A skeleton-based geographical map reconstruction method

Abstract

A skeleton-based geographic map reconstruction method comprises the following steps: (1) extracting geographic map boundary data; (2) simplifying the map by using the Douglas-Peucker algorithm; (3) extracting the skeleton structure of the map by using an angle bisector-based skeleton extraction algorithm; (4) skeleton regularization, converting irregular skeleton forms into standard forms; (5) skeleton-based boundary growth, in a two-dimensional grid, each skeleton line segment takes the cell where it is initially located as the starting point, and the boundary is grown and expanded; (6) after the above operation, each region is converted into a block composed of rectangles or right-angle rectangles, and different regions are filled with different colors to visually distinguish them. The present application successfully solves the problem of irregular map boundaries, reduces the visual burden, and is more visually appealing and easier to understand, while effectively ensuring that the reconstructed map and the original map have high consistency in adjacency and area share.

Description

Technical Field

[0001] This invention relates to a skeleton-based geographic map reconstruction method. Background Technology

[0002] Spatial visualization has always been an important research direction in the fields of information visualization and visual analytics. Maps, in particular, serve as a medium for visualizing spatial data, helping users to more easily understand and analyze spatial information. Map visualization has wide applications in various fields such as Geographic Information Systems (GIS), urban planning, traffic management, and environmental monitoring.

[0003] Geographic maps are invaluable in facilitating visual exploration, providing key natural features such as topology, shape, and area, which inherently aid in understanding spatial relationships and patterns. However, raw geographic maps often fall short in visual friendliness, presenting challenges in terms of aesthetics and simplicity. Consequently, creating visualizations directly on maps is relatively uncommon, and the use of advanced techniques such as heatmaps, embedded bar charts, or pie charts is often limited.

[0004] Geographic map reconstruction methods can be broadly categorized into two types: deformation-based methods and tile-based methods. Deformation-based reconstruction methods visually alter region boundaries to express relevant statistical information. In other words, deformation-based map reconstruction distorts map boundaries by adjusting region sizes according to statistical parameters, while maintaining the recognizable nature of the reconstructed map. Tile-based reconstruction methods utilize regular tiles, such as squares or hexagons, to convert the original geographic map into a grid-based representation, which enhances map readability to some extent. Both methods employ an "outside-in" strategy to alter boundaries.

[0005] Existing reconstruction algorithms, whether for representing statistical information or beautifying boundaries, do not fully consider all the attributes of the original map during the reconstruction process. Over-graphicalizing regions to display additional data such as regional statistics can reduce map realism and recognizability. Some algorithms also use arcs to draw the outlines of map regions, which can easily lead to overlap between adjacent areas. Therefore, geographic map reconstruction is a multi-criteria optimization problem.

[0006] Map reconstruction has wide applications in numerous fields. In terms of methodological applications, combining hexagonal grid diagrams and information layers provides a novel approach to exploring and analyzing retail data. In terms of technical implementation, extending visual parametric spatial analysis to the spatial domain allows for the selection of variables that effectively represent complex entities from multiple variables while considering geographical features. In terms of technology transfer, map reconstruction visualization techniques can be applied to other analytical tasks, such as visualizing egocentric information diffusion patterns, analyzing thematic hierarchy, and metaphorically conveying information forwarding processes. Summary of the Invention

[0007] To overcome the challenges of existing geographic map reconstruction techniques, this invention proposes a skeleton-based geographic map reconstruction method. This method extracts map boundary data and simplifies the map to obtain a simplified map skeleton structure. After merging, deleting, and regularizing the skeleton structure, the final skeleton structure is obtained, typically a line segment structure consisting of two points or a right-angle structure consisting of three points. Based on this skeleton structure, its boundaries are grown and expanded until there are no blank areas between the blocks. This method employs an "inside-out" strategy to reconstruct the map, preserving the shape of the original map while ensuring topological consistency and boundary regularity.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0009] A skeleton-based geographic map reconstruction method, the algorithm comprising the following steps:

[0010] Step 1: Extract geographic boundary data: Extract the boundary data of the map to ensure that the shape of the map generated by the algorithm provided by this invention is the same as the corresponding location of the area in reality;

[0011] Step 2, Map Simplification: A simplified map is obtained using the Douglas-Peucker algorithm to reduce distortion points in the original map data. The simplified map boundary lines consist of a series of key points and line segments connecting them.

[0012] Step 3, Map Skeleton Extraction: An angle bisector-based skeleton extraction algorithm is used to extract the simplified map skeleton. The process is as follows:

[0013] First, the boundary is obtained on the simplified map. For the set of pixels on the boundary, corner detection is used to identify the corners on the boundary. Corners are the turning points or intersections of the boundary. For each corner, the two adjacent corners are found, and the angle bisector between them is calculated. The line segments on the angle bisector are connected, and the intersection of the line segments are the nodes of the skeleton. The connected line segments represent the skeleton structure of the map.

[0014] Step 4, Skeleton Regularization: Convert irregular skeletons into standard forms: Based on the extracted map skeleton structure, firstly, loop through and merge the two adjacent and closest nodes in the skeleton; then delete unnecessary leaf nodes; for the skeleton after deleting leaf nodes, loop through and merge the two adjacent and closest nodes again until the final skeleton structure has only two or three nodes.

[0015] Step 4.1: Loop through and merge the two closest adjacent nodes in the skeleton nodes: Traverse the linked list l l From the list of skeleton nodes l n Obtain the corresponding node coordinates and calculate the distance d between two nodes P(x1,y1) and Q(x2,y2). If the distance between these two connected nodes is the smallest among all linked nodes, merge these two nodes into a single node R, and delete P and Q from the original list of nodes. The x and y coordinates of the new node R are the midpoint between the x and y coordinates of the original two nodes. The specific formula is as follows:

[0016]

[0017] Iterate through the above process until the number of remaining nodes is 50% of the original skeleton nodes. Update the node list. n For l nn1 Link list l For l ln1 .

[0018] Step 4.2, Delete unnecessary leaf nodes: Traverse l ln1 If a node has one and only one connected node, then the node is a leaf node. Record the distance between the node and the connected node according to formula (2). Delete nodes from smallest to largest according to the distance between leaf nodes until the number of remaining nodes is 50% of the number of nodes before deletion.

[0019] Step 4.3: Loop through and merge the two closest adjacent nodes in the skeleton nodes: Traverse l ln1 From l nn1 Obtain the corresponding node coordinates, calculate the distance between two nodes, and then merge the nodes according to formula (1). Iterate the above process until the number of remaining nodes is less than or equal to 3. Update the node list l nn1 For l nn2 Link list ln1 For l ln2 .

[0020] Step 4.4, Correcting the skeletal structure:

[0021] If the number of remaining skeleton nodes is 2, calculate the two nodes P1(x) p1 ,y p1 P2(x)p2 ,y p2 ) Δx and Δy. If The y-values ​​of the two points remain unchanged, and the x-values ​​are both taken as the midpoint of the horizontal direction between the two points, forming a line segment in the vertical direction; if The x-values ​​of the two points remain unchanged, while the y-values ​​are both taken as the midpoint of the vertical direction between the two points, forming a horizontal line segment, as shown in the formula below:

[0022]

[0023] If the number of skeleton nodes is 3, then P1(x) p1 ,y p1 P2(x) p2 ,y p2 P3(x) p3 ,x p3 ), calculate Δx from the middle node to the two side nodes according to formula (4) k and Δy k .like The y-values ​​of the two points remain unchanged, and the x-values ​​are both taken as the midpoint of the horizontal direction between the two points, forming a line segment in the vertical direction; if The x-values ​​of the two points remain unchanged, while the y-values ​​are both taken as the midpoint of the vertical direction between the two points, forming a horizontal line segment. Combining these line segments creates a right-angled skeletal structure. The formula is summarized below, where P... a P b It represents any two adjacent points among P1, P2, and P3.

[0024]

[0025] Step 5: Boundary growth based on skeleton: The boundary is grown based on the skeleton. The extracted skeleton is placed in a two-dimensional grid, and the skeleton segments maintain relative size and relative position. For each skeleton segment, starting from the cell where it was initially located, it is grown and expanded in four directions: up, down, left, and right. The expanded cell represents the area covered by the skeleton segment. The above growth process is repeated until there are no blank areas between all blocks, and the reconstructed target map is obtained.

[0026] Step 5.1: Place the extracted skeleton in a suitable 2D grid, keeping the relative position and size of each skeleton segment unchanged. Divide the skeleton segments into straight skeletons, right-angled skeletons, and hybrid skeletons.

[0027] Step 5.2: When the skeletal structure of a certain region consists of multiple straight-line skeletons or right-angle skeletons, first split the skeleton, dividing the right-angle skeleton into two line segment skeletons, to obtain a set of line segment skeletons R = {r1, r2, ..., rn}, and record the set of line segment skeletons D = {(r i1 ,r j1 ), (r i2 ,r j2 ),…,(r ik ,r jk Line segments from the same right-angled skeleton are marked with the same color.

[0028] Step 5.3: Expand the skeleton according to the skeleton growth rules: Each line segment skeleton grows in four directions (up, down, left, and right) according to the size of the cell on the grid. If a certain area has been occupied by other line segment skeletons, growth in that direction will stop.

[0029] Step 5.4, iterate through step 5.3 until there are no blank areas between any two grown blocks.

[0030] Step 5.5: Based on set D = {(r i1 ,r j1 ), (r i2 ,r j2 ),…,(r ik ,r jk The regions growing from the same right-angled skeleton are merged to obtain the reconstructed target map, which is formed by splicing together multiple rectangles and right-angled rectangles.

[0031] In step 5, to accurately obtain the target map, the following constraints are given:

[0032] (1) Ensure that the map skeleton line segments maintain their relative size in the two-dimensional grid;

[0033] (2) Ensure that the constructed blocks maintain their original relative positions to avoid information loss;

[0034] (3) Ensure that all blocks do not overlap;

[0035] (4) Ensure that there are no blank areas between the two blocks.

[0036] Step 6: After the above operations, each area on the original geographic map is converted into a block composed of rectangles or right-angled rectangles, and different blocks are filled with different colors for visual differentiation.

[0037] The technical concept of this invention is as follows: This invention provides a skeleton-based geographic map reconstruction method. This method extracts the skeleton from a simplified map, and then performs "territorial expansion" based on the skeleton of each sub-region of the map. The area occupied by the skeleton is determined by the occupied grid, using this inside-out approach to determine boundaries, thus providing an aesthetically pleasing and clear visual experience. This visualization method, based on the traditional Douglas-Peucker algorithm, proposes a skeleton regularization algorithm and a boundary growth algorithm specifically for geographic maps.

[0038] The data foundation of this invention is regional data, such as administrative region geographic data, constructed from a series of geographic coordinate data. This data is encoded in GeoJSON format, which represents geographic features (a list of discrete boundary coordinate points, regional centroids) and non-spatial attributes (such as sub-region names, postal codes, administrative levels, etc.) in a simple format. This visualization method can read the aforementioned geographic data and, through algorithms, design a deformed map that meets actual needs, thereby more effectively and vividly displaying the required content.

[0039] The advantages of this invention are: while effectively ensuring a high degree of consistency between the reconstructed map and the original map in terms of adjacency relationships and area share, the reconstructed map is more aesthetically pleasing and easier to understand, reducing visual burden. During the map region skeleton regularization process, the final skeleton structure consists of vertical or horizontal line segments composed of two nodes and right-angled broken line segments composed of three nodes. The results obtained by expanding the region based on this structure are visually more aesthetically pleasing and easier to understand. Attached Figure Description

[0040] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0041] Figure 1 This is a schematic diagram illustrating the process of reconstructing the original map according to the present invention.

[0042] Figure 2 This is a schematic diagram of the map simplification process of the present invention.

[0043] Figure 3 This is a schematic diagram of the Douglas-Peucker algorithm in this invention.

[0044] Figure 4 This is a schematic diagram of the skeleton extraction algorithm based on angle bisectors of the present invention.

[0045] Figure 5 This is a schematic diagram of the skeleton regularization process of the present invention.

[0046] Figure 6 This is a schematic diagram of the line segment skeleton boundary growth process of the present invention.

[0047] Figure 7 This is a schematic diagram of the right-angled skeleton boundary growth process of the present invention.

[0048] Figure 8 This is a schematic diagram of the multi-type skeleton boundary growth process of the present invention. Detailed Implementation Plan

[0049] Example 1

[0050] See Figures 1 to 8 ,like Figure 1 This paper demonstrates the complete process of the method of this invention. A GeoJSON-encoded Zhejiang Province geographic map dataset is selected, which contains geographic features (a list of discrete boundary coordinate points, regional centroids) and non-spatial attributes (e.g., sub-region names, postal codes, administrative levels, etc.). Boundary data of the Zhejiang Province geographic map is extracted, retaining the largest sub-regions. A simplified map is obtained using the Douglas-Peucker algorithm. The map skeleton structure is obtained through an angle bisector-based skeleton extraction algorithm. After merging nodes, deleting leaf nodes, and regularizing the skeleton structure, the skeleton structure of each sub-region is obtained. Boundaries of each sub-region are grown in a two-dimensional grid using a skeleton boundary growth algorithm until there are no blank areas between blocks. Different colors are filled into each block for visual distinction.

[0051] A skeleton-based geographic map reconstruction method specifically includes the following steps:

[0052] Step 1: Extract geographic boundary data: Extract the boundary data of the map to ensure that the shape of the map generated by the algorithm provided by this invention is the same as the corresponding location of the area in reality;

[0053] Step 2, as follows Figure 2 Map simplification: A simplified map is obtained using the Douglas-Peucker algorithm to reduce distortion points in the original map data. The algorithm process is as follows: Figure 3 The final simplified map consists of a series of key points and line segments connecting them;

[0054] Step 3, as follows Figure 4 Map skeleton extraction: An angle bisector-based skeleton extraction algorithm is used to extract the simplified map skeleton. The process is as follows:

[0055] First, the boundary is obtained on the simplified map. For the set of pixels on the boundary, corner detection is used to identify the corners on the boundary. Corners are the turning points or intersections of the boundary. For each corner, the two adjacent corners are found, and the angle bisector between them is calculated. The line segments on the angle bisector are connected, and the intersection of the line segments are the nodes of the skeleton. The connected line segments represent the skeleton structure of the map.

[0056] Step 4, as follows Figure 5 Skeleton regularization: Convert irregular skeletons into standard forms: Based on the extracted map skeleton structure, firstly, iteratively merge the two adjacent and closest nodes in the skeleton; then, delete unnecessary leaf nodes; for the skeleton after deleting leaf nodes, iteratively merge the two adjacent and closest nodes again, until the final map skeleton has only two or three nodes left.

[0057] Step 4.1: Loop through and merge the two closest adjacent nodes in the skeleton nodes: Traverse the linked list l l From the list of skeleton nodes l n Obtain the corresponding node coordinates and calculate the distance d between two nodes P(x1,y1) and Q(x2,y2). If the distance between these two connected nodes is the smallest among all linked nodes, merge these two nodes into a single node R, and delete P and Q from the original list of nodes. The x and y coordinates of the new node R are the midpoint between the x and y coordinates of the original two nodes. The specific formula is as follows:

[0058]

[0059] Iterate through the above process until the number of remaining nodes is 50% of the original skeleton nodes. Update the node list. n For l nn1 Link list l For l ln1 .

[0060] Step 4.2, Delete unnecessary leaf nodes: Traverse l ln1 If a node has one and only one connected node, then the node is a leaf node. Record the distance between the node and the connected node according to formula (2). Delete nodes from smallest to largest according to the distance between leaf nodes until the number of remaining nodes is 50% of the number of nodes before deletion.

[0061] Step 4.3: Loop through and merge the two closest adjacent nodes in the skeleton nodes: Traverse l ln1 From l nn1 Obtain the corresponding node coordinates, calculate the distance between two nodes, and then merge the nodes according to formula (1). Iterate the above process until the number of remaining nodes is less than or equal to 3. Update the node list l nn1 For lnn2 Link list ln1 For l ln2 .

[0062] Step 4.4, Correcting the skeletal structure:

[0063] If the number of remaining skeleton nodes is 2, calculate the two nodes P1(x) p1 ,y p1 P2(x) p2 ,y p2 ) Δx and Δy. If The y-values ​​of the two points remain unchanged, and the x-values ​​are both taken as the midpoint of the horizontal direction between the two points, forming a line segment in the vertical direction; if The x-values ​​of the two points remain unchanged, while the y-values ​​are both taken as the midpoint of the vertical direction between the two points, forming a horizontal line segment, as shown in the formula below:

[0064]

[0065] If the number of skeleton nodes is 3, then P1(x) p1 ,y p1 P2(x) p2 ,y p2 P3(x) p3 ,y p3 ), calculate Δx from the middle node to the two side nodes according to formula (4) k and Δy k .like The y-values ​​of the two points remain unchanged, and the x-values ​​are both taken as the midpoint of the horizontal direction between the two points, forming a line segment in the vertical direction; if The x-values ​​of the two points remain unchanged, while the y-values ​​are both taken as the midpoint of the vertical direction between the two points, forming a horizontal line segment. Combining these line segments creates a right-angled skeletal structure. The formula is summarized below, where P... a P b It represents any two adjacent points among P1, P2, and P3.

[0066]

[0067] Step 5, as follows Figure 6 , 78. Boundary growth based on skeleton: The boundary is grown based on the skeleton. The extracted skeleton is placed in a two-dimensional grid, and the skeleton segments maintain relative size and relative position relationship. For each skeleton segment, starting from the cell where it is initially located, it is grown and expanded in four directions: up, down, left, and right. The expanded cell represents the area covered by the skeleton segment. The above growth process is repeated until there are no blank areas between all blocks, and the reconstructed target map can be obtained.

[0068] Step 5.1: Place the extracted skeleton in a suitable 2D grid, keeping the relative position and size of each skeleton segment unchanged. Divide the skeleton segments into straight skeletons, right-angled skeletons, and hybrid skeletons.

[0069] Step 5.2: When the skeletal structure of a certain region consists of multiple straight-line skeletons or right-angle skeletons, first split the skeleton, dividing the right-angle skeleton into two line segment skeletons, to obtain a set of line segment skeletons R = {r1, r2, ..., r n}, and record the set of line segment skeletons D = {(r i1 ,r j1 ), (r i2 ,r j2 ),…,(r ik ,r jk Line segments from the same right-angled skeleton are marked with the same color.

[0070] Step 5.3: Expand the skeleton according to the skeleton growth rules: Each line segment skeleton grows in four directions (up, down, left, and right) according to the size of the cell on the grid. If a certain area has been occupied by other line segment skeletons, growth in that direction will stop.

[0071] Step 5.4, iterate through step 5.3 until there are no blank areas between any two grown blocks.

[0072] Step 5.5: Based on set D = {(r i1 ,r j1 ), (r i2 ,r j2 ),…,(r ik ,r jk The regions growing from the same right-angled skeleton are merged to obtain the reconstructed target map, which is formed by splicing together multiple rectangles and right-angled rectangles.

[0073] In step 5, to accurately obtain the target map, the following constraints are given:

[0074] (1) Ensure that the map skeleton line segments maintain their relative size in the two-dimensional grid;

[0075] (2) Ensure that the constructed blocks maintain their original relative positions to avoid information loss;

[0076] (3) Ensure that all blocks do not overlap;

[0077] (4) Ensure that there are no blank areas between the two blocks.

[0078] Step 6: After the above operations, each area on the original geographic map is converted into a block composed of rectangles or right-angled rectangles, and different blocks are filled with different colors for visual differentiation.

[0079] The embodiments described in this specification are merely examples of implementations of the inventive concept. The scope of protection of this invention should not be considered as limited to the specific forms stated in the embodiments. The scope of protection of this invention also extends to equivalent technical means that can be conceived by those skilled in the art based on the inventive concept.

Claims

1. A skeleton-based geographic map reconstruction method, characterized in that, Includes the following steps: Step 1: Extract geographic boundary data: Extract map boundary data; Step 2, Map Simplification: A simplified map is obtained using the Douglas-Peucker algorithm. The map boundary lines consist of a series of key points and line segments connecting them. Step 3, Map Skeleton Extraction: An angle bisector-based skeleton extraction algorithm is used to extract the map skeleton structure. The process is as follows: First, the boundary is obtained on the simplified map. For the set of pixels on the boundary, corner detection is used to identify the corners on the boundary. Corners are the turning points or intersections of the boundary. For each corner, the two adjacent corners are found, the angle bisector between them is calculated, and the line segments on the angle bisector are connected. The intersection of the line segments is the node of the skeleton, and the connected line segments represent the skeleton structure of the map. Step 4, Skeleton Regularization: Convert irregular skeletons into standard forms: Based on the extracted map skeleton structure, firstly, loop through and merge the two adjacent and closest nodes in the skeleton; then delete unnecessary leaf nodes; for the skeleton after deleting leaf nodes, loop through and merge the two adjacent and closest nodes again until the final skeleton structure has only two or three nodes left. Step 5: Boundary growth based on skeleton: The boundaries of the skeleton are grown and expanded. The extracted skeleton is placed in a two-dimensional grid, and the skeleton segments maintain relative size and relative position. For each skeleton segment, starting from the cell where it was initially located, it is grown and expanded in four directions: up, down, left, and right. The expanded cell represents the area occupied by the skeleton segment. The above growth process is repeated until there are no blank areas between all blocks, and the reconstructed target map is obtained. Step 6: After the above operations, each area on the original geographic map is converted into a block composed of rectangles or right-angled rectangles, and different blocks are filled with different colors for visual differentiation.

2. The skeleton-based geographic map reconstruction method according to claim 1, characterized in that, The skeleton regularization in step 4 specifically includes the following steps: Step 4.1: Loop through and merge the two closest adjacent nodes in the skeleton nodes: Traverse the linked list. From the list of skeleton nodes Obtain the corresponding node coordinates and calculate the coordinates of the two nodes. , Distance between If the distance between two connected nodes is the smallest among all connected nodes, then merge these two nodes into one node. And delete from the original list node. , New node The x and y coordinates are the midpoints of the original two node x and y coordinates, and the specific formula is: Iterate through the above process until the number of remaining nodes equals that of the original skeleton nodes. Update the node list for Link list for ; Step 4.2, Delete unnecessary leaf nodes: Traverse If a node has exactly one connected node, then that node is a leaf node, and the distance between that node and its connected nodes is recorded. Nodes are deleted in ascending order of leaf node distance until the number of remaining nodes equals the number of nodes before deletion. ; Step 4.3: Loop through and merge the two closest adjacent nodes in the skeleton nodes: Traverse ,from Obtain the corresponding node coordinates, calculate the distance between two nodes, and then merge the nodes according to formula (1); iterate the above process until the number of remaining nodes is less than or equal to Stop when needed; update the node list. for Link list for ; Step 4.4, Correcting the skeletal structure: If the number of remaining skeleton nodes is Calculate these two nodes ( ), of and ;like The y-values ​​of the two points remain unchanged. Take the midpoint of the two points in the horizontal direction to form a line segment in the vertical direction; if Two points The value remains unchanged. The values ​​are all taken as the midpoint of the vertical direction between the two points, forming a horizontal line segment, as shown in the formula below: If the number of skeleton nodes is , respectively ( ), , Calculate the distance from the middle node to the two side nodes. and ;like Two points The value remains unchanged. Take the midpoint of the two points in the horizontal direction to form a line segment in the vertical direction; if Two points The value remains unchanged. The values ​​are all taken as the midpoint of the two points in the vertical direction, forming a line segment in the horizontal direction; combining the line segments formed above, a right-angled skeleton structure is formed; the formula is summarized as follows, where , represent , , Any two adjacent points in the; 。 3. The skeleton-based geographic map reconstruction method according to claim 1, characterized in that, Step 5, boundary growth based on the skeleton, specifically includes the following steps: Step 5.1: Place the extracted skeleton in a suitable two-dimensional grid, keeping the relative position and size of each skeleton line segment unchanged; divide the skeleton line segments into straight skeletons, right-angle skeletons, and hybrid skeletons; Step 5.2: When the skeletal structure of a certain area consists of multiple straight-line skeletons or right-angle skeletons, first split the skeleton, breaking the right-angle skeleton into two line segment skeletons to obtain a set of line segment skeletons. And record the set of line segment skeletons obtained from the splitting. Line segments from the same right-angled skeleton are marked with the same color. Step 5.3: Expand the skeleton according to the skeleton growth rules: Each line segment skeleton grows in four directions (up, down, left, and right) according to the size of the cell on the grid. If a certain area has been occupied by other line segment skeletons, then growth in that direction will stop. Step 5.4, iterate through step 5.3 until there are no blank areas between any two grown blocks; Step 5.5, based on the set Regions growing from the same right-angled skeleton are merged to obtain the reconstructed target map, which is formed by stitching together multiple rectangles and right-angled rectangles.

4. The skeleton-based geographic map reconstruction method according to claim 1, characterized in that: In step 5, the following constraints are given in order to obtain the target map accurately: (1) Ensure that the skeleton line segments of the map maintain their relative size in the two-dimensional grid; (2) Ensure that the constructed blocks maintain their original relative positions to avoid information loss; (3) Ensure that all blocks do not overlap; (4) Ensure that there are no blank areas between the two blocks.

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