A potential street vector identification method for historical urban areas based on axis extraction

Abstract

The application provides a historical urban area potential street vector identification method based on axis extraction. The method comprises the following steps: step one, historical urban area map data acquisition and processing; step two, division of calculation units and preliminary evaluation; step three, street axis extraction based on a triangular mesh and parameter rules; and step four, street axis optimization and output. The application can set rules for the existing planning framework and street layout, explore potential street axis lines with traffic conditions in the historical urban area, and screen the generated results according to the parameter rules, thereby guiding the generated results. The application can provide quantitative data support for designers to optimize the street network structure without destroying the existing texture of the historical urban area.

Description

Technical Field

[0001] This invention relates to the fields of computer-aided design, geographic information systems, street patching technology, and urban renewal, specifically to a method for identifying potential street vectors in historical urban areas based on axis extraction. Background Technology

[0002] Urban development has shifted from primarily incremental expansion to mainly renewing existing infrastructure. Old urban areas generally suffer from low density and continuity of street and alleyway networks; therefore, improving the street and alleyway network system in old urban areas is one of the key tasks of urban renewal.

[0003] Historic districts are areas within a city that highly concentrate historical and cultural heritage and functions. Their street and alley systems reflect traditional urban fabric while simultaneously fulfilling residents' primary commuting needs. Due to factors such as the protection of historical and cultural elements, property boundaries, existing preserved blocks, and waterways, historic districts often suffer from uneven street and alley density, narrow road widths, and numerous dead-end streets. In past historic district redevelopment practices, designers have needed to conduct on-site surveys and data collection to understand the current state of the site and, relying on experience, identify potential streets and alleys with navigable conditions within the complex existing environment, ultimately optimizing the street and alley system. With the advancement of big data and artificial intelligence technologies, new methods and tools have been provided for identifying potential streets and alleys within historic districts. Existing street and alley axis identification technologies mainly include two methods based on vector data and image data.

[0004] Vector data-based methods rely on well-defined geometric and topological structures, enabling precise analysis and optimization. However, they are highly dependent on high-quality, multi-source site data and struggle to cope with situations where data is insufficient.

[0005] Image-based methods utilize remote sensing imagery and deep learning techniques, demonstrating excellent performance in complex backgrounds and large-scale scenes. However, the controllability and interpretability of the generated results are relatively weak, and the requirement for a large amount of labeled data increases the model training cost.

[0006] These methods still have limitations in optimizing existing complex historical urban streets and alleys.

[0007] To address the aforementioned technical problems, this invention proposes a vector identification method for potential streets and alleys in historical urban areas based on axis extraction. This invention establishes rules for existing planning frameworks and street and alley layouts, identifies potential street and alley axes within historical urban areas that meet traffic conditions, and filters them according to parameter rules to guide the generated results. Without disrupting the existing fabric of the historical urban area, it provides quantitative data support for designers to optimize the street and alley network structure.

[0008] To achieve the above-mentioned technical objectives, the present invention adopts the following technical solution:

[0009] A method for identifying potential street and alley vectors in historical urban areas based on axis extraction includes the following steps:

[0010] A. Obtain map data of historical districts and determine spatial element maps;

[0011] B. Preliminary division and assessment of the street and alley network map;

[0012] Based on the block partitioning theory, the spatial element map determined in step A is divided into independent computing units; according to the preset evaluation index, each computing unit is preliminarily evaluated, and computing units that meet the conditions for optimization are selected.

[0013] C. Based on parameter rules, street and alley axis generation is performed on the calculation units to be optimized selected in step B;

[0014] D. Visualize and evaluate the street and alley axes generated in step C.

[0015] Beneficial Effects: This invention addresses street and alley network identification in urban renewal. It primarily focuses on identifying potential streets and alleys with improved accessibility within existing historical urban areas, rather than regenerating a standardized street and alley network. A calculation and evaluation method based on multiple evaluation indicators is proposed, providing designers with quantitative data support and significantly improving the scientific rigor and operability of the street and alley network generation and optimization process. Through comprehensive evaluation of multiple indicators and the setting of pre-defined conditions, it not only accurately identifies computational units requiring optimization but also provides effective monitoring and evaluation methods for the actual performance of the street and alley network after renovation. Finally, the generated street and alley axes are visualized and interactively editable, allowing designers to adjust the street and alley axis scheme in real time and manually delete or add important axes, enhancing the presentation and interactivity of street and alley axis identification.

[0016] In one optional implementation, step B specifically includes the following sub-steps:

[0017] B1. The computational unit based on the block partitioning theory is used as the basic unit for street network generation and optimization. The computational unit has a set of spatial attributes specifically for street network generation and is divided and evaluated at the block scale. During the optimization process, the computational unit is regarded as the gene of the genetic algorithm, so that the street network generation can be carried out in accordance with the unified rule logic under the parallel computing framework.

[0018] B2. A calculation unit is defined as a plot of land formed by trunk road-trunk road, trunk road-branch road, and branch road-branch road closures. The current graphic data structure is obtained or updated from the street network map object. The calculation unit includes at least 3 nodes to form a closed plot. The graph structure is traversed by a depth-first search algorithm to identify closed plots for creating initial calculation units, or calculation units are selected by customization. The calculation unit is added to the street network map structure as the basic unit for optimization calculation.

[0019] B3. Conduct a preliminary assessment of the street network indicators for each calculation unit.

[0020] Beneficial Effects: This invention proposes a calculation and evaluation method based on multiple evaluation indicators, providing designers with quantitative data support and significantly improving the scientific rigor and operability of the street network generation and optimization process. Through comprehensive evaluation of multiple indicators and the setting of preset conditions, it can not only accurately identify the calculation units that need optimization, but also provide an effective means of monitoring and evaluating the actual performance of the street network after renovation.

[0021] In one optional implementation, in step B, the preset evaluation indicators include:

[0022] Street density, street ratio, and land parcel uniformity are among the criteria. The overall street density is 8.0 km / km², the street ratio is 0.5–0.7, and the land parcel uniformity is 1:2–2:3. If the indicators of a certain calculation unit do not meet the above preset conditions, it is determined that the calculation unit needs further optimization.

[0023] Beneficial effects: In step B, this invention achieves a comprehensive evaluation of the computational unit by pre-setting multiple quantitative evaluation indicators such as street density, street ratio, and land parcel uniformity. This method can accurately identify areas that do not meet the optimization standards during the overall street network generation process and provide quantitative data support, offering a scientific basis for designers' decisions.

[0024] In one optional implementation, step C, the generation of street and alleyway axes based on parameter rules, specifically includes the following sub-steps:

[0025] C1. Determine the current features based on existing map attributes and parameter rules, specifically including the following sub-steps:

[0026] C11. Street and alley level analysis: Analyze the street and alley levels, determine the standard width d of each type of street and alley, and offset the centerline of the existing street and alley outward by d / 2 to define the part of the non-street and alley area 1;

[0027] C12. Building Type Analysis: Analyze the coordinates and types of buildings in the map. Based on the building setback distance n1, the historical building setback distance n2, and the standard width d of streets and alleys, determine the outward offset distances of ordinary buildings and historical buildings as n1+d / 2 and n2+d / 2, respectively. Define the part of non-street and alley area 2 based on the expanded coordinates.

[0028] C13. Area Type Analysis: Analyze the coordinates and types of each special area in the map, define the part of non-street and alley area 3, special areas include water systems, green spaces, boundaries of closed communities, and property plots, etc., which cannot be used for street and alley patching; the sum of the non-street and alley area 1, non-street and alley area 2 and non-street and alley area 3, that is, all areas in the map except for streets and alleys and areas that cannot be modified, are determined as blank areas;

[0029] C2. The blank area is meshed using constrained Delaunay triangulation. The largest continuous blank area is selected as the basic convex polygon for generating the triangular mesh. The constraints and quality parameters of the triangular mesh are set, and the CDT process is executed to generate the triangular mesh.

[0030] C3 If a triangular mesh shares an edge with another triangular mesh, then that edge is defined as an adjacent edge. Connect the midpoints of the two adjacent edges and extend the line to the inner and outer boundaries of the convex polygon to obtain the preliminary street axis.

[0031] C4 uses steps C1 to C3 to extract all generated preliminary street and alley axes and caches the results for reuse and filtering.

[0032] C5. Construct a filter to filter the generated preliminary street and alley axes through parameter rules to ensure that the final street and alley skeleton structure meets the requirements of geometric logic and road design specifications;

[0033] C6. The simplified street and alley axis obtained after screening is subjected to intersection point extraction, splitting of street and alley axis, and overall simplification and deduplication. The street and alley axis can be connected with the existing street and alley network and is restricted to the calculation unit according to the parameter rules, summarizing the street and alley network topology structure without dead ends. Finally, the street and alley axis retains the information of individual streets and alleys as well as the overall graph structure, providing a data foundation for subsequent optimization calculations.

[0034] Beneficial effects: This invention ensures that the generated axes conform to spatial constraints by analyzing street and alley levels and region types; it optimizes the division of blank areas based on constrained Delaunay triangular meshes to improve computational stability; it combines parameter rules for screening to ensure that the street and alley skeleton structure conforms to geometric logic and road design specifications; and finally, through intersection point extraction, axis splitting, and topology optimization, it generates a complete street and alley network without dead ends, which seamlessly connects with the existing street and alley structure, providing a high-quality data foundation for subsequent optimization calculations.

[0035] In one optional implementation, step C5 simultaneously satisfies the following filtering rules:

[0036] Selection Rule 1. Street and alley length and shape: The Douglas-Puk algorithm compresses redundant path control points to obtain a simplified street and alley shape; the length ratio of streets and alleys is calculated. Given that the length of the street or alley between two points is D and the length of the line connecting the two points is d, the length ratio is r = D / d, which is between 1 and 1.5.

[0037] Filtering Rule 2. Street Angle: Determine whether the angle formed by the starting or ending point of a given path with the street meets the requirements. Specifically, calculate the angle between the vector from the starting point of the path to the next corner point and the normal vector of the side where the starting point is located. If these angles are between 45 degrees and 90 degrees, the angle is considered appropriate and meets the requirements; if these angles are less than 45 degrees, the angle is considered too small and does not meet the requirements. The same applies to the ending point of the path.

[0038] Filtering Rule 3. Intersection Analysis: Analyze the intersection type and determine the intersection threshold r according to road design specifications; define whether a street or alley can be generated at the location by expanding a circular area with a radius equal to the intersection threshold r outward from the existing arterial and branch road nodes. For street / alley level road network generation, r is set to 50m.

[0039] Beneficial effects: This invention optimizes street and alley networks through screening rules, simplifies street and alley morphology, ensures the rationality of path design, standardizes intersection generation, provides designers with scientific data support, and improves the efficiency and accuracy of street and alley network optimization.

[0040] In one optional implementation, step A specifically includes the following sub-steps:

[0041] A1. Obtain map data from open-source platforms, determine the street and alley identification range, perform layer classification on the selected historical block texture DXF file, extract the plot boundary, street and alley center line and building outline information, and convert it into Map file format;

[0042] A2. Use the NetTopologySuite library in the Unity engine to read the imported DXF file, classify the geometric data in it, and convert it into MAP file format;

[0043] A3. Define a class named Map, which contains multiple properties and collections for storing and managing different elements in the map. Each Map object includes the following: Map name: used to identify the specific map;

[0044] Road list: Includes roads at different levels, storing trunk roads, branch roads, and streets / lanes respectively;

[0045] Building list: includes ordinary buildings and historical buildings, marked as inaccessible areas;

[0046] Area list: Stores inaccessible enclosed areas and waterways.

[0047] Beneficial effects: This invention obtains map data from open-source platforms through big data, extracts key information, and performs data classification processing, solving the problem of large workload in traditional manual data collection methods and improving design efficiency.

[0048] In one alternative implementation, in step A2, primitive deduplication and line data conversion are performed before importing to ensure that subsequent calculations and analysis can proceed smoothly.

[0049] Beneficial effects: This invention uses the NetTopologySuite library in the Unity engine to read imported DXF files, classifies the geometric data in them, and converts them into MAP file format; primitive deduplication and line data conversion are performed before import to ensure that subsequent calculation and analysis processes can proceed smoothly.

[0050] In summary, this invention, by setting rules for existing planning frameworks and street layouts, identifies potential street axes with traversable conditions within historical urban areas and filters them according to parameter rules, guiding the generated results. Without disrupting the existing fabric of the historical urban area, it provides quantitative data support for designers to optimize the street network structure. Attached Figure Description

[0051] Figure 1 Flowchart of the method of this invention;

[0052] Figure 2 Ideal model - triangular mesh diagram;

[0053] Figure 3 Ideal Model - Street and Alley Axis Recognition Map;

[0054] Figure 4 Ideal model - Comparison chart of program-extracted results and manual design results;

[0055] Figure 5 Current situation model - triangular mesh diagram;

[0056] Figure 6 Current Status Model - Street and Lane Axis Identification Map;

[0057] Figure 7 Current Status Model - Comparison Chart of Program Extraction Results and Manual Design Results. Detailed Implementation

[0058] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific implementation examples, so that those skilled in the art can better understand the present invention and implement it. However, the embodiments described are not intended to limit the present invention.

[0059] This invention proposes a method for identifying potential street and alley vectors in historical urban areas based on axis extraction. The specific method flow is as follows: Figure 1 As shown, the process begins by acquiring urban street data, importing it into the Unity platform to create a spatial feature map, and then conducting a preliminary evaluation.

[0060] Next, street and alley axes are generated through triangular meshes. Based on existing conditions and planning requirements, such as street and alley width and building location, suitable street and alley axes are selected.

[0061] Finally, the final street axis scheme is visualized and displayed on the Unity platform, and the final street axis scheme is output. The specific steps include:

[0062] 1. Obtain relevant data from the city map, identify basic elements, and establish a street and alley network file.

[0063] 1.1. First, using the map import function, open-source platforms or existing geographic information data (such as DXF format files) are imported into the system, and the basic elements within the block are identified and classified. The main identified elements include: street and alley levels, building types, water systems, and enclosed areas. All classified data will be stored in the map management module and used as the basis for street and alley network generation and analysis.

[0064] 1.2. Using the NetTopologySuite library in the Unity engine, the imported DXF file is read, and overlay analysis and geometric data transformation are performed on each data layer, including street, building, and water system data, to generate a complete MAP file format street network map, which is then saved for future use. When opening the same map a second time, the "Load Map" option can be clicked.

[0065] 1.3. Detailed geometric information within the block can be viewed in Map Management - Basic Information, including: ① Map Name: Used to identify the specific map; ② Road List: Classified according to different street and alley levels, specifically including main roads, secondary roads, and alleys. Through the graphical interface, users can clearly see the geometric information of each street and alley and its relative position within the overall block; ③ Building List: Includes detailed information on ordinary buildings and historical buildings; ④ Area Information: Identifies waterways and other inaccessible enclosed areas. The location of basic elements on the map can be viewed by referring to the legend.

[0066] Preferably, in step 1.2, primitive deduplication and line data conversion are performed before importing to ensure that subsequent calculation and analysis processes can proceed smoothly.

[0067] 2. Create blocks by clicking on street network nodes, divide the street network units, and generate Delaunay triangular meshes.

[0068] 2.1. Click Map Management - Create Block to set up street network nodes. Each block unit is connected through these nodes. Users can divide the block area by clicking in the program interface and save it for future use.

[0069] 2.2. Click Create Navigation Block - Create All Calculation Units. The entire map is divided into independent calculation units based on block partitioning theory. Clicking on a calculation unit allows for preliminary evaluation.

[0070] 2.3. Click "Evaluate," select the evaluation indicators to be calculated, and the system will calculate the basic evaluation score for each block unit. The evaluation indicators include street connectivity, street flow, and the rationality of land parcel division. This evaluation score will be used as a reference standard in subsequent optimization stages.

[0071] 3. Generate basic street and alley axis lines and filter them according to parameter rules.

[0072] 3.1. Click "Create Triangular Grid." Based on each street and alley network unit within the block, and taking into account property boundaries, preserved buildings, existing streets and alleys, waterways, and other inaccessible areas, generate a triangular grid that conforms to the actual situation. The result will be as follows: Figure 2 The ideal model shown is a triangular mesh diagram. Using this triangular mesh, the system will automatically extract all possible preliminary street and alleyway axes.

[0073] 3.2 Based on the rigid rules of street and alley network in urban design, the program proposes corresponding geometric parameters to filter higher-quality street and alley axes, such as controlling the distance and angle between two axes, controlling the distance between the endpoints of the axes and existing streets and alleys, and merging similar axes. Users can manually modify the filtering parameters to generate more ideal axis results.

[0074] 3.3. Clicking the segmentation axis will segment the initially filtered streets and alleys and add them to the street and alley dataset to be optimized. This will display all possible filtered street and alley axes, as shown below. Figure 3 The ideal model shown is a street axis identification diagram.

[0075] As a further preferred embodiment of the technical solution of the present invention, step C5 simultaneously satisfies the following screening rules:

[0076] Selection Rule 1. Street and alley length and shape: The Douglas-Puk algorithm compresses redundant path control points to obtain a simplified street and alley shape; the length ratio of streets and alleys is calculated. Given that the length of the street or alley between two points is D and the length of the line connecting the two points is d, the length ratio is r = D / d, which is between 1 and 1.5.

[0077] Filtering Rule 2. Street Angle: Determine whether the angle formed by the starting or ending point of a given path with the street meets the requirements. Specifically, calculate the angle between the vector from the starting point of the path to the next corner point and the normal vector of the side where the starting point is located. If these angles are between 45 degrees and 90 degrees, the angle is considered appropriate and meets the requirements; if these angles are less than 45 degrees, the angle is considered too small and does not meet the requirements. The same applies to the ending point of the path.

[0078] Filtering Rule 3. Intersection Analysis: Analyze the intersection type and determine the intersection threshold r according to road design specifications; define whether a street or alley can be generated at the location by expanding a circular area with a radius equal to the intersection threshold r outward from the existing arterial and branch road nodes. For street / alley level road network generation, r is set to 50m.

[0079] 4. Visualize and optimize the initially selected street and alley axes within the Unity engine.

[0080] 4.1 Imported street and alley axes can be interactively viewed and edited, allowing for real-time optimization of street and alley axes.

[0081] 4.2 After completing the optimization process, the system will output the final optimized potential street and alleyway axes. The final street and alleyway axis results can be exported in DXF format for use in actual urban planning and architectural design. Figure 4 The image on the left shows the final result of the program extraction. Figure 4 The image on the right shows the result of the manual design. The program extraction method retains the control of the overall structure by the manual design and divides the plots into regular patterns. At the same time, it has stronger adaptability at the detail level, and can automatically discover and retain small-scale street and alley forms, making it suitable for micro-scale optimization of complex blocks.

[0082] The following is combined with Figures 5-7 Taking a historical district in He County, Ma'anshan City, Anhui Province as an example, this example further illustrates the method used in this case study:

[0083] 1. Obtain relevant city map data, identify basic elements, and create a street and alley network file. This area is functionally defined as a historical district. Import the vector data of the prototype plan from the reference case into the AutoCAD software platform, classify the data according to layers, convert it into polylines, and store it in DXF format. Import the DXF file into the Unity platform, identify street and alley levels and current features, and create a street and alley network MAP file.

[0084] 2. By clicking on street and alley network nodes, a block is created, and the street and alley network units are divided to generate a Delaunay triangular mesh. The area is divided into plots based on main road-main road, main road-local road, and local road-local road connections. Click "Create Block," select the evaluation indicators to be calculated, and finally, 11 calculation units, i.e., 11 plots, are formed. Figure 5 The current situation model shown is a triangular mesh diagram.

[0085] 3. Generate basic street and alleyway axes and perform preliminary filtering based on parameter rules. Street and alleyway axes are generated based on geometric filtering factors and default filtering parameters, and the filtered streets and alleyways for all plots are segmented. The result is as follows: Figure 6 The current situation model shown is a street axis identification map.

[0086] 4. The system visualizes the initially selected street and alleyway axes within the Unity engine, allowing for interactive viewing and editing of the imported axes for real-time optimization. After optimization, the system outputs the final optimized potential street and alleyway axes, which can be exported in DXF format for use in urban planning and architectural design.

[0087] Figure 7 The image shown is a comparison chart between the final results extracted by the program and the results designed manually.

Claims

1. A method for identifying potential street and alley vectors in historical urban areas based on axis extraction, characterized in that, Includes the following steps: A. Obtain map data of historical districts and determine spatial element maps; B. Preliminary division and evaluation of the street and alley network map; based on the block partitioning theory, the spatial element map determined in step A is divided into independent calculation units; according to the preset evaluation indicators, each calculation unit is preliminarily evaluated, and calculation units that meet the conditions for optimization are selected. C. Based on parameter rules, street and alley axis generation is performed on the calculation units to be optimized selected in step B; D. Visualize and evaluate the street and alley axis lines generated in step C; Step C, the generation of street and alleyway axes based on parameter rules, specifically includes the following sub-steps: C1. Determine the existing features based on the existing map attributes and parameter rules, specifically including the following sub-steps: C11. Street and alley level analysis: Analyze the street and alley levels, determine the standard width d1 of each type of street and alley, and offset the centerline of the existing streets and alleys outward by d1 / 2 to define the part of non-street and alley area 1; C12. Building type analysis: Analyze the coordinates and types of buildings in the map, and determine the outward offset distance of ordinary buildings and historical buildings based on the building setback distance n1, the historical building setback distance n2, and the standard width d1 of the streets and alleys. n1+d1 / 2 and n2+d1 / 2 are respectively used to define the part of non-street and alley area 2 based on the expanded coordinates; C13. Area type analysis: Analyze the coordinates and types of each special area in the map, and define the part of non-street and alley area 3. Special areas include water systems, green spaces, boundaries of closed communities, and areas of property plots that cannot be used for street and alley patching; The sum of the non-street and alley area 1, non-street and alley area 2, and non-street and alley area 3, that is, all areas in the map area except for streets and alleys and areas that cannot be modified, are determined as blank areas; C2. The blank area is meshed using constrained Delaunay triangulation. The largest continuous blank area is selected as the basic convex polygon for generating the triangular mesh. The constraints and quality parameters of the triangular mesh are set, and the CDT process is executed to generate the triangular mesh. C3 If a triangular mesh shares an edge with another triangular mesh, then that edge is defined as an adjacent edge. Connect the midpoints of the two adjacent edges and extend the line to the inner and outer boundaries of the convex polygon to obtain the preliminary street axis. C4 uses steps C1 to C3 to extract all generated preliminary street and alley axes and caches the results for reuse and filtering. C5. Construct a filter to filter the generated preliminary street and alley axes through parameter rules to ensure that the final street and alley skeleton structure meets the requirements of geometric logic and road design specifications; C6. The simplified street and alley axis obtained after screening is subjected to intersection point extraction, splitting of street and alley axis, and overall simplification and deduplication. The street and alley axis can be connected with the existing street and alley network and is restricted to the calculation unit according to the parameter rules, summarizing the street and alley network topology structure without dead ends. Finally, the street and alley axis retains the information of individual streets and alleys as well as the overall graph structure, providing a data foundation for subsequent optimization calculations.

2. The method for identifying potential street and alley vectors in historical urban areas based on axis extraction according to claim 1, characterized in that, Step B specifically includes the following sub-steps: B1. Using a computational unit defined based on block partitioning theory as the basic unit for street network generation and optimization; the computational unit has a set of spatial attributes specifically for street network generation, and is divided and evaluated at the block scale; in the subsequent optimization process, the computational unit enables street network generation to be carried out in a parallel computing framework according to a unified rule logic; B2. A calculation unit is defined as a plot of land closed by main and secondary roads. The current graphic data structure is obtained or updated from the street network map object. The calculation unit includes at least 3 nodes to form a closed plot. The graph structure is traversed by a depth-first search algorithm to identify closed plots for creating initial calculation units, or calculation units are selected by customization. The calculation unit is added to the street network map structure as the basic unit for optimization calculation. B3. Conduct a preliminary assessment of the street network indicators for each calculation unit.

3. The method for identifying potential street and alley vectors in historical urban areas based on axis extraction according to claim 1, characterized in that, In step B, the preset evaluation indicators include: street density, street ratio, and land parcel uniformity. The preset conditions are: global street density reaches 8.0 km / km², street ratio is 0.5 to 0.7, and land parcel uniformity is 1:2 to 2:

3. When the indicators of a certain calculation unit do not meet the above preset conditions, it is determined that the calculation unit needs further optimization.

4. The method for identifying potential street and alley vectors in historical urban areas based on axis extraction according to claim 1, characterized in that, In step C5, the following filtering rules are simultaneously satisfied: Filtering rule 1. Street length and shape: The Douglas-Puk algorithm compresses redundant path control points to obtain simplified street shapes; the street length ratio is calculated. Given that the street length between two points is D and the length of the line connecting the two points is d2, the length ratio is r1 = D / d2, which is between 1 and 1.5; Filtering rule 2. Street angle: Determine whether the angle formed by the starting or ending point of the given path with the street meets the requirements. Specifically, calculate the angle from the starting point to the end point of the street. The angle between the vector of a corner point and the normal vector of the edge where the starting point is located is considered appropriate if the angle is between 45 and 90 degrees; otherwise, it is considered too small and does not meet the requirements. Selection rule three: Intersection analysis: Analyze the intersection type and determine the intersection threshold r2 according to road design specifications; define whether streets and alleys can be generated by expanding a circular area with a radius of the intersection threshold r2 outward from the existing arterial and branch road nodes. For street and alley level road network generation, r2 is set to 50m.

5. The method for identifying potential street and alley vectors in historical urban areas based on axis extraction according to claim 1, characterized in that, Step A specifically includes the following sub-steps: A1. Obtain map data from open-source platforms, determine the street and alley identification range, perform layer classification on the selected historical block texture DXF file, extract the plot boundary, street and alley center line and building outline information, and convert it into Map file format; A2. Use the NetTopologySuite library in the Unity engine to read the imported DXF file, classify the geometric data in it, and convert it into MAP file format; A3. Define a class named Map, which contains multiple attributes and collections for storing and managing different elements in the map. Each Map object includes the following: Map name: used to identify the specific map; Road list: includes roads at different levels, storing trunk roads, branch roads, and streets respectively; Building list: includes ordinary buildings and historical buildings, marked as inaccessible areas; Area list: Stores inaccessible enclosed areas and waterways.

6. The method for identifying potential street and alley vectors in historical urban areas based on axis extraction according to claim 5, characterized in that, In step A2, duplicate elements and line data conversion are performed before importing.

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