Index file generation, land image information tracing and tile image generation method

Abstract

The application discloses an index file generation, land image information tracing and tile image generation method, relates to a mosaic line space-time index and dynamic time phase service, and the method is as follows: based on the matched association relationship between the obtained mosaic image and the mosaic line, an association dataset is formed; according to the image coverage range in the association dataset and a predefined space partition rule, a service area is divided into a plurality of geographical sub-areas; for each geographical sub-area, a matching adaptive image is matched from an image library to form a parent area and a corresponding relationship between the parent area and the adaptive image; based on the corresponding relationship and the association dataset, the parent area is divided to determine a geographical unit, corresponding index records are generated for each geographical unit, and each index record is summarized to generate the index file, so that the application solves the problem of space-time information decoupling in the traditional tile service and can improve the accuracy of index file generation.

Description

Technical Field

[0001] This invention relates to the field of spatial data processing, and in particular to methods for generating index files, tracing land image information, and generating tile images. Background Technology

[0002] Land imagery data is typically published in the form of Web Map Tile Services (WMTS). Existing technical solutions have significant shortcomings in spatial data organization and management. The WMTS standard relies on a spatial index (tile row and column numbers) built from a pre-tiled tile pyramid. This index can only achieve rapid spatial location and visualization, and is a shallow index geared towards rendering. The core problem is that this indexing mechanism fails to structurally associate the spatial location of tiles with the multi-dimensional attributes (such as temporal phase and mosaic lines) of the underlying image entities, resulting in a decoupling between the spatiotemporal metadata of the image and the tile spatial index.

[0003] Therefore, within the existing technological framework, when it is necessary to query the temporal phase, source, or associated mosaic lines of an image corresponding to a specific geographic location, the system cannot directly complete this through the efficient spatial index of the service layer. Users are forced to abandon the service and turn to the backend file system or database for experience-based matching based on filenames, or to rely on time-consuming spatial overlay analysis using professional GIS software. This backend retrieval method, based on manual or semi-manual means, is essentially due to the lack of a service-oriented unified spatiotemporal data index that integrates spatial location and multidimensional attributes. It not only suffers from low query efficiency, failing to meet real-time business needs, but also its accuracy heavily relies on human experience and data standardization, thus limiting the in-depth application value of massive amounts of land image data in scenarios such as land surveys and ecological monitoring. Summary of the Invention

[0004] This invention provides methods for generating index files, tracing land image information, and generating tile images, which can improve the accuracy of index file generation.

[0005] In a first aspect, the present invention provides a method for generating an index file, comprising: Based on the pairing and association relationships between the acquired mosaic images and mosaic lines, an associated dataset is formed; Based on the image coverage and predefined spatial partitioning rules in the associated dataset, the service area is divided into several geographic sub-regions; For each of the aforementioned geographic sub-regions, matching images are obtained from the image library to form a parent region and a correspondence between the parent region and the matching images; Based on the correspondence and the associated dataset, the parent region is divided to determine geographic units, corresponding index records are generated for each geographic unit, and the index records are summarized to generate the index file. The index records include spatial range fields, image association fields, and temporal metadata fields.

[0006] This invention establishes a pairing relationship between mosaic images and mosaic lines to form an initial associated dataset. This ensures the precise binding of image entities to their spatial stitching boundaries (mosaic lines) from the data source, providing accurate and reliable foundational relationship pairs for subsequent mapping. Geographic sub-regions are divided according to image coverage and predefined spatial zoning rules, deconstructing the macro-service area into regular or business-oriented management units, providing a clear and comprehensive spatial framework for defining geographic regions. For each sub-region, an appropriate image is matched and a correspondence between the parent region and the image is formed. Intelligent decision-making based on image attributes (such as temporal phase and resolution) explicitly assigns the optimal image data source to each spatial framework unit, establishing the core mapping between region and image. Based on this correspondence and associated dataset, the parent region is divided, geographic units are determined, and index records containing spatiotemporal metadata are generated. A secondary refined segmentation of the region is performed by introducing mosaic lines, ensuring that the indexed geographic units accurately reflect the actual effective coverage and stitching structure within the image, and solidifying key attributes such as temporal phase into the index. The entire method systematically integrates scattered related data, spatial range, multi-source images and business rules, and automatically generates a structured index file that can accurately describe the spatial location and fully carry the image identity and spatiotemporal attributes, thus fundamentally ensuring the accuracy and reliability of the mapping relationship.

[0007] Further, the step of dividing the parent region and determining geographical units based on the correspondence and the associated dataset includes: If the adapted image is associated with a mosaic line, then the parent region is spatially clipped and segmented using the mosaic line as the dividing boundary, and the resulting split sub-regions are determined as the geographic units, wherein each split sub-region corresponds to a continuous image segment in the adapted image. If the adapted image has no associated mosaic lines, then the parent region is determined as the geographic unit.

[0008] By introducing mosaic lines to perform secondary fine-grained segmentation of the region, the geographic units in the index can accurately reflect the actual effective coverage and mosaic structure within the image, and key attributes such as temporal phase are solidified in the index.

[0009] Furthermore, the association dataset formed based on the pairing and association relationships between the acquired mosaic images and mosaic lines includes: Based on the rule engine, the shared unique identifier between the mosaic image and the mosaic line vector file is extracted to establish the first mapping relationship; For mosaic images and mosaic lines that do not conform to the filename matching rules in the first mapping relationship, the overlap between the geometric range of the mosaic lines and the spatial boundary of the image is calculated by spatial topology analysis. When the overlap exceeds a preset threshold, a second mapping relationship is established. Based on the first mapping relationship and the second mapping relationship, an associated dataset is formed.

[0010] By establishing pairing relationships between mosaic images and mosaic lines, an initial associated dataset is formed. This ensures the precise binding of image entities to their spatial stitching boundaries (mosaic lines) from the data source, providing accurate and reliable basic relationship pairs for subsequent mapping.

[0011] Secondly, this invention provides a method for tracing the source of land imagery information, applied to online map tile services, including: Respond to the client's image tracing request for a specific geographical location on the service map interface, and determine the geographical coordinates to be queried based on the image tracing request; Based on the geographic coordinates, a spatial inclusion determination is performed in a pre-built index file to identify target geographic units containing the geographic coordinates, wherein the index file is obtained based on the index file generation method described in the first aspect of the present invention; Based on the image identifier associated with the target geographic unit, the corresponding land image information is obtained from the associated dataset associated with the index file, wherein the land image information includes target temporal information, target metadata, and target mosaic line information.

[0012] This invention, by responding to client click requests on the map interface and accurately determining the geographic coordinates to be queried, ensures the error-free conversion and transmission of user query intent and spatial location, laying a reliable data input foundation for subsequent accurate matching. Utilizing a pre-generated index file based on a precise pairing association method for spatial inclusion determination ensures that any geographic coordinate is mapped to the correct target geographic unit at the millisecond level, fundamentally avoiding positioning errors caused by manual comparison or fuzzy rules in traditional methods. Based on the image identifier associated with the target geographic unit, temporal, metadata, and mosaic line information are obtained from the associated, verified complete dataset, ensuring that the provided information has a unique source, complete content, and strict correspondence with spatial location, thereby fundamentally guaranteeing the accuracy of the tracing results.

[0013] Furthermore, the step of determining spatial inclusion in a pre-built index file based on the geographic coordinates to identify target geographic units containing the geographic coordinates includes: The spatial query interface of the index file is called, and the spatial inclusion determination algorithm and the spatial index structure pre-built in the index file are used to filter out the index records in which the geographic coordinates are contained in the geometric boundary of the region. The geographic unit corresponding to the index record is identified as the target geographic unit.

[0014] By using an index file pre-generated based on a precise pairing and association method for spatial inclusion determination, it is possible to ensure that any geographic coordinate is mapped to the correct target geographic unit in milliseconds and uniquely, fundamentally avoiding positioning errors caused by manual comparison or fuzzy rules in traditional methods.

[0015] Further, determining the geographic coordinates to be queried based on the image tracing request includes: The image tracing request is parsed to obtain the screen coordinates; Based on the service map interface parameters, the coordinate conversion interface is called to convert the screen coordinates into geographic coordinates consistent with the coordinate system of the index file.

[0016] By responding to the client's click request on the map interface and accurately determining the geographic coordinates to be queried, the error-free conversion and transmission of the user's query intent and spatial location are ensured, laying a reliable data input foundation for subsequent accurate matching.

[0017] Thirdly, the present invention provides a tile image generation method, applied to network map tile services, comprising: Receive a tile request initiated by the client, wherein the tile request includes the tile row and column numbers, scaling level, and service coordinate system; Calculate the geographic range corresponding to the tile based on the tile row and column number, the zoom level, and the service coordinate system; Based on the geographical range, spatial intersection matching is performed in a pre-built index file to filter out at least one target geographical unit that intersects with the geographical range, wherein the index file is obtained based on the index file generation method of the first aspect of the present invention; The corresponding image segments are retrieved according to the image identifiers associated with each target geographic unit, and the image segments are seamlessly stitched together in the tile coordinate system to generate an initial tile image. The target temporal information and target mosaic line information associated with each target geographic unit are written into the initial tile image to obtain the target tile image.

[0018] This invention, by receiving and parsing a standardized request containing tile row and column numbers, scaling levels, and coordinate systems, can directly and unambiguously determine the precise geographic extent of the tiles to be generated. This avoids the performance overhead caused by ambiguous extent calculations or multiple transformations required in traditional methods, laying an efficient foundation for subsequent precise positioning. It utilizes a pre-generated index file, independent of the tile grid, based on the method of this invention, for spatial intersection matching. This index structurally associates and spatially optimizes complex geographic regions, images, temporal phases, and mosaic lines. During matching, specific geographic units intersecting with the geographic extent of the tiles can be directly selected from the index through efficient spatial computation (such as R-tree indexing). This completely replaces the heavy preprocessing mode of "first pre-tiling all tiles, then manually or semi-automatically associating them," achieving on-demand and precise image positioning and significantly reducing unnecessary image data reading and processing. The system directly retrieves and seamlessly stitches together corresponding image fragments based on index records. Since the index ensures a precise correspondence between each geographic unit and image fragment, and the fragment boundaries are clearly defined, the data retrieval and stitching process is extremely efficient. This avoids the computational burden of real-time cropping and complex boundary fusion of the entire large image, a process common in traditional methods. Simultaneously with generating tile images, core metadata such as temporal phase and mosaic lines, obtained directly from the index, are written into the tiles, completing the synchronous encapsulation of data and spatiotemporal attributes. This completely eliminates the separate step of metadata association and binding required after tile generation in traditional processes.

[0019] Further, the step of performing spatial intersection matching in a pre-built index file based on the geographical range to filter out at least one target geographical unit that intersects with the geographical range includes: Spatial intersection matching is performed between the geographic range and the regional geometric boundaries in the index file to locate the index record that matches the geographic range; The geographic unit corresponding to the index record is identified as the target geographic unit.

[0020] This efficient spatial computing (such as R-tree indexing) filters out specific geographic units that intersect with the geographic range of the tiles from the index, replacing the heavy preprocessing mode of "first pre-tiling all the tiles, then manually or semi-automatically associating them". This achieves on-demand and accurate image positioning, and greatly reduces unnecessary image data reading and processing.

[0021] Further, the step of calculating the geographic range corresponding to a tile based on the tile row and column number, the scaling level, and the service coordinate system includes: calculating the geographic boundary coordinates corresponding to the tile based on the tile row and column number, the scaling level, and the service coordinate system using a tile grid calculation model, and generating the geographic range of the tile based on the geographic boundary coordinates.

[0022] Further, the step of seamlessly stitching the image segments in a tile coordinate system to generate an initial tile image includes: The image segments are spatially aligned to obtain several spatial alignment results in the tile coordinate system. The spatial alignment results are stitched together to obtain the initial tile image.

[0023] This method directly retrieves the corresponding image fragments from the index records and performs seamless stitching. Since the index ensures a precise correspondence between each geographic unit and the image fragment, and the fragment boundaries are clear, the data retrieval and stitching process is extremely efficient, avoiding the computational burden of real-time cropping and complex boundary fusion of the entire large image in traditional methods. Attached Figure Description

[0024] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1 This is a flowchart illustrating one embodiment of the index file generation method provided in this application; Figure 2 This is a flowchart illustrating one embodiment of steps S201 to S203 provided in this application; Figure 3 This is a flowchart illustrating one embodiment of the land image information tracing method provided in this application; Figure 4 This is a schematic flowchart of an embodiment of the tile image generation method provided in this application. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0028] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0029] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0030] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0031] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0032] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0033] Building an index file enables the system to quickly and accurately locate the corresponding image fragment and its complete spatiotemporal attributes based on geographic coordinates or ranges during service publishing and invocation. This provides unified and reliable data support for efficient image tracing at any location, dynamic tile generation, and viewpoint analysis. In existing technologies, the WMTS (World Land Imagery Service) itself does not build an independent, service-layer-available spatiotemporal index, failing to meet the needs of accurate and real-time land monitoring and decision-making.

[0034] See Figure 1 To improve the accuracy of index file generation, an embodiment of the present invention provides a method for tracing land image information, including steps S101 to S103: Step S101: Based on the pairing and association relationships between the acquired mosaic images and mosaic lines, an associated dataset is formed; Please refer to Figure 2 In some embodiments, step S101 includes steps S201 to S203: Step S201: Based on the rule engine, extract the shared unique identifier between the mosaic image and the mosaic line vector file, and establish the first mapping relationship; In some embodiments, a rule engine module is designed to automatically extract shared unique identifiers (such as project number, shooting date, region code, etc.) contained in the filenames of mosaic image files and mosaic line vector files, and establish a first mapping relationship based on these identifiers. For example, when a mosaic image is named "DOM_20240515_A.tif" (where "20240515" is the shooting date and "A" is the project number), the rule engine can automatically match mosaic line files named "MosaicLine_A.shp" (containing the same project number "A") or "MosaicLine_20240515_A.shp" (containing both the same shooting date and project number). Simultaneously, users can customize identifier extraction rules (such as specifying delimiters in filenames and the position of key fields) according to their own data production specifications, thereby adapting to diverse file naming formats and achieving rapid and broad initial association.

[0035] It should be noted that the rules engine module supports both predefined and user-defined filename matching rules, enabling rapid association between mosaic images and mosaic lines.

[0036] Step S202: For mosaic images and mosaic lines that do not conform to the file name matching rules in the first mapping relationship, calculate the overlap between the geometric range of the mosaic lines and the spatial boundary of the image through spatial topology analysis. When the overlap exceeds a preset threshold, establish a second mapping relationship. In some embodiments, for the remaining data that cannot be matched according to filename rules (such as cases of non-standard file naming or missing metadata), a precise association algorithm based on spatial range matching (i.e., the second mapping relationship) is initiated. Specifically, the geographic data parsing module automatically reads the spatial boundary coordinates (such as the image's four boundaries) and the geometric range (such as the vertex coordinates of the vector polygons) of each mosaic image. Subsequently, a spatial topology matching algorithm is used to calculate the area overlap between the geometric range of the mosaic lines and the spatial boundary of the image. Then, when the calculated area overlap exceeds a preset threshold, the association is automatically determined to be successful. At this point, the two are integrated to determine the second mapping relationship.

[0037] It should be noted that the spatial topology matching algorithm can be, but is not limited to, boundary overlap calculation models, minimum bounding rectangle comparison algorithms, etc., and this application does not impose any restrictions.

[0038] It should be noted that the formula for calculating the area overlap is: overlapping area / total image area.

[0039] It should be noted that the threshold for high-resolution images is set to ≥95%, and the threshold for medium and low-resolution images is set to ≥90%. This threshold can be manually adjusted according to the accuracy requirements of the actual application, or it can be automatically adapted by the system according to the image resolution (e.g., better than 1 meter is high resolution, 1-10 meters is medium resolution, and greater than 10 meters is low resolution). This application does not impose any restrictions.

[0040] Step S203: Based on the first mapping relationship and the second mapping relationship, form an associated dataset.

[0041] In some embodiments, the first mapping relationship (rule-based) and the second mapping relationship (spatial analysis-based) obtained from the above automated process are merged to form a preliminary associated dataset.

[0042] It should be noted that, to address the challenges of image association in complex scenarios such as high overlap of multiple image ranges and data defects (e.g., image boundary damage), the system also provides a visual verification interface based on manual intervention as a fallback. This interface overlays the mosaic images to be paired with candidate mosaic lines in different layers, supporting functions such as layer transparency adjustment, scaling, translation, and spatial measurement (e.g., distance and area measurement). Simultaneously, a human decision-making interaction interface is provided, allowing users to confirm correct association relationships, manually delete erroneous association records, and add custom association mappings. The operation process is saved in real-time to the association log module, supporting the review and modification of association results. Furthermore, the interface simultaneously displays image metadata (e.g., shooting time, resolution) and mosaic line attribute information (e.g., generation time, project affiliation), providing data support for human decision-making and ensuring the reliability of association results in complex scenarios.

[0043] By establishing pairing relationships between mosaic images and mosaic lines, an initial associated dataset is formed. This ensures the precise binding of image entities to their spatial stitching boundaries (mosaic lines) from the data source, providing accurate and reliable basic relationship pairs for subsequent mapping.

[0044] Step S102: Based on the image coverage in the associated dataset and the predefined spatial partitioning rules, the service area is divided into several geographic sub-regions. In some embodiments, based on the overall coverage of all images in the associated dataset, combined with the spatial distribution of mosaic lines, the distribution of mosaic lines, and the needs of land management operations (e.g., management according to administrative boundaries, ecological protection red line areas, etc.), the service area to be published by the WMTS service is determined. Then, the system calls a predefined spatial partitioning algorithm to divide the service area published by the WMTS service into several independent, non-overlapping sub-geographic regions, which form the basic spatial unit for subsequently finding and matching the most suitable image for each region.

[0045] It should be noted that the spatial partitioning algorithm supports two modes: one is the regular grid partitioning mode, which divides the area according to a fixed geographic coordinate grid; the other is the irregular business partitioning mode, which directly uses the existing business vector boundary (such as the administrative division surface) as the partitioning basis.

[0046] Step S103: For each of the geographic sub-regions, match the adapted image from the image library to form a parent region and the correspondence between the parent region and the adapted image; In some embodiments, for each geographic sub-region, an image priority evaluation model is invoked to select the most suitable original image for that region from the image library. After evaluation by the model, the system determines the optimal image from the candidate images as the suitable image for that region. If the suitable image can completely cover the initial region, the initial region is directly determined as the parent region, and a binding relationship is established between the parent region and the suitable image. If the suitable image cannot completely cover the initial region (i.e., only covers a part of it), the system extracts the effective coverage boundary of the suitable image within the initial region and uses this boundary as a dividing line to split the initial region into two parts: one part is the area actually covered by the suitable image, which is determined as the parent region and bound to the suitable image; the remaining uncovered part becomes the new "remaining area to be adapted," and the above matching process needs to be repeated until all geospatial areas are successfully matched with suitable images. In this way, each parent region establishes a uniquely corresponding suitable image, forming a one-to-one correspondence between parent region and suitable image.

[0047] It should be noted that the image priority evaluation model is a multi-dimensional comprehensive decision-making model. Its evaluation criteria include: image resolution (following the principle of high resolution priority), shooting time (following the principle of recent image priority), data quality (following the principle of high quality level priority) and business adaptability (setting preferences according to different application scenarios, such as prioritizing images with clear vegetation coverage in ecological monitoring scenarios).

[0048] Step S104: Based on the correspondence and the associated dataset, the parent region is divided to determine geographic units, corresponding index records are generated for each geographic unit, and the index records are summarized to generate the index file. The index records include spatial range fields, image association fields, and temporal metadata fields.

[0049] In some embodiments, the step of dividing the parent region and determining geographic units based on the correspondence and the associated dataset includes: if the adapted image is associated with mosaic lines, then the parent region is spatially cropped and segmented using the mosaic lines as the dividing boundary, and the resulting sub-regions are determined as the geographic units, wherein each sub-region corresponds to a continuous image segment in the adapted image; if the adapted image is not associated with mosaic lines, then the parent region is determined as the geographic unit. Specifically, after forming the "parent region - adapted image" correspondence, it is determined whether the adapted image is associated with mosaic line data based on the "mosaic line - image" associated dataset. If a correlation is determined, a secondary splitting process is initiated: First, the system extracts the geometric data of all mosaic lines corresponding to the adapted image from the associated dataset, ensuring that the spatial coordinate system of the mosaic lines is consistent with the parent region. Second, it calls spatial clipping and segmentation algorithms, using these mosaic lines as precise segmentation boundaries, to perform irregular geometric splitting of the parent region. Through this operation, the parent region is divided into multiple independent sub-regions, where the geometric boundary of each sub-region completely coincides with one or more mosaic lines, and each sub-region spatially corresponds one-to-one with a continuous image segment in the adapted image (i.e., the physical part of the image segmented by the mosaic lines). Subsequently, the system synchronously records the association information for this splitting: assigning a unique MOSAIC_ID to each mosaic line participating in the splitting, assigning an independent AREA_ID to each sub-region and associating it with the PARENT_AREA_ID of its parent region, and assigning a unique SPLIT_IMAGE_FRAGMENT_ID to each image segment, thereby ensuring a precise one-to-one correspondence between the sub-region, the image segment, and the mosaic line. At this point, each sub-region is determined as an independent geographic unit. Conversely, if the system determines through the associated dataset that the adapted image is not associated with any mosaic line data, it skips the secondary splitting process and directly treats the parent region as a complete geographic unit, and the corresponding image fragment is the whole scene adapted image.

[0050] It should be noted that the spatial clipping and segmentation algorithm is a computational model that supports complex polygon operations.

[0051] By introducing mosaic lines to perform secondary fine-grained segmentation of the region, each geographic unit in the index file can clearly define its spatial extent, associated image content, and complete information on whether it has been segmented by mosaic lines.

[0052] In some embodiments, corresponding index records are generated for each geographic unit, and the index records are aggregated to generate the index file. Specifically, for each geographic unit, the system integrates all its associated information to generate a structured index record. This index record strictly follows the predefined vector index file field specifications and includes three types of fields: 1. Spatial extent fields, including AREA_GEOM (which accurately records the geometric boundary of the geographic unit using a polygon vector format), AREA_ID (a unique identifier code assigned to the geographic unit), and PARENT_AREA_ID (if the unit is split from a parent region, its parent region code is recorded; otherwise, it is empty); 2. Image association fields, including MATCH_IMAGE_ The system includes the following data types: ID (the ID of the original adapted image associated with the data), MOSAIC_ID (if the unit is generated by splitting a mosaic line, enter the unique ID of the corresponding mosaic line; otherwise, leave it empty), and SPLIT_IMAGE_FRAGMENT_ID (the unique ID of the image segment corresponding to the unit in a split-line scenario; its value is consistent with MATCH_IMAGE_ID in a non-split-line scenario); and temporal metadata fields, including IMAGE_TIME (the capture time of the original image), IMAGE_METADATA (stores core metadata such as resolution and sensor model in JSON format), and MOSAIC_SPLIT_METADATA (reuses the content of IMAGE_METADATA in a non-split-line scenario). The system iterates through all geographic units and generates a complete record containing the above fields for each unit. Finally, all independent index records are aggregated, organized and encapsulated according to a standard spatial vector data format (specifically, SHP format), thereby generating a complete index file.

[0053] This invention establishes a pairing relationship between mosaic images and mosaic lines to form an initial associated dataset. This ensures the precise binding of image entities to their spatial stitching boundaries (mosaic lines) from the data source, providing accurate and reliable foundational relationship pairs for subsequent mapping. Geographic sub-regions are divided according to image coverage and predefined spatial zoning rules, deconstructing the macro-service area into regular or business-oriented management units, providing a clear and comprehensive spatial framework for defining geographic regions. For each sub-region, an appropriate image is matched and a correspondence between the parent region and the image is formed. Intelligent decision-making based on image attributes (such as temporal phase and resolution) explicitly assigns the optimal image data source to each spatial framework unit, establishing the core mapping between region and image. Based on this correspondence and associated dataset, the parent region is divided, geographic units are determined, and index records containing spatiotemporal metadata are generated. A secondary refined segmentation of the region is performed by introducing mosaic lines, ensuring that the indexed geographic units accurately reflect the actual effective coverage and stitching structure within the image, and solidifying key attributes such as temporal phase into the index. The entire method systematically integrates scattered related data, spatial range, multi-source images and business rules, and automatically generates a structured index file that can accurately describe the spatial location and fully carry the image identity and spatiotemporal attributes, thus fundamentally ensuring the accuracy and reliability of the mapping relationship.

[0054] See Figure 3 To improve the accuracy of tracing land image information, an embodiment of the present invention provides a method for tracing land image information, applied to online map tile services, including steps S301 to S303: Step S301: Respond to the client's image tracing request for a specific geographical location on the service map interface, and determine the geographical coordinates to be queried based on the image tracing request; In some embodiments, the system responds to a client's image tracing request for a specific geographic location on the service map interface. Specifically, the user accesses the published WMTS service by operating a client (e.g., a WebGIS application or desktop GIS software) and clicks on any geographic location to be queried on its map display interface. This click action then triggers the client's image tracing query function and sends a structured image tracing query request to the server.

[0055] In some embodiments, determining the geographic coordinates to be queried based on the image tracing request includes: parsing the image tracing request to obtain screen coordinates; and, in conjunction with service map interface parameters, calling a coordinate conversion interface to convert the screen coordinates into geographic coordinates consistent with the coordinate system of the index file. Specifically, after obtaining the image tracing query request, the map rendering engine integrated in the client accurately extracts the coordinate values ​​of the user's click location in the screen pixel coordinate system (usually recorded as pixel coordinates X and Y). To convert these screen coordinates into coordinates with real geographic meaning for spatial querying, the system calls a built-in coordinate conversion interface. During conversion, this interface needs to combine several key parameters of the current map view, including the map's real-time zoom level, the geographic coordinates of the view's center point, and the spatial coordinate system parameters (such as CGCS2000 or WGS84) consistent with the WMTS service and the underlying index file. Through a map projection inverse calculation model, the screen pixel coordinates are converted into corresponding geographic coordinates (e.g., latitude and longitude or projected coordinates) in real time and dynamically.

[0056] It should be noted that, in order to ensure the accuracy of subsequent matching with high-precision imagery and spatial vector indexes, the technical solution requires that the coordinate data generated in this conversion process be retained to 6 decimal places.

[0057] By responding to the client's click request on the map interface and accurately determining the geographic coordinates to be queried, the error-free conversion and transmission of the user's query intent and spatial location are ensured, laying a reliable data input foundation for subsequent accurate matching.

[0058] Step S302: Based on the geographic coordinates, perform spatial inclusion determination in a pre-built index file to determine the target geographic unit containing the geographic coordinates, wherein the index file is obtained based on the index file generation method described in Embodiment 1 of the present invention; It should be noted that the generation of the index file has been described in detail in Embodiment 1, so it will not be repeated here.

[0059] In some embodiments, step S302 includes: calling the spatial query interface of the index file, using a spatial inclusion determination algorithm and a spatial index structure pre-built in the index file to filter out index records whose regional geometric boundaries contain the geographic coordinates; and determining the geographic unit corresponding to the index record as the target geographic unit. Specifically, after obtaining the geographic coordinate point to be queried, the spatial query interface of the independent index file bound to the WMTS service is immediately called. The core of this query process is to use the "point-polygon spatial inclusion determination" algorithm to quickly calculate the spatial position relationship between the converted geographic coordinate point and the polygon vector surface defined by the "regional geometric boundary (AREA_GEOM)" field of each record in the index file. At the same time, in order to greatly improve query efficiency, the system does not traverse all records, but uses an efficient spatial index structure (such as an R-tree index) pre-built during the index file generation stage to accelerate the filtering. Guided by this spatial index, the system can instantly locate the candidate region set that may contain the coordinate point, and then, through precise geometric calculation, filter out the index record whose AREA_GEOM polygon uniquely contains the geographic coordinate point from all candidate records. Because the vector index file is generated with the assumption that geographic units do not overlap and are fully covered, there will be one and only one matching record for any given coordinate point. The system then extracts information such as the associated AREA_ID from this matching index record, thus uniquely identifying the geographic unit represented by that record as the target geographic unit for this query.

[0060] By using an index file pre-generated based on a precise pairing and association method for spatial inclusion determination, it is possible to ensure that any geographic coordinate is mapped to the correct target geographic unit in milliseconds and uniquely, fundamentally avoiding positioning errors caused by manual comparison or fuzzy rules in traditional methods.

[0061] Step S303: Based on the image identifier associated with the target geographic unit, obtain the corresponding land image information from the associated dataset associated with the index file, wherein the land image information includes target temporal information, target metadata, and target mosaic line information.

[0062] In some embodiments, after matching and identifying the target geographic unit through a vector index file, the system directly extracts core association information from the matched index records, including the image ID (i.e., IMAGE_ID or SPLIT_IMAGE_FRAGMENT_ID) and the mosaic line ID (MOSAIC_ID). Based on this image ID, the system initiates a precise query request to the pre-built four-dimensional association dataset of "mosaic line-image-temporal-metadata" that is compatible with the entire technical system, in order to retrieve the complete details of the image.

[0063] It should be noted that the acquired land imagery information is a structured dataset, specifically including: target temporal information, i.e., the complete image capture time (accurate to YYYY-MM-DD HH:MM:SS format) and seasonal classification (e.g., "Spring 2024"); target metadata, including sensor model (e.g., "GF-2"), spatial resolution (e.g., "1 meter"), data production unit, and data quality level (e.g., "Level 1"); and target mosaic information, i.e., the complete geometric boundary data of the mosaic lines associated with this image (which can be used for overlay display on the map interface) and the original coverage of the image.

[0064] This invention, by responding to client click requests on the map interface and accurately determining the geographic coordinates to be queried, ensures the error-free conversion and transmission of user query intent and spatial location, laying a reliable data input foundation for subsequent accurate matching. Utilizing a pre-generated index file based on a precise pairing association method for spatial inclusion determination ensures that any geographic coordinate is mapped to the correct target geographic unit at the millisecond level, fundamentally avoiding positioning errors caused by manual comparison or fuzzy rules in traditional methods. Based on the image identifier associated with the target geographic unit, temporal, metadata, and mosaic line information are obtained from the associated, verified complete dataset, ensuring that the provided information has a unique source, complete content, and strict correspondence with spatial location, thereby fundamentally guaranteeing the accuracy of the tracing results.

[0065] In some embodiments, to enhance user experience and provide an intuitive overview of spatiotemporal distribution, the land image information tracing method further includes a screen view image distribution thumbnail generation and interactive function. Specifically, the system captures the coordinates of the upper left and lower right corners of the screen view in real time through the coordinate acquisition interface of the client map window, and uses a coordinate transformation algorithm to convert them into a spatial coordinate system consistent with the WMTS service and index file, generating the current view's boundary data. Based on this view range, the system calls the corresponding index file and the "mosaic line-image-temporal-metadata" associated dataset, uses a spatial overlay analysis algorithm to extract all image-related records within the view, classifies them by image temporal phase or image ID, and statistically analyzes the spatial distribution range of each type of image and its area proportion within the view. Subsequently, the system dynamically generates thumbnails using "spatial outline mapping-color coding differentiation" technology: using the view range as the boundary, and through predefined color mapping rules (such as 2023 images)... The distribution areas of images from different time periods are marked in red (and blue for 2024 images), and corresponding color-time legends are added to the edges of the thumbnails. These thumbnails support zooming and panning, and are synchronized with the main map view through a coordinate mapping algorithm. Furthermore, the system establishes a three-dimensional linkage mechanism of "thumbnail-map view-information display": when a user clicks on an image distribution area in the thumbnail, the system uses a view positioning algorithm to focus the main map view on that area, displays the corresponding mosaic line boundary using a boundary highlighting algorithm, and automatically pops up a window showing the time period and metadata details of the image. In addition, the system supports multi-view comparison functionality; users can save thumbnails of view image distribution at different time points through a data storage interface for historical comparative analysis. This module allows users to customize thumbnail sizes (e.g., 200×200 pixels, 300×300 pixels), color mapping rules, and information annotation density to adapt to the needs of different terminal devices and application scenarios.

[0066] It should be noted that, to provide a macroscopic and intuitive overview of the image distribution within the field of view, the system also includes a module for generating and interacting with thumbnail images of the screen's field of view distribution. Its specific implementation is as follows: First, the coordinates of the upper left and lower right corners of the screen's field of view are captured in real time through the coordinate acquisition interface of the client's map window, and then converted into a spatial coordinate system consistent with the WMTS service using a coordinate transformation algorithm, thereby generating boundary data of the field of view. Second, based on the accompanying spatial vector index file and the four-dimensional associated dataset of "mosaic line-image-temporal-metadata," a spatial overlay analysis algorithm is used to extract all associated records of images within the field of view, classifying them according to image temporal phase or image ID, and statistically analyzing the spatial distribution range of each type of image and its area proportion within the field of view. Next, a "spatial approximation mapping and color coding distinction" technical solution is adopted. Using the extracted field of view as the thumbnail boundary, predefined color mapping rules (e.g., red for 2023 images and blue for 2024 images) are used to mark the distribution areas of images from different time periods on the thumbnail. Legendary labels with corresponding colors and times are set at the thumbnail edges. This thumbnail supports zooming and panning operations and achieves synchronous linkage with the main map's field of view through a coordinate mapping algorithm. Finally, a three-dimensional linkage mechanism of "thumbnail—map field of view—information display" is established. When a user clicks on an image distribution area in the thumbnail, the system automatically focuses the main map's field of view on that area using a field of view positioning algorithm, displays the corresponding mosaic line boundary using a boundary highlighting algorithm, and pops up a window showing the image's time period and metadata details. Simultaneously, this module supports multi-field of view comparison; users can save thumbnails of the field of view image distribution at different time points through a data storage interface to achieve historical comparative analysis. In addition, this technology module supports parameter customization. Users can set the thumbnail size (such as 200×200 pixels, 300×300 pixels), color mapping rules (such as setting colors according to seasonal categories), and information labeling density as needed to adapt to the needs of different terminal devices and specific application scenarios.

[0067] See Figure 4 To improve the efficiency of tile image generation, this invention provides a tile image generation method for use in network map tile services, including steps S401 to S404: Step S401: Receive a tile request initiated by the client, wherein the tile request includes the tile row and column numbers, scaling level, and service coordinate system; In some embodiments, when a client needs to load or update a map view, it sends a request to the published service. Specifically, the client follows the OGC WMTS standard protocol and sends a structured WMTS tile request to the server, where the request is received and parsed by the server over the network.

[0068] It's important to note that a standard WMTS tile request contains several core parameters for uniquely identifying the required map tiles. These parameters include at least: tile row and column numbers (used to locate the tile within a tile matrix at a specific zoom level), zoom level (used to determine the pyramid hierarchy of the map display), and service coordinate system (used to specify the spatial reference system used by the tile, such as CGCS2000, WGS84, etc.; this coordinate system must be aligned with the coordinate system of the backend index file). Additionally, the request typically includes a service identifier parameter to identify the specific service instance.

[0069] Step S402: Calculate the geographic range corresponding to the tile based on the tile row and column number, the zoom level, and the service coordinate system; In some embodiments, step S402 includes: calculating the geographic boundary coordinates corresponding to the tile using a tile grid calculation model, based on the tile row and column number, the scaling level, and the service coordinate system, and generating the geographic extent of the tile based on the geographic boundary coordinates. Specifically, after the server receives a WMTS request containing tile row and column number, scaling level, and service coordinate system parameters, it calls a preset WMTS tile grid calculation model to calculate the precise geographic boundary coordinates (usually latitude and longitude or projected coordinates) of the four corner points of the tile in the real world based on the specific tile row and column number, scaling level, and service coordinate system parameters provided in the request, using a reverse geographic coordinate extrapolation algorithm. After obtaining the precise geographic coordinates of the four corner points of the tile, the system generates a "spatial extent polygon of the tile" that accurately describes the surface area covered by the tile, based on these coordinate points and according to polygon construction rules (e.g., connecting the four corner points in sequence to form a closed loop). This polygon is the geographic extent of the tile required for subsequent spatial analysis.

[0070] It should be noted that the WMTS tile grid computing model is based on the standard WMTS tile pyramid and mesh generation rules.

[0071] It should be noted that, in order to ensure that the calculated coordinates can be accurately matched with high-resolution image data and subsequent spatial indexing, the coordinate values ​​generated in this calculation process must be retained to 6 decimal places.

[0072] Step S403: Based on the geographic range, perform spatial intersection matching in a pre-built index file to filter out at least one target geographic unit that intersects with the geographic range, wherein the index file is obtained based on the index file generation method described in the first aspect of the present invention; It should be noted that the method for generating the index file has been described in detail in Embodiment 1, so it will not be repeated here.

[0073] In some embodiments, step S403 includes: performing spatial intersection matching between the geographic range and the region geometric boundaries in the index file to locate index records that match the geographic range; and determining the geographic unit corresponding to the index record as the target geographic unit. Specifically, the geographic range generated in the previous step is spatially calculated with the polygon vector surface defined by the "Region Geometric Boundary (AREA_GEOM)" field of each record in the index file. By traversing and quickly retrieving candidate records with the spatial index (such as an R-tree) and performing precise geometric intersection operations, the system can locate all index records that meet the intersection conditions. Since the range of a tile may cover one or more geographic units in the index file, one or more matching index records are usually selected. Each successfully matched index record represents an independent geographic unit that is partially or completely covered by the tile. Subsequently, the system determines the geographic units corresponding to these matching records (i.e., the regions identified by their AREA_ID) as the target geographic units to be processed in this tile request.

[0074] It should be noted that spatial computation is a type of spatial intersection analysis, which aims to find all index records that intersect (i.e., overlap) with the tile range.

[0075] This efficient spatial computing (such as R-tree indexing) filters out specific geographic units that intersect with the geographic range of the tiles from the index, replacing the heavy preprocessing mode of "first pre-tiling all the tiles, then manually or semi-automatically associating them". This achieves on-demand and accurate image positioning, and greatly reduces unnecessary image data reading and processing.

[0076] Step S404: Retrieve the corresponding image segments according to the image identifiers associated with each target geographic unit, and seamlessly stitch the image segments in the tile coordinate system to generate an initial tile image. Write the target temporal information and target mosaic line information associated with each target geographic unit into the initial tile image to obtain the target tile image.

[0077] In some embodiments, the system retrieves corresponding image fragments based on the image identifiers associated with each target geographic unit. Specifically, for each target geographic unit, the system first extracts key image association fields from its corresponding index record, including MATCH_IMAGE_ID (matching original image ID) and SPLIT_IMAGE_FRAGMENT_ID (image fragment ID). These two identifiers uniquely identify the specific image data block to be retrieved, either jointly or independently. The system then initiates a precise resource retrieval request to the underlying image data repository or online image service based on these extracted image identifiers. It locates and reads the continuous image fragment in the original adapted image that perfectly corresponds to the geometric boundary of the target geographic unit, based on the SPLIT_IMAGE_FRAGMENT_ID (if it exists and is valid). If this field matches the MATCH_IMAGE_ID (i.e., not a split scene), the entire original image is directly retrieved.

[0078] It should be noted that the associated dataset not only stores metadata, but also maintains the access paths or indexes of the image physical files (or database storage blocks).

[0079] In some embodiments, seamlessly stitching the image segments in a tile coordinate system to generate an initial tile image includes: spatially aligning the image segments to obtain several spatial alignment results in the tile coordinate system; and stitching the spatial alignment results together to obtain the initial tile image. Specifically, using the precise geographic coordinates recorded in the "Area Geometric Boundary (AREA_GEOM)" of the corresponding target geographic unit in the index file as a reference, a coordinate projection transformation algorithm is used to transform and project each image segment from its original geographic coordinate system (or image coordinate system) to the tile coordinate system determined by the current tile request, thereby obtaining several "spatial alignment results" (i.e., image data blocks that have undergone coordinate transformation and geometric correction) that are accurately located in a unified tile coordinate system. Next, a spatial stitching algorithm is used to combine the image blocks according to the topological relationship of the area boundary in the index record, based on the spatial positional relationship of each image block in the tile coordinate system. Finally, all the aligned image blocks are integrated into a complete raster image covering the entire geographic area of ​​the tile, thus obtaining the initial tile image.

[0080] It should be noted that the AREA_GEOM index record ensures that the boundaries of adjacent image blocks can be accurately aligned, and corresponding pixel processing techniques (such as edge blending) are used to avoid visual defects such as misalignment or overlap, thereby achieving seamless stitching.

[0081] This method directly retrieves the corresponding image fragments from the index records and performs seamless stitching. Since the index ensures a precise correspondence between each geographic unit and the image fragment, and the fragment boundaries are clear, the data retrieval and stitching process is extremely efficient, avoiding the computational burden of real-time cropping and complex boundary fusion of the entire large image in traditional methods.

[0082] In some embodiments, the target temporal information and target mosaic line information associated with each target geographic unit are written into the initial tile image to obtain the target tile image. Specifically, the core attribute information associated with each target geographic unit matching the current tile is extracted from the index record. This information consists of the target temporal information (mainly the IMAGE_TIME field in the index record, i.e., the image capture time) and target mosaic line information (mainly the MOSAIC_ID in the index record, i.e., the unique identifier of the mosaic line). A concise metadata summary (such as METADATA_SUMMARY) is also extracted from the record. Subsequently, this structured text information is written into the metadata header file or predefined metadata tag segment of the initial tile image file generated in the previous step, using a method conforming to image format specifications and WMTS extension conventions.

[0083] Through this operation, the tile image, which originally only contained visual pixel data, was given spatiotemporal semantics that could be parsed by the client, thus obtaining a target tile image that contains both visual content and embedded precise spatiotemporal attribute descriptions.

[0084] It should be noted that this target tile image will be encapsulated according to the WMTS standard protocol and returned to the client. At the same time, the image and its embedded metadata will be cached in a high-performance caching system such as Redis to improve the response speed of subsequent identical requests.

[0085] It should be noted that, Based on the above-described embodiments of the land image information tracing method, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the land image information tracing method of any embodiment of the present invention.

[0086] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0087] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0088] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0089] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the land image information tracing method described in any of the above-described method embodiments of the present invention.

[0090] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0091] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for generating an index file, characterized in that, include: Based on the pairing and association relationships between the acquired mosaic images and mosaic lines, an associated dataset is formed; Based on the image coverage and predefined spatial partitioning rules in the associated dataset, the service area is divided into several geographic sub-regions; For each of the aforementioned geographic sub-regions, matching images are obtained from the image library to form a parent region and a correspondence between the parent region and the matching images; Based on the correspondence and the associated dataset, the parent region is divided to determine geographic units, corresponding index records are generated for each geographic unit, and the index records are summarized to generate the index file. The index records include spatial range fields, image association fields, and temporal metadata fields.

2. The index file generation method according to claim 1, characterized in that, The step of dividing the parent region and determining geographical units based on the correspondence and the associated dataset includes: If the adapted image is associated with a mosaic line, then the parent region is spatially clipped and segmented using the mosaic line as the dividing boundary, and the resulting split sub-regions are determined as the geographic units, wherein each split sub-region corresponds to a continuous image segment in the adapted image. If the adapted image has no associated mosaic lines, then the parent region is determined as the geographic unit.

3. The index file generation method according to claim 1, characterized in that, The associated dataset, formed based on the pairing and association relationships between the acquired mosaic images and mosaic lines, includes: Based on the rule engine, the shared unique identifier between the mosaic image and the mosaic line vector file is extracted to establish the first mapping relationship; For mosaic images and mosaic lines that do not conform to the filename matching rules in the first mapping relationship, the overlap between the geometric range of the mosaic lines and the spatial boundary of the image is calculated by spatial topology analysis. When the overlap exceeds a preset threshold, a second mapping relationship is established. Based on the first mapping relationship and the second mapping relationship, an associated dataset is formed.

4. A method for tracing the source of national land imagery information, characterized in that, Applications to web map tile services include: Respond to the client's image tracing request for a specific geographical location on the service map interface, and determine the geographical coordinates to be queried based on the image tracing request; Based on the geographic coordinates, a spatial inclusion determination is performed in a pre-built index file to identify the target geographic unit containing the geographic coordinates, wherein the index file is obtained based on the index file generation method of any one of claims 1-3; Based on the image identifier associated with the target geographic unit, the corresponding land image information is obtained from the associated dataset associated with the index file, wherein the land image information includes target temporal information, target metadata, and target mosaic line information.

5. The method for tracing land image information according to claim 4, characterized in that, The step of determining the target geographic unit containing the geographic coordinates by performing spatial inclusion determination in a pre-built index file based on the geographic coordinates includes: The spatial query interface of the index file is called, and the spatial inclusion determination algorithm and the spatial index structure pre-built in the index file are used to filter out the index records in which the geographic coordinates are contained in the geometric boundary of the region. The geographic unit corresponding to the index record is identified as the target geographic unit.

6. The method for tracing the source of land imagery information according to claim 4, characterized in that, The process of determining the geographic coordinates to be queried based on the image tracing request includes: The image tracing request is parsed to obtain the screen coordinates; Based on the service map interface parameters, the coordinate conversion interface is called to convert the screen coordinates into geographic coordinates consistent with the coordinate system of the index file.

7. A method for generating tile images, characterized in that, Applications to web map tile services include: Receive a tile request initiated by the client, wherein the tile request includes the tile row and column numbers, scaling level, and service coordinate system; Calculate the geographic range corresponding to the tile based on the tile row and column number, the zoom level, and the service coordinate system; Based on the geographic range, spatial intersection matching is performed in a pre-built index file to filter out at least one target geographic unit that intersects with the geographic range, wherein the index file is obtained based on the index file generation method of any one of claims 1-3; The corresponding image segments are retrieved according to the image identifiers associated with each target geographic unit, and the image segments are seamlessly stitched together in the tile coordinate system to generate an initial tile image. The target temporal information and target mosaic line information associated with each target geographic unit are written into the initial tile image to obtain the target tile image.

8. The tile image generation method according to claim 7, characterized in that, The step of performing spatial intersection matching in a pre-built index file based on the geographical range to filter out at least one target geographical unit that intersects with the geographical range includes: Spatial intersection matching is performed between the geographic range and the regional geometric boundaries in the index file to locate the index record that matches the geographic range; The geographic unit corresponding to the index record is determined as the target geographic unit.

9. The tile image generation method according to claim 7, characterized in that, The step of calculating the geographic range corresponding to a tile based on the tile row and column number, the scaling level, and the service coordinate system includes: calculating the geographic boundary coordinates corresponding to the tile based on the tile row and column number, the scaling level, and the service coordinate system using a tile grid calculation model, and generating the geographic range of the tile based on the geographic boundary coordinates.

10. The tile image generation method according to claim 7, characterized in that, The step of seamlessly stitching the image segments in a tile coordinate system to generate an initial tile image includes: The image segments are spatially aligned to obtain several spatial alignment results in the tile coordinate system. The spatial alignment results are stitched together to obtain the initial tile image.

Images