A spatial grid-based remote sensing data scheduling method

Abstract

The application discloses a kind of remote sensing data scheduling methods based on space grid, method includes: according to different levels, different scale remote sensing data, the index file of different levels is constructed;Different levels, different scale remote sensing data correspond to the data file of different scale map;Based on the index file, the latitude and longitude vector rectangular boundary of corresponding level is formed, and corresponding number is formed;Data file and index file are separated, stored;According to the latitude and longitude coordinates of request point, judge the latitude and longitude rectangular vector boundary range of corresponding level where request point is located, access the path where the data to be acquired is located, obtain corresponding data file information, complete the positioning and search of remote sensing data;The application improves data acquisition rate, reduces storage capacity.

Description

Technical Field

[0001] This invention relates to the field of remote sensing image processing technology, and in particular to a remote sensing data scheduling method based on spatial grids. Background Technology

[0002] Remote sensing data is characterized by its wide coverage, rapid updates, and high accuracy. It contains a wealth of spatial features, attribute features, and spatial relationships between data points, making it a crucial data source for Earth monitoring and assessment. Deep learning is widely used in the interpretation of various land features in remote sensing, offering high accuracy and effectiveness. However, most interpretation models remain confined to laboratory settings and are rarely applied to large-scale land feature extraction. Large-scale land feature extraction tasks are characterized by large data volumes, complex data formats, and heavy management burdens. Typical data organization methods require extensive data filtering, searching, and location processes, necessitating lengthy workflows and leading to data redundancy and increased storage pressure. Therefore, a multi-level spatial vector grid is needed to achieve effective scheduling of remote sensing data in large-scale remote sensing interpretation. Summary of the Invention

[0003] To address the issues of slow data acquisition rate and large data storage volume in existing remote sensing data scheduling technologies, a spatial grid-based remote sensing data scheduling method is proposed to improve data acquisition rate and reduce storage volume.

[0004] The technical solution of the present invention is as follows:

[0005] A remote sensing data scheduling method based on spatial grids includes the following steps:

[0006] Based on remote sensing data of different levels and scales, index files of different levels are constructed; remote sensing data of different levels and scales correspond to data files of maps of different scales.

[0007] Based on the index file, latitude and longitude vector rectangle boundaries for the corresponding level are formed, and corresponding numbers are generated; the data file and the index file are separated and stored.

[0008] Based on the latitude and longitude coordinates of the requested point, determine the latitude and longitude rectangular vector boundary range of the corresponding layer where the requested point is located, access the path where the data to be acquired is located, obtain the corresponding data file information, and complete the location and search of remote sensing data.

[0009] Preferably, based on remote sensing data of different levels and scales, index files of different levels are constructed, including:

[0010] A first-level index file is created based on the data file of the first-scale map, a second-level index file is created based on the data file of the second-scale map, and a third-level index file is created based on the data file of the second-scale map after it has been divided into sections.

[0011] Furthermore, the index file includes image size, attribute fields, file path, and geographic coverage.

[0012] The attribute fields include file number and administrative division.

[0013] Furthermore, the data files include data tags, attributes, and image framing features at different levels for each data file.

[0014] The image framing features at different levels are obtained by framing the image in different ways according to the different levels used in the data scheduling method.

[0015] Furthermore, after the data file is divided into sections, it includes data tags and attribute information corresponding to the image coverage area; and the tags correspond one-to-one.

[0016] Information corresponding to the data file is stored in the index file by creating an index. The index file includes the data number and the administrative region information where the data is located.

[0017] Query and locate the corresponding data file by accessing the file.

[0018] When the attributes of the corresponding data file are updated or modified, the index file is modified by feature overlay to update the file attribute information.

[0019] Preferably, the specific process for data location and retrieval is as follows:

[0020] Based on the latitude and longitude coordinates (X, Y) of the requested point, locate it using the following formula:

[0021] Xleft <X<Xright

[0022] Ybottem <Y<Ytop

[0023] area∈{Xleft <X||X<Xright||Ybottem<Y||Y<Ytop}

[0024] Determine the rectangular vector boundary range of the location, complete the data location and search, and index upwards to obtain higher-level image data.

[0025] Preferably, the index file records the index information of the data file, and the data cache is accessed through the data index.

[0026] Data caching includes:

[0027] The image data is separated and stored according to the first scale, second scale, and third scale refinement data. Each storage node contains an image of a fixed size, covering a fixed spatial range.

[0028] The highest level corresponds to the scope and area required by the application scenario.

[0029] The second level is divided according to longitude and latitude grids to obtain the first-scale remote sensing image data.

[0030] The third level performs standard framing on the first-scale image to obtain the corresponding number of second-scale framing images.

[0031] The fourth level performs rule-based segmentation operations to varying degrees based on different usage scenarios, inputs the corresponding attribute information of the data, and performs regional combination and spatial analysis.

[0032] Each level is indexed by the spatial relationship of vector boundaries; each level contains data attribute information for the corresponding level, which is indexed and located through attribute fields.

[0033] Furthermore, the data index includes:

[0034] The index corresponds to the data storage, which is saved hierarchically and forms file numbers.

[0035] The index records the data at each level, including its own unique identifier, geographical coverage, and top-left and bottom-right location information.

[0036] The index's geographic coverage records the administrative division location of the data.

[0037] The index records the data storage path information to locate the data.

[0038] The index records the original data size information and attribute fields.

[0039] Furthermore, higher-level remote sensing data is retrieved by indexing upwards, and the data is retrieved and located using index file numbers. Based on the independent branches in the quadtree structure, the data files are modified, and the corresponding index files are updated. The specific steps are as follows:

[0040] Update the image Picture_ID corresponding to the data as needed, the latitude and longitude coordinates (X, Y), locate the vector boundary position of the data area, construct the access path of the image Picture_ID, obtain the path Picture[0]-Picture[N] of the same level, index upward from the same level path, obtain the data Picture of the previous level, obtain the vector grid information of the corresponding dimension of the data Picture of the previous level, locate the latitude and longitude coordinate group {[x1,y1],[x1,y2],[x2,y1],[x2,y1]} of the map sheet where the image Picture_ID of the data is located, extract the Picture_ID from the data Picture, replace the required image in the form of a geographic grid, perform image pseudo-cropping, separate the data from the data of the previous level, and modify the index file of the updated data.

[0041] Furthermore, the upper level corresponds to the boundary position range of the data vector, and the lower level is the first scale data map containing the range, numbered from Picture[0] to Picture[n].

[0042] The data is divided into segments based on the data of the previous level. The segmented data is numbered accordingly and sent to the Picture[0]_ID[n]_[0]_[0] layer, which is the smallest storage unit of the data. This level records the attribute information of the data and performs data union to form an associated division. The recording and updating of attribute fields form the feature superposition of unified data and increase the feature dimension.

[0043] The beneficial effects of this invention are:

[0044] 1. By calculating the latitude and longitude coordinates of points, the path to the data can be found, enabling quick location and retrieval of the data.

[0045] 2. By creating hierarchical index files, the stored data is divided, reducing the amount of data stored and effectively reducing the pressure on the stored data. Attached Figure Description

[0046] Figure 1 A flowchart of a data scheduling method based on a spatial grid provided in an embodiment of this application.

[0047] Figure 2 A schematic diagram of the purchase index file and data file provided in the embodiments of this application.

[0048] Figure 3 This is a diagram showing the separate storage of index files and data files provided in the embodiments of this application.

[0049] Figure 4 This is a schematic diagram illustrating the data location and retrieval process provided in the embodiments of this application.

[0050] Figure 5 This is a schematic diagram of a spatial quadtree structure provided in an embodiment of this application.

[0051] Figure 6 This is a schematic diagram illustrating the relationship between data caching and data indexing provided in an embodiment of this application.

[0052] Figure 7 This is a schematic diagram of the image update process provided in an embodiment of this application.

[0053] Figure 8 This is a schematic diagram of the data update process provided in the embodiments of this application. Detailed Implementation

[0054] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0055] Example 1

[0056] In this embodiment, as Figure 1 As shown, a remote sensing data scheduling method based on spatial grids includes the following steps:

[0057] S101: Construct index files of different levels based on remote sensing data of different levels and scales; remote sensing data of different levels and scales correspond to data files of maps of different scales;

[0058] S102: Based on the index file, form latitude and longitude vector rectangle boundaries for the corresponding level and generate corresponding numbers; separate and store the data file and the index file;

[0059] S103: Determine the latitude and longitude rectangular vector boundary range of the corresponding layer where the requested point is located based on the latitude and longitude coordinates of the requested point, access the path where the data to be acquired is located, obtain the corresponding data file information, and complete the location and search of remote sensing data.

[0060] In this embodiment, as Figure 2 As shown, based on remote sensing data of different levels and scales, index files of different levels are constructed, including:

[0061] A first-level index file is created based on the data file of the first-scale map, a second-level index file is created based on the data file of the second-scale map, and a third-level index file is created based on the data file of the second-scale map after it has been divided into sections.

[0062] More specifically, the index file includes image size, attribute fields, file path, and geographic coverage.

[0063] The attribute fields include file number and administrative division.

[0064] More specifically, the data files include data tags, attributes, and image framing features at different levels for each data file.

[0065] The image framing features at different levels are obtained by framing the image in different ways according to the different levels used in the data scheduling method.

[0066] More specifically, such as Figure 3 As shown, after the data file is divided into sections, it includes data markers and attribute information corresponding to the image coverage area; and the markers correspond one-to-one.

[0067] Information corresponding to the data file is stored in the index file by creating an index. The index file includes the data number and the administrative region information where the data is located.

[0068] Query and locate the corresponding data file by accessing the file.

[0069] When the attributes of the corresponding data file are updated or modified, the index file is modified by feature overlay to update the file attribute information.

[0070] More specifically, such as Figure 4 As shown, the specific process for locating and searching data is as follows:

[0071] Based on the latitude and longitude coordinates (X, Y) of the requested point, locate it using the following formula:

[0072] Xleft <X<Xright

[0073] Ybottem <Y<Ytop

[0074] area∈{Xleft <X||X<Xright||Ybottem<Y||Y<Ytop}

[0075] Determine the rectangular vector boundary range of the location, complete the data location and search, and index upwards to obtain higher-level image data.

[0076] like Figure 5 , Figure 6 As shown, a spatial quadtree structure is constructed to manage data files and index files.

[0077] The spatial quadtree structure includes two parts: data cache and data index.

[0078] The index file records the index information of the data file, and the data index accesses the data cache.

[0079] Data caching includes:

[0080] The image data is separated and stored according to the first scale, second scale, and third scale refinement data. Each storage node contains an image of a fixed size, covering a limited spatial range.

[0081] The top level of a quadtree is its root node, which corresponds to the range required by the application scenario.

[0082] The second layer of the quadtree is divided according to longitude and latitude grids to obtain the first scale remote sensing image data.

[0083] In the third layer of the quadtree, the first scale image is divided into standard frames to obtain the corresponding number of second scale frames.

[0084] At the fourth level of the quadtree, different levels of rule-based segmentation are performed according to different usage scenarios, and the corresponding attribute information of the data is entered to perform regional combination and spatial analysis.

[0085] Each level of the quadtree structure is indexed by the spatial relationship of vector boundaries; each level of the quadtree structure contains data attribute information of the corresponding level, which is indexed and located through attribute fields.

[0086] Data indexes include:

[0087] The index corresponds to the data storage, which is saved hierarchically and forms file numbers.

[0088] The index records the data at each level, including its own unique identifier, geographical coverage, and top-left and bottom-right location information.

[0089] The index's geographic coverage records the administrative division location of the data.

[0090] The index records the data storage path information to locate the data.

[0091] The index records the original data size information and attribute fields.

[0092] like Figure 7 , Figure 8 As shown, higher-level remote sensing data is retrieved by indexing upwards, and the data is retrieved and located by index file number. Based on the independent branches in the quadtree structure, data files are modified, and the corresponding index files are updated. The specific steps are as follows:

[0093] Update the image Picture_ID corresponding to the data as needed, the latitude and longitude coordinates (X, Y), locate the vector boundary position of the data area, construct the quadtree access path of the image Picture_ID, obtain the path Picture[0]-Picture[N] at the same level of the quadtree, index upwards from the same level path, obtain the label of the previous level data Picture, obtain the generated vector grid information of the corresponding dimension of the previous level data Picture, locate the latitude and longitude coordinate group {[x1,y1],[x1,y2],[x2,y1],[x2,y1]} of the map sheet where the image Picture_ID of the data is located, extract the Picture_ID from the data Picture, replace the required image in the form of a geographic grid, perform image pseudo-cropping, separate the data from the previous level data, modify the index file of the updated data; the data Picture has 368 corresponding sub-IDs.

[0094] Based on the latitude and longitude coordinates of the requested point, the rectangular vector boundary, and the spatial and attribute characteristics of the associated data, feature overlay is performed to increase the feature dimension of the data.

[0095] Furthermore, the upper level of the tree structure corresponds to the boundary position range of the data vector; the lower level is the first scale data map containing the range, numbered from Picture[0] to Picture[n].

[0096] The data is divided into segments based on the data of the previous level. The segmented data is numbered accordingly and sent to the Picture[0]_ID[n]_[0]_[0] layer, which is the smallest storage unit of the data. This level records the attribute information of the data and performs data union to form an associated division. The recording and updating of attribute fields form the feature superposition of unified data and increase the feature dimension.

[0097] In this embodiment, the first scale is 1:1,000,000, the second scale is 1:10,000, and the third scale is 1:10,000 or less.

[0098] Example 2

[0099] S201: The data request point coordinates are 112.137°E, 29.632°N. Request to retrieve the 1:10000 image of the point location and the 1:500 image data of the point location and update it.

[0100] S202: After point conversion, the rectangular vector boundary range of its H49 grid has a grid scale of 1:1000000.

[0101] S203: Perform spatial association within grid number H49, requesting points contained in grid number H49G057067 with a grid scale of 1:10000. Access the index file and retrieve the image data of grid number H49G057067.

[0102] S204: The requested point is located in H49G057067 at row and column number (3, 2), number 26, and pixel coordinates 1521, 971. Access the next level 1:500 index file of H49G057067, locate the data, and perform pseudo-cropping.

[0103] S205: Request location data, update the data, and then update the index file information.

[0104] Example 3

[0105] In this embodiment, a remote sensing data scheduling method based on spatial grids includes the following steps:

[0106] A spatial quadtree structure is constructed, and index files are created at different levels based on the data files of maps with different scales; the latitude and longitude coordinates of the four endpoints of the image are recorded to form a closed rectangular vector boundary; the data files and index files are separated.

[0107] Based on the latitude and longitude coordinates of the requested point, determine the rectangular vector boundary range where the requested point is located, access the path where the data to be obtained is located, obtain the corresponding data file information, and complete the location and search of the data.

[0108] Based on the independent branches in the quadtree structure, modify the data file and update the corresponding index file of the modified data file.

[0109] Based on data files from maps of different scales, index files are created at different levels, including:

[0110] A primary index is created for 1:1,000,000 scale maps, a secondary index is created for 1:10,000 scale maps, and a tertiary index file is created for the data after the 1:10,000 scale file is divided into sections. At the same time, the index file is separated from the original data to facilitate quick access and location of the data.

[0111] Image data is stored separately according to scales of 1:1,000,000, 1:10,000, and smaller. Each storage node contains a fixed-size image covering a limited spatial range. The top layer of the tree is the root node of the quadtree, representing the area required for the application scenario. The second layer is divided according to a grid of longitude [-180, 180] and latitude [90, -90] to obtain 1:1,000,000 level remote sensing image data. In the third layer of the quadtree, the 1:1,000,000 images are standardly divided into 9,216 1:10,000 images. In the fourth layer of the quadtree, different degrees of regular framing are performed according to different application scenarios, and the corresponding attribute information of the data is entered to facilitate regional fusion and spatial analysis.

[0112] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the claims of the present invention.

Claims

1. A remote sensing data scheduling method based on spatial grids, characterized in that, Includes the following steps: Based on remote sensing data of different levels and scales, index files of different levels are constructed, specifically as follows: A first-level index file is created based on the data file of the first-scale map, a second-level index file is created based on the data file of the second-scale map, and a third-level index file is created for the data file after the data file corresponding to the second-scale map is divided into sections; remote sensing data of different levels and scales correspond to data files of different scale maps; the data files include data tags, attributes, and image sectioning features of different levels for each data file. Based on the index file, latitude and longitude vector rectangle boundaries of the corresponding level are formed, and corresponding numbers are generated; the data file and the index file are separated and stored; wherein the index file records the index information of the data file, and the data cache is accessed through the data index; Data caching includes: The image data is separated and stored according to the first scale, the second scale, and the third scale. Each storage node contains an image of a fixed size, covering a fixed spatial range. The highest level corresponds to the scope and area required by the application scenario; The second level is divided according to longitude and latitude grids to obtain the first scale remote sensing image data; The third level performs standard framing on the first scale remote sensing image data to obtain the corresponding number of second scale framing images. The fourth level performs rule-based segmentation operations to varying degrees based on different usage scenarios, inputs the corresponding attribute information of the data, and performs regional combination and spatial analysis. Each level is indexed by the spatial relationship of the latitude and longitude vector rectangle boundaries; each level contains the data file attribute information of the corresponding level, which is indexed and located through attribute fields; Based on the latitude and longitude coordinates of the requested point, determine the latitude and longitude rectangular vector boundary range of the corresponding layer where the requested point is located, access the path where the data to be acquired is located, obtain the corresponding data file information, and complete the location and search of remote sensing data. The specific process of data location and search is as follows: Based on the latitude and longitude coordinates (X, Y) of the requested point, locate it using the following formula: Xleft <X<Xright Ybottem <Y<Ytop area∈{Xleft <X||X<Xright||Ybottem<Y||Y<Ytop} The process involves determining the boundary range of the latitude and longitude vector rectangle where the location is situated, completing the data location and retrieval, and then indexing upwards to obtain higher-level remote sensing data. This higher-level remote sensing data is retrieved and located using the index file number. Based on the independent branches in the quadtree structure, the data file is modified, and the corresponding index file is updated. When the attributes of the corresponding data file are updated or modified, the index file is modified using feature overlay to update the data file attribute information. The specific steps are as follows: Update the image Picture_ID corresponding to the data as needed, the latitude and longitude coordinates (X, Y), locate the boundary position of the latitude and longitude vector rectangle where the data is located, construct the access path of the image Picture_ID, obtain the path Picture[0]-Picture[N] at the same level, index upward from the same level path, obtain the label of the data Picture at the next higher level, obtain the vector grid information generated in the corresponding dimension of the data Picture at the next higher level, locate the latitude and longitude coordinate group {[x1,y1],[x1,y2],[x2,y1],[x2,y1]} of the map sheet where the image Picture_ID of the data is located, extract the Picture_ID from the data Picture at the next higher level, replace the required image in the form of a geographic grid, perform image pseudo-cropping, separate the data from the data Picture at the next higher level, modify the index file of the updated data; the latitude and longitude vector rectangle boundary position range of the data at the next higher level, the next level is the first scale data map containing the range, numbered from Picture[0] to Picture[n]; The next level of data is divided into segments based on the data of the previous level. The segmented data is numbered accordingly and sent to the Picture[0]_ID[n]_[0]_[0] layer of the data, which is the smallest storage unit of the data. The next level of data records the attribute information of the data, and the data is combined to form an associated division. The recording and updating of attribute fields form the feature superposition of unified data, increasing the feature dimension.

2. The remote sensing data scheduling method based on spatial grids according to claim 1, characterized in that, The index file includes image size, attribute fields, file path, and geographic coverage. The attribute fields include file number and administrative division.

3. The remote sensing data scheduling method based on spatial grids according to claim 2, characterized in that, The image framing features at different levels are obtained by framing the image in different ways according to the different levels used in the data scheduling method.

4. The remote sensing data scheduling method based on spatial grids according to claim 3, characterized in that, After the data file is divided into sections, it includes data markers and attribute information corresponding to the image coverage area; The information corresponding to the data file is stored in the index file by creating an index. The index file includes the data number and the administrative region information where the data number is located. Query and locate the corresponding data file by accessing the file.

5. The remote sensing data scheduling method based on spatial grids according to claim 4, characterized in that, Data indexes include: The index corresponds to data storage, which is saved hierarchically and forms file numbers; The index records the data at each level, including its own number, geographical coverage, top-left location information, and bottom-right location information. The index's geographic coverage records the administrative division location of the data. The index records the data storage path information to locate the data; The index records the original data size information and attribute fields.

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