of claim 1, wherein, The steps of converting the resource status database into a raster image, setting seed points, and merging similar pixel regions to segment the raster image into land patches include: Based on the land category attribute field in the resource status database, the vector format of the resource status database is converted into a raster image with a specified resolution; Seed points are automatically set in a uniform region based on the spectral characteristics of the pixels in the raster image. Using the seed point as the core, region growth is performed based on the feature similarity between adjacent pixels, while pixels that meet the similarity condition are merged. When the growth of the region stops, the merged pixels are identified as the initial segmentation result, and the initial boundary is obtained; A topological check is performed on the initial boundary to correct boundary areas with gaps or overlaps, thus obtaining the land patch.

3. The method of claim 1, wherein, The steps of vectorizing the initial land parcel data to obtain the green land patches of the national land space include: Perform a boundary extraction operation on the initial land parcel data to generate the boundary lines of the initial land parcel data; The boundary lines are topologically constructed to form closed polygonal surface features; Redundant vertices are eliminated from the polygonal surface features to generate simplified vector boundaries; The simplified vector boundary is associated with the attribute information of the initial land parcel data to establish a vector plot with a complete attribute structure; Perform geometric consistency verification on the vector map features, and output the vector map features that have successfully passed the consistency verification as land greening land features.

4. The method for marking land greening space according to claim 1, characterized in that, The steps for constructing the control boundary database of the national land space based on the spatial vector ranges of cultivated land, construction land, nature reserves, and mining rights include: Obtain the original vector datasets of the spatial vector ranges of cultivated land, construction land, nature reserves, and mining rights; The original vector dataset is subjected to coordinate system and data format standardization processing to obtain a standardized vector data set; Identify spatial overlaps and boundary conflicts between different vector ranges in the standardized vector data set to obtain the control boundary data of the national territory; The control boundary data is subjected to attribute structuring processing to obtain the attribute table structure of the national land space; Based on the attribute table structure and the control boundary data, a control boundary database for the national territory is constructed.

5. The method for marking land greening space according to claim 1, characterized in that, The steps of overlaying the control boundary database onto the land greening land parcels and simultaneously removing overlapping parcels from the overlaid image to obtain the initial greening parcels of the land space include: Spatial overlay analysis is performed between the control boundary vector data in the control boundary database and the land greening land parcels to obtain the overlay result of the land space; Based on the control attribute information in the overlay results, identify the land greening land parcels that spatially overlap with the cultivated land spatial vector range, the construction land vector range, the nature reserve vector range, and the mining rights vector range; The identified land greening patches with spatial overlap were erased to obtain intermediate data with the overlapping areas removed. Perform an integrity check on the remaining patches in the intermediate data and remove broken patches with too small an area caused by the erasure operation; The integrity-checked patches are output as the initial greening patches for the land space.

6. A land greening spatial labeling system, characterized in that, include: The initial resource database construction module is used to construct a resource status database of the national land space based on the latest land change survey results and forestry, grassland and wetland survey and monitoring results; The raster image segmentation module is used to convert the resource status database into a raster image, and to segment the raster image into land patches by setting seed points and merging similar pixel regions; The initial land parcel data generation module is used to take site factors as growth constraints for the land patches to obtain the initial land parcel data of the national land space, specifically for: Obtain site factor data including soil type, slope, and aspect; The attribute information in the site factor data is assigned to the corresponding land patch through attribute connection; Based on the preset greening suitability rules, land parcels with site factor attributes are screened. The land parcels that meet the greening suitability rules will be output as the initial land parcel data of the national land space; The image vectorization processing module is used to vectorize the initial land parcel data to obtain the green land patches of the national land space; The boundary line generation module is used to construct a control boundary database of the national land space based on the spatial vector ranges of cultivated land, construction land, nature reserves, and mining rights. The initial greening patch generation module is used to overlay the control boundary database onto the land greening patch, and simultaneously remove overlapping patches in the overlaid image to obtain the initial greening patch of the land space; The vector result data acquisition module is used to perform erosion and expansion processing on the initial greening patches, and to adaptively adjust the structural element size of the eroded and expanded initial greening patches to obtain vector result data of greening annotations in the land space. Specifically, it is used for: Remove the small protrusions and isolated pixel areas at the boundary of the initial greening patch to obtain the intermediate patch after erosion. After corrosion treatment, the pores inside the intermediate patch and the adjacent broken areas are filled to obtain the optimized patch; Based on the geometric features of the optimized patch, the size of the structural element used for morphological processing is adaptively adjusted; The optimized patch was subjected to secondary morphological filtering using the adjusted structural element size to obtain vector data of greening annotations in the land space.

7. A smart terminal, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any one of claims 1 to 5, a method for marking land greening spaces.

8. A computer-readable storage medium, characterized in that, The computer program is stored that can be loaded by a processor and execute the method as described in any one of claims 1 to 5.

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