Bridge identification method and device based on spatial analysis, equipment and storage medium

Abstract

The present application relates to the technical field of spatial data analysis, and particularly relates to a bridge identification method and device based on spatial analysis, equipment and a storage medium, the present application obtains the existing vector data of roads and water areas, first, topological connectivity of water area elements is processed to construct a complete drainage network, then the spatial intersection of roads and the drainage network is calculated, and potential bridge areas are automatically identified; subsequently, the topological connection relationship between each potential area and the surrounding water area is analyzed to quantify the connection characteristics, and the final bridge element is screened and confirmed according to the characteristics. The present application makes full use of existing geographic spatial data, realizes the automatic and batch identification of bridge elements through vector spatial relationship analysis, effectively improves the accuracy and processing efficiency of extracting specific infrastructure from complex geographic environment, and provides reliable support for the automatic update of map data.

Description

Technical Field

[0001] This invention relates to the field of spatial data analysis technology, and in particular to a bridge identification method, apparatus, equipment and storage medium based on spatial analysis. Background Technology

[0002] Currently, with the in-depth advancement of technologies such as smart cities, unprecedented demands are being placed on the accuracy and precision of basic geographic information. High-precision, high-freshness map data is a key foundation for achieving refined urban management, intelligent traffic navigation, and emergency decision support.

[0003] Among the various elements of map data, bridges, as vital infrastructure connecting transportation networks and bridging natural obstacles, require highly accurate and complete information. Traditional methods for bridge element identification and map updating heavily rely on field surveys or manual interpretation of high-resolution remote sensing imagery. This approach is not only time-consuming, labor-intensive, and costly, but also struggles to meet the demands for rapid acquisition and regular updates of bridge information over large areas. Consequently, bridge information in map databases often lags behind reality, hindering accurate batch and automated processing and updates, thus becoming a bottleneck restricting the timeliness of map data. Although existing technologies attempt to utilize existing basic vector data such as roads and waterways for spatial analysis to aid identification, the lack of in-depth understanding and effective utilization of the complex topological relationships between geographic entities often leads to insufficient accuracy in identification results, with frequent misjudgments and omissions.

[0004] Therefore, how to automatically and accurately identify bridge elements based on existing road and waterway vector data is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] The main objective of this invention is to provide a bridge identification method, apparatus, device, and storage medium based on spatial analysis, aiming to solve the technical problem of how to automatically and accurately identify bridge elements based on existing road and waterway vector data in the prior art.

[0006] To achieve the above objectives, the present invention provides a bridge identification method based on spatial analysis, the method comprising the following steps: Acquire road surface vector data and water surface vector data for the target area; Based on the water surface vector data of the target area, obtain the connected water surface data; Based on the spatial overlay analysis of the connected water surface data and road surface vector data, candidate bridge surface data are obtained. Based on the water surface vector data, calculate the number of water surface connections in the candidate bridge surface data; The bridge deck data is determined based on the number of waterway connections of the candidate bridge decks.

[0007] Optionally, obtaining connected water surface data based on the water surface vector data of the target area includes: The water surface vector data is subjected to outward expansion buffering to obtain expanded water surface data; The expanded water surface data is subjected to adjacent patch fusion and in-surface hole filling processing to obtain expanded continuous water surface data; The expanded continuous water surface data is subjected to inward shrinkage buffering along the graphic boundary to obtain the connected water surface data.

[0008] Optionally, the buffer distance of the outward expansion buffering process is equal to the buffer distance of the inward contraction buffering process; The in-plane void filling process includes identifying planar voids in the expanded water surface data and filling voids with an area less than or equal to a preset threshold. The adjacent patch fusion process includes fusing patches generated after the outward expansion buffering process where the distance between adjacent patches is less than a preset threshold.

[0009] Optionally, the step of performing spatial overlay analysis based on the connected water surface data and road surface vector data to obtain candidate bridge surface data includes: By performing layer intersection calculations on the road surface vector data and the connected water surface data, road surface data covering the water area is obtained; Based on the road surface data covering the water area, the road centerline data is obtained; Based on the road centerline data and the preset bridge width, double-sided buffering is performed to obtain initial candidate bridge deck data; The overlapping area between the initial candidate bridge deck data and the connected water surface data is clipped to obtain the candidate bridge deck data.

[0010] Optionally, the step of performing double-sided buffering based on the road centerline data and the preset bridge width to obtain initial candidate bridge deck data includes: The single-sided distance extending to both sides of the centerline is determined based on the preset bridge width; Based on the road centerline data and the single-sided distance, generate double-sided boundary lines parallel to the centerline; The initial candidate bridge surface data is obtained by constructing a closed polygon surface based on the road centerline data and the two side boundary lines.

[0011] Optionally, calculating the number of waterway connections in the candidate bridge deck data based on the water surface vector data includes: Spatial intersection analysis is performed between the candidate bridge surface data and the connected water surface data to identify all independent water areas that are adjacent to the candidate bridge surface. Based on the results of the spatial intersection analysis, the independent water areas adjacent to the same candidate bridge surface are distinguished and counted to obtain the number of independent water areas adjacent to each candidate bridge surface. The number of independent water areas adjacent to each candidate bridge surface is associated with the corresponding candidate bridge surface attribute to obtain the number of water area connections in the candidate bridge surface data.

[0012] Optionally, the bridge identification method based on spatial analysis further includes: Obtain the background map data of the target area; Register the geographic coordinates of the bridge deck data with the coordinate system of the background map data; The registered bridge surface data is overlaid and rendered on the background map in a preset visualization style to generate a distribution map containing the geographical locations of the bridges.

[0013] Furthermore, to achieve the above objectives, the present invention also proposes a bridge identification device based on spatial analysis, the bridge identification device based on spatial analysis comprising: The data acquisition module is used to acquire road surface vector data and water surface vector data of the target area; The data preprocessing module is used to obtain connected water surface data based on the water surface vector data of the target area; The data preprocessing module is also used to perform spatial overlay analysis based on the connected water surface data and road surface vector data to obtain candidate bridge surface data; The bridge determination module is used to calculate the number of waterway connections in the candidate bridge surface data based on the water surface vector data. The bridge determination module is also used to determine bridge surface data based on the number of waterway connections of the candidate bridge surfaces.

[0014] Furthermore, to achieve the above objectives, the present invention also proposes a bridge identification device based on spatial analysis, the bridge identification device based on spatial analysis comprising: a memory, a processor, and a bridge identification program based on spatial analysis stored in the memory and executable on the processor, the bridge identification program based on spatial analysis being configured to implement the steps of the bridge identification method based on spatial analysis as described above.

[0015] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing a bridge identification program based on spatial analysis, wherein the bridge identification program based on spatial analysis, when executed by a processor, implements the steps of the bridge identification method based on spatial analysis as described above.

[0016] The present application proposes one or more technical solutions, which have at least the following technical effects: This application acquires existing vector data of roads and waterways, first performs topological connectivity processing on waterway elements to construct a complete watershed network, then calculates the spatial intersection of roads and the watershed network to automatically identify potential bridge areas; subsequently, it quantifies the connection characteristics by analyzing the topological connections between each potential area and its surrounding waterways, and uses these characteristics to screen and confirm the final bridge elements. This solution fully utilizes existing geospatial data and, through vector spatial relationship analysis, achieves automated and batch identification of bridge elements, effectively improving the accuracy and processing efficiency of extracting specific infrastructure from complex geographical environments, and providing reliable support for the automated updating of map data. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the first embodiment of the bridge identification method based on spatial analysis of the present invention. Figure 2 This is a flowchart illustrating the second embodiment of the bridge identification method based on spatial analysis of the present invention. Figure 3 This is a structural block diagram of the first embodiment of the bridge identification device based on spatial analysis of the present invention; Figure 4 This is a schematic diagram of the structure of a bridge identification device based on spatial analysis in the hardware operating environment of the embodiment of the present invention.

[0020] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] The main solution of this application embodiment is as follows: acquire road surface vector data and water surface vector data of the target area; obtain connected water surface data based on the water surface vector data of the target area; perform spatial overlay analysis on the connected water surface data and road surface vector data to obtain candidate bridge surface data; calculate the number of water connections of the candidate bridge surface data based on the water surface vector data; and determine the bridge surface data based on the number of water connections of the candidate bridge surface.

[0024] Currently, traditional methods for bridge feature identification and map updating heavily rely on field surveys or manual interpretation of high-resolution remote sensing imagery. This approach is not only time-consuming, labor-intensive, and costly, but also struggles to meet the demands for rapid acquisition and regular updates of bridge information over large areas. Consequently, bridge information in map databases often lags behind reality, hindering accurate batch and automated processing and updates, thus becoming a bottleneck restricting the timeliness of map data. Therefore, how to automatically and accurately identify bridge features based on existing road and waterway vector data is a pressing technical problem that needs to be solved.

[0025] This application acquires existing vector data of roads and waterways, first performing topological connectivity processing on waterway elements to construct a complete watershed network, then calculating the spatial intersection of roads and the watershed network to automatically identify potential bridge areas. Subsequently, it quantifies the connectivity characteristics of each potential area by analyzing the topological connections between each potential area and its surrounding waterways, and uses these characteristics to screen and confirm the final bridge elements. This solution fully utilizes existing geospatial data and achieves automated and batch identification of bridge elements through vector spatial relationship analysis, effectively improving the accuracy and processing efficiency of extracting specific infrastructure from complex geographical environments, and providing reliable support for the automated updating of map data.

[0026] It should be noted that the executing entity of this invention can be a bridge identification device based on spatial analysis, or a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or a thermal management device capable of realizing the above functions for a bridge identification device based on spatial analysis. This embodiment does not specifically limit it in this way. The following uses a bridge identification device based on spatial analysis as the executing entity as an example to describe this embodiment and the following embodiments.

[0027] Based on this, embodiments of this application provide a bridge identification method based on spatial analysis, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the bridge identification method based on spatial analysis of this application.

[0028] In this embodiment, the bridge identification method based on spatial analysis includes steps S10 to S50: Step S10: Obtain road surface vector data and water surface vector data for the target area.

[0029] It should be noted that road surface vector data here specifically refers to geographic information data representing the actual coverage area of ​​a road as a closed polygon geometry, such as the road surface outline of an urban arterial road or the projected area of ​​an overpass. This type of data clearly reflects the physical width and spatial location of the road. Water surface vector data, on the other hand, refers to data depicting the boundaries of water bodies such as rivers, lakes, and ponds in the same planar feature form, such as the complete area enclosed by a river from its left bank to its right bank. The subsequent bridge identification in this invention relies on analyzing whether there is spatial overlap or adjacency between road surfaces and water surfaces; only planar data can fully convey this type of spatial interaction information.

[0030] Understandably, the focus of this step is to acquire areal information that can characterize the true planar distribution of ground features. This data can originate from various sources, such as publicly available national geographic census databases, standardized data products provided by commercial navigation map companies, or results generated through manual or semi-automatic interpretation of high-resolution remote sensing satellite imagery. The specific file format of the data is not limited by this invention; any general or proprietary format that can be read and parsed by a geographic information system to extract polygonal features and their spatial coordinates is applicable.

[0031] Step S20: Obtain connected water surface data based on the water surface vector data of the target area.

[0032] It's important to note that raw water surface vector data often contains numerous independent and dispersed surface features. For example, a large lake might be represented as a single polygon, while a meandering river might be divided into surface units representing multiple consecutive river segments. Connected water surface data, on the other hand, refers to data generated after specific spatial analysis and processing that fully reflects the natural connectivity of water bodies. Specifically, by identifying and merging adjacent or interconnected water surfaces, physically unified water bodies are integrated into a single logical unit. For instance, a main river and its associated lakes and reservoirs, as long as their surfaces are actually connected, will be merged into a complete connected water surface. This ensures that subsequent analysis of road bridges crossing waterways can be performed from the perspective of the overall water system structure, avoiding misjudgments caused by artificial data segmentation.

[0033] In one embodiment, obtaining connected water surface data based on the water surface vector data of the target area includes: performing outward expansion buffering processing on the water surface vector data to obtain expanded water surface data; performing adjacent patch fusion and in-plane hole filling processing on the expanded water surface data to obtain expanded continuous water surface data; and performing inward contraction buffering processing on the expanded continuous water surface data along the graphic boundary to obtain the connected water surface data.

[0034] It should be noted that outward expansion buffering of water surface vector data refers to extending the boundary line of each water polygon patch outward by a small and fixed distance, thereby slightly increasing its coverage area. The direct purpose of this operation is to bridge the subtle gaps between adjacent water patches caused by errors in the original data acquisition accuracy, cartographic generalization, or coordinates. For example, the main channel of a river and the inlet of a tributary may have a millimeter-level disconnect in the data due to digitization deviations; by outward buffering, these two parts of the water can be spatially connected.

[0035] Understandably, performing adjacent patch fusion and in-plane hole filling on the expanded water surface data is crucial for generating continuous water surfaces. The fusion process involves merging independent water patches that have been buffered and are in contact or overlapping into a single, unified polygon, thus creating a geometrically coherent whole. Simultaneously, filling in-plane holes involves transforming island-like non-water areas within the water surface (e.g., an island in a lake might be represented as a void within the water surface) into part of the water surface. This step ensures that all physically connected water bodies, regardless of their internal structure, are integrated into a complete single-surface feature at the data level.

[0036] It should be understood that the final step involves an inward contraction buffering process on the generated continuous water surface data, with the contraction distance being strictly equal to the initial outward expansion distance. This step aims to restore the boundaries of the water surface to their original, more realistic positions, thereby eliminating the geometric deformation introduced by the first expansion step. The core idea of ​​the entire process is to prioritize resolving spatial connectivity issues caused by data quality problems at the cost of controllable and reversible geometric deformation, and ultimately restore the accurate contours of ground features through contraction callbacks. This method can robustly construct a connected water surface dataset that both conforms to hydrological connectivity realities and maintains high geometric accuracy, providing a reliable foundation for subsequent analysis.

[0037] In a preferred embodiment, the buffer distance of the outward expansion buffering process is equal to the buffer distance of the inward contraction buffering process; the in-plane void filling process refers to identifying planar voids in the expanded water surface data and filling voids with an area less than or equal to a preset threshold; the adjacent patch fusion process refers to fusing patches generated after the outward expansion buffering process where the distance between adjacent patches is less than a preset critical value.

[0038] Understandably, the purpose of generating connected water surface data is to accurately characterize the natural geographic attributes of water systems, since bridges typically span entire water bodies rather than local fragments. This process primarily relies on calculating the spatial topological relationships between water surface features, such as determining whether two features share a common boundary or are in very close proximity. In practice, this can be achieved using standard spatial processing tools in Geographic Information System (GIS) software, such as merging adjacent features or combining them based on a preset small distance tolerance.

[0039] It should be understood that this step is a crucial preprocessing step for improving the accuracy of bridge identification. Without this type of connectivity processing, a road that crosses different segments of the same river may be misidentified as crossing multiple independent small bodies of water, resulting in incorrect identification results. Therefore, reshaping water area data into connected water surfaces can effectively eliminate the influence of human segmentation caused by data collection or mapping standards, ensuring that subsequent analysis is based on real and complete geographical entities.

[0040] Step S30: Perform spatial overlay analysis based on the connected water surface data and road surface vector data to obtain candidate bridge surface data.

[0041] It should be noted that the spatial overlay analysis here specifically refers to processing the water surface data representing a complete connected water body and the original road surface vector data using the clipping analysis operator. The principle of the clipping analysis operator is to retain the overlapping parts of the geometric shapes in the two layers. Specifically, it extracts all road patches in the road surface data that intersect with the connected water surface. For example, a highway crossing a river will inevitably have its road surface intersecting with the water surface polygon representing that river; this intersecting road area will be clipped out as a preliminary result. This step is an initial screening based on the fundamental spatial characteristic that bridges inevitably cross water bodies.

[0042] Understandably, the preliminary results may contain interfering elements that are not actually bridges, such as small patches formed by the slight brushing of roads against the water's edge, or road sections that run along the riverbank and partially encroach on the water. Therefore, after obtaining the overlapping portions, the process immediately applies a culling analysis operator to remove small fragments with areas smaller than a certain threshold, thus filtering out noise caused by inaccurate data boundaries or atypical overlaps. Subsequently, a stitching analysis operator is used to merge spatially adjacent remaining road patches into complete segments. For example, a long bridge might be segmented into multiple consecutive patches in the original data; the stitching operation can integrate them into a complete candidate bridge surface, thus more accurately reflecting the geometry of the actual bridge.

[0043] It should be understood that the candidate bridge deck data is a high-quality candidate set processed through a series of "pruning-elimination-stitching" operations. This process effectively focuses the analysis scope from the entire road network to a limited number of candidate targets with spatial bridge characteristics. This lays a solid foundation for subsequent, more refined discrimination processes, ensuring that subsequent analysis can be carried out efficiently on a highly relevant subset of data.

[0044] Step S40: Based on the water surface vector data, calculate the number of water surface connections in the candidate bridge surface data.

[0045] It should be noted that the core of this step lies in further identifying the true bridge target by analyzing the topological relationship between the candidate bridge surface and the original water area. Specifically, the water surface vector data here refers to the original water area patch layer without connectivity processing, and the number of water area connections refers to how many independent water area patches are spatially connected to a specific candidate bridge surface. This step first performs a small-scale buffer analysis on the candidate bridge surface, slightly expanding its boundary to ensure that its contact relationship with the surrounding water area can be stably captured, and then performs spatial connection analysis with the stitched original water area surface.

[0046] Understandably, the primary purpose of calculating the number of water body connections is to distinguish genuine bridges from other potential targets. For example, a standard bridge spanning a single river connects both banks, but since the river is typically treated as a single, independent feature in the raw data, this bridge has only one water body connection. Conversely, if a road is built along the edge of a lake or merely parallel to and partially overlaps with a riverbank, its buffered surface may only have long-side contact or enclosure with the water body. While its connection number may also be one, its spatial pattern is fundamentally different from the "spanning" pattern of a bridge and can be excluded using other rules. A highway bridge built in a complex estuary or traversing a dense network of ponds will continuously span multiple independent water bodies, resulting in a greater than one water body connection.

[0047] It should be understood that screening based on the number of waterway connections can effectively capture the unique spatial interaction patterns between bridges and water bodies. By setting an appropriate threshold for the number of connections, candidate targets that are more likely to truly reflect the bridge's spanning characteristics can be retained.

[0048] Step S50: Determine the bridge deck data based on the number of waterway connections of the candidate bridge decks.

[0049] It should be noted that a specific numerical threshold is set to distinguish between bridges and non-bridges. For example, the rule can be set to retain all candidate surfaces with more than one water body connection. This means that the key criterion for determining a bridge is whether it spans multiple independent water body units, or whether its spatial connection pattern with the water body meets the definition of "effective crossing".

[0050] Understandably, the core concept of this scheme lies in the fundamental function of a bridge: connecting land separated by water. Therefore, for a road segment to qualify as a bridge, it must be situated beneath a continuous body of water, and its two ends must lie on different land parcels. Based on this, this scheme actively repairs water areas fragmented by road features, constructing a "continuous" water surface to accurately capture all road segments that "cross water." However, to avoid misclassifying roads "following the water" as bridges, an additional check is applied: verifying whether the road segment is adjacent to two or more original, independent water units. This topological condition perfectly aligns with the definition of a bridge—it must be a spatial "connector" rather than an "adjacent neighbor," thus achieving high-precision identification achievable solely through vector space analysis.

[0051] In one embodiment, the method further includes the step of visualizing the analysis results: acquiring background map data of the target area; registering the geographic coordinates of the bridge surface data with the coordinate system of the background map data; and overlaying the registered bridge surface data onto the background map in a preset visualization style to generate a distribution map containing the geographical locations of the bridges.

[0052] Understandably, this series of visualizations aims to transform abstract geometric data into intuitive and easy-to-understand map language. Bridge surface data itself is a vector surface containing precise boundaries and location information, but it lacks geographical context, making direct interpretation quite difficult. By precisely overlaying it onto a background map containing elements such as roads, waterways, and settlements, users can immediately see the specific environment of each bridge, such as which river it crosses, which roads it connects, and which village or town it is near. This visualization significantly improves the efficiency of data analysis and the intuitiveness of decision support. Such distribution maps can provide crucial graphical evidence for regional transportation planning, infrastructure surveys, and emergency rescue route analysis, clearly revealing the spatial patterns of bridges hidden within complex data, thereby supporting deeper insights and decision-making.

[0053] This embodiment acquires the planar vector data of roads and water bodies, and performs connectivity processing on the water body data to construct complete water body units. Then, through spatial overlay analysis, road surfaces intersecting with water bodies are selected as candidate targets. Furthermore, by calculating the number of connections between each candidate target and the original independent water body patch, and setting rules based on this number for discrimination, the actual bridge surface data is finally determined.

[0054] In summary, this technical solution avoids the image processing challenges faced by high-resolution remote sensing image data, such as cloud cover and tree shading, by using only vector data of water areas and roads. The entire process utilizes vector operations, resulting in higher speed and efficiency when extracting bridges over large areas. The extraction method of this invention can quickly identify bridges with high accuracy. Compared to computer vision-based methods that require significant computational resources and time, this method is faster and effectively saves computational resources. Furthermore, it has stronger generalization capabilities, eliminating the need for repeated modeling; the same model can be applied to a large area.

[0055] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 The bridge identification method based on spatial analysis, wherein the step of performing spatial overlay analysis based on the connected water surface data and road surface vector data to obtain candidate bridge surface data includes steps S301~S304: Step S301: Perform layer intersection calculation based on the road surface vector data and the connected water surface data to obtain road surface data covering the water area.

[0056] It's important to note that layer intersection calculation refers to overlaying two layers representing different geographic features (specifically, a road surface vector data layer and a connected water surface data layer), and outputting only the spatially overlapping geometric portions of the two layers. The road surface data covering the water surface refers to the new set of areal features obtained after this calculation, whose geometric characteristics require them to simultaneously belong to the original road surface and be located within the connected water surface. For example, the road surface of a highway crossing a river will inevitably fall within the connected water surface polygon representing the river, and this section of the road surface will be extracted by the intersection calculation; conversely, roads or water bodies far from the water body themselves will not be included in this result.

[0057] Understandably, this step significantly narrows the scope of analysis from all roads in the entire target area to those road segments that have direct spatial contact with water systems. This provides a highly relevant candidate dataset for the subsequent more refined identification process, avoiding the waste of resources and interference caused by performing calculations on the full dataset.

[0058] Step S302: Obtain road centerline data based on the road surface data covering the water area.

[0059] It's important to note that the core task of this step is to transform the planar road features into their linear skeleton, namely, road centerline data. Road centerline data refers to one or more central axes extracted from the planar road polygon using a specific algorithm, representing the main direction and connecting paths of the road. For example, for a long, narrow planar feature representing a straight highway, its centerline is typically a straight line located in the center of the planar feature and parallel to its boundary; while for a complex planar structure representing a roundabout or interchange, a ring-shaped or network-like centerline may be extracted.

[0060] Understandably, the essential function of a bridge in space is to provide a linear passage across obstacles, and its centerline best embodies this core characteristic. Converting irregular or varying width surfaces into standardized lines helps eliminate interference from uneven road boundaries or inconsistent widths in the original data.

[0061] Step S303: Perform double-sided buffering based on the road centerline data and the preset bridge width to obtain initial candidate bridge deck data.

[0062] It should be noted that the double-sided buffering process refers to extending an equal distance to both sides of the road centerline obtained in the preceding steps, using this centerline as a baseline. This distance is determined by the "preset bridge width," which is typically half the width of a bridge pavement. The result of this process is to expand a line into a strip-shaped surface with a uniform width; this surface becomes the initial candidate bridge surface data. For example, if the preset bridge width is twenty meters, then using this centerline as the central axis, a buffer of ten meters is extended to both the left and right sides, thereby generating a surface area with a constant width of twenty meters.

[0063] In one embodiment, the step of performing bilateral buffering processing based on the road centerline data and a preset bridge width to obtain initial candidate bridge surface data includes: determining the single-sided distance extending to both sides of the centerline based on the preset bridge width; generating bilateral boundary lines parallel to the centerline based on the road centerline data and the single-sided distance; and constructing a closed polygonal surface based on the road centerline data and the bilateral boundary lines to obtain the initial candidate bridge surface data.

[0064] Understandably, the purpose of this step is to standardize and reconstruct the physical morphology of the bridge based on prior knowledge. The original road surface covering the water may vary in width and have irregular boundaries due to data sources or actual conditions. By using a buffer based on the centerline and a fixed width, a new surface layer with a regular geometric shape and consistent width can be generated. This better conforms to the uniform and symmetrical engineering characteristics of artificial bridge structures and effectively smooths out the geometric noise in the original data.

[0065] Step S304: The overlapping area between the initial candidate bridge deck data and the connected water surface data is clipped to obtain the candidate bridge deck data.

[0066] It should be noted that clipping is a specific spatial analysis method that uses one layer as the clipping boundary to extract the overlapping portion of another layer. In this step, the connected water surface data acts as the clipping boundary, while the initial candidate bridge surface data is the object being clipped. The resulting candidate bridge surface data refers to the portions of the initial candidate bridge surfaces that actually fall within the connected water surface area.

[0067] Understandably, while the initial candidate bridge surfaces are generated based on the road centerline crossing the water, the buffered surfaces may partially cover the riverbank or land. By using connected water surfaces for trimming, these parts unrelated to the water can be removed, ensuring that the final candidate bridge surface data is strictly limited to the water area, thus more accurately corresponding to the actual bridge's projection area on the water surface.

[0068] It should be understood that the candidate bridge deck data obtained after this processing step has its geometry definitively confirmed, representing the precise projection of the bridge structure onto the water body at a standardized width. This data is a direct result of the aforementioned series of processing steps, providing high-precision basic geographic information for subsequent possible bridge feature verification or statistical analysis.

[0069] In this embodiment, the road surface crossing the waterway is extracted through intersection calculation, and then its centerline is extracted and buffered on both sides with a preset bridge width to generate a regular candidate surface. Finally, the water surface is used to trim the candidate surface to obtain an accurate bridge projection surface. This scheme transforms the original road data into standardized, high-precision candidate bridge surface data through a series of progressively refined spatial geometric operations.

[0070] In summary, this embodiment effectively eliminates the interference of irregular road widths and boundaries in the original data by transforming irregular road surfaces into centerlines and then performing standardized reconstruction. This enhances the robustness of the method to data noise, and the generated results have a regular and consistent geometric shape. Finally, by using precise water surface clipping, it ensures that the results strictly correspond to the bridge projection above the water, thereby improving the overall accuracy and reliability of the final candidate bridge surface data.

[0071] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the bridge identification method based on spatial analysis of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0072] This application also provides a bridge identification device based on spatial analysis, please refer to... Figure 3 The bridge identification device based on spatial analysis includes: Data acquisition module 10 is used to acquire road surface vector data and water surface vector data of the target area; Data preprocessing module 20 is used to obtain connected water surface data based on the water surface vector data of the target area; The data preprocessing module 20 is also used to perform spatial overlay analysis based on the connected water surface data and road surface vector data to obtain candidate bridge surface data; The bridge determination module 30 is used to calculate the number of waterway connections in the candidate bridge surface data based on the water surface vector data. The bridge determination module 30 is also used to determine bridge surface data based on the number of waterway connections of the candidate bridge surfaces.

[0073] In one embodiment, the data preprocessing module 20 is further configured to perform outward expansion buffering on the water surface vector data to obtain expanded water surface data; perform adjacent patch fusion and in-plane hole filling on the expanded water surface data to obtain expanded continuous water surface data; and perform inward contraction buffering on the expanded continuous water surface data along the graphic boundary to obtain connected water surface data.

[0074] In one embodiment, the data preprocessing module 20 is further configured to ensure that the buffer distance of the outward expansion buffering process is equal to the buffer distance of the inward contraction buffering process; the in-plane void filling process includes identifying planar voids in the expanded water surface data and filling voids with an area less than or equal to a preset threshold; the adjacent patch fusion process includes fusing patches generated after the outward expansion buffering process where the distance between adjacent patches is less than a preset critical value.

[0075] In one embodiment, the data preprocessing module 20 is further configured to perform layer intersection calculations based on the road surface vector data and the connected water surface data to obtain road surface data covering the water area; obtain road centerline data based on the road surface data covering the water area; perform double-sided buffering processing based on the road centerline data and a preset bridge width to obtain initial candidate bridge surface data; and perform clipping processing on the overlapping area between the initial candidate bridge surface data and the connected water surface data to obtain the candidate bridge surface data.

[0076] In one embodiment, the data preprocessing module 20 is further configured to determine the single-sided distance extending to both sides of the centerline based on the preset bridge width; generate double-sided boundary lines parallel to the centerline according to the road centerline data and the single-sided distance; and construct a closed polygonal surface according to the road centerline data and the double-sided boundary lines to obtain the initial candidate bridge surface data.

[0077] In one embodiment, the bridge determination module 30 is further configured to perform spatial intersection analysis on the candidate bridge surface data and the connected water surface data to identify all independent water areas that are adjacent to the candidate bridge surface; based on the result of the spatial intersection analysis, distinguish and count the independent water areas adjacent to the same candidate bridge surface to obtain the number of independent water areas adjacent to each candidate bridge surface; and associate the number of independent water areas adjacent to each candidate bridge surface with the corresponding candidate bridge surface attributes to obtain the number of water connections in the candidate bridge surface data.

[0078] In one embodiment, the bridge determination module 30 is further configured to acquire background map data of the target area; register the geographical coordinates of the bridge surface data with the coordinate system of the background map data; and overlay and render the registered bridge surface data on the background map in a preset visualization style to generate a distribution map containing the geographical location of the bridge.

[0079] This embodiment avoids the image processing challenges faced by high-resolution remote sensing image data, such as cloud cover and tree shading, by using only vector data of water areas and roads. The entire process employs vector operations, resulting in higher speed and efficiency when extracting bridges over large areas. The extraction method of this invention can quickly identify bridges with high accuracy. Compared to computer vision-based methods that require significant computational resources and time, this method is faster and effectively saves computational resources. Furthermore, it has stronger generalization capabilities, eliminating the need for repeated modeling; the same model can be applied to a large area.

[0080] The bridge identification device based on spatial analysis provided in this application, employing the bridge identification method based on spatial analysis in the above embodiments, can solve the technical problem of how to automatically and accurately identify bridge elements based on existing road and waterway vector data. Compared with the prior art, the beneficial effects of the bridge identification device based on spatial analysis provided in this application are the same as those of the bridge identification method based on spatial analysis provided in the above embodiments, and other technical features in the bridge identification device based on spatial analysis are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0081] This application provides a bridge identification device based on spatial analysis. The bridge identification device based on spatial analysis includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the bridge identification method based on spatial analysis in the above embodiment 1.

[0082] The following is for reference. Figure 4This document illustrates a structural schematic diagram of a bridge identification device based on spatial analysis suitable for implementing embodiments of this application. The bridge identification device based on spatial analysis in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 4 The bridge identification device based on spatial analysis shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0083] like Figure 4 As shown, the bridge identification device based on spatial analysis may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the bridge identification device based on spatial analysis. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the spatial analysis-based bridge identification device to communicate wirelessly or wiredly with other devices to exchange data. Although a spatial analysis-based bridge identification device with various systems is shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0084] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0085] The bridge identification device based on spatial analysis provided in this application, employing the bridge identification method based on spatial analysis in the above embodiments, can solve the technical problem of how to automatically and accurately identify bridge elements based on existing road and waterway vector data. Compared with the prior art, the beneficial effects of the bridge identification device based on spatial analysis provided in this application are the same as those of the bridge identification method based on spatial analysis provided in the above embodiments, and other technical features of this bridge identification device based on spatial analysis are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0086] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0087] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0088] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the bridge identification method based on spatial analysis in the above embodiments.

[0089] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0090] The aforementioned computer-readable storage medium may be included in a bridge identification device based on spatial analysis; or it may exist independently and not assembled into a bridge identification device based on spatial analysis.

[0091] The aforementioned computer-readable storage medium carries one or more programs that, when executed by a bridge identification device based on spatial analysis, cause the bridge identification device based on spatial analysis to: acquire road surface vector data and water surface vector data of a target area; obtain connected water surface data based on the water surface vector data of the target area; perform spatial overlay analysis on the connected water surface data and road surface vector data to obtain candidate bridge surface data; calculate the number of water surface connections in the candidate bridge surface data based on the water surface vector data; and determine the bridge surface data based on the number of water surface connections in the candidate bridge surface data.

[0092] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0093] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0094] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0095] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described spatial analysis-based bridge identification method. This solves the technical problem of how to automatically and accurately identify bridge elements based on existing road and waterway vector data. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the spatial analysis-based bridge identification method provided in the above embodiments, and will not be repeated here.

[0096] The computer program product provided in this application can solve the technical problem of bridge identification based on spatial analysis. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the bridge identification method based on spatial analysis provided in the above embodiments, and will not be repeated here.

[0097] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A bridge identification method based on spatial analysis, characterized in that, The bridge identification method based on spatial analysis includes: Acquire road surface vector data and water surface vector data for the target area; Based on the water surface vector data of the target area, obtain the connected water surface data; Based on the spatial overlay analysis of the connected water surface data and road surface vector data, candidate bridge surface data are obtained. Based on the water surface vector data, calculate the number of water surface connections in the candidate bridge surface data; The bridge deck data is determined based on the number of waterway connections of the candidate bridge decks; The step of obtaining connected water surface data based on the water surface vector data of the target area includes: The water surface vector data is subjected to outward expansion buffering to obtain expanded water surface data; The expanded water surface data is subjected to adjacent patch fusion and in-surface hole filling processing to obtain expanded continuous water surface data; The expanded continuous water surface data is subjected to inward shrinkage buffering along the graphic boundary to obtain the connected water surface data. The step of obtaining candidate bridge surface data by spatial overlay analysis based on the connected water surface data and road surface vector data includes: By performing layer intersection calculations on the road surface vector data and the connected water surface data, road surface data covering the water area is obtained; Based on the road surface data covering the water area, the road centerline data is obtained; Based on the road centerline data and the preset bridge width, double-sided buffering is performed to obtain initial candidate bridge deck data; The overlapping area between the initial candidate bridge deck data and the connected water surface data is clipped to obtain the candidate bridge deck data.

2. The bridge identification method based on spatial analysis according to claim 1, characterized in that, The buffer distance for the outward expansion buffering process is equal to the buffer distance for the inward contraction buffering process; The in-plane void filling process includes identifying planar voids in the expanded water surface data and filling voids with an area less than or equal to a preset threshold. The adjacent patch fusion process includes fusing patches generated after the outward expansion buffering process where the distance between adjacent patches is less than a preset threshold.

3. The bridge identification method based on spatial analysis according to claim 1, characterized in that, The initial candidate bridge deck data is obtained by performing double-sided buffering based on the road centerline data and the preset bridge width, including: The single-sided distance extending to both sides of the centerline is determined based on the preset bridge width; Based on the road centerline data and the single-sided distance, generate double-sided boundary lines parallel to the centerline; The initial candidate bridge surface data is obtained by constructing a closed polygon surface based on the road centerline data and the two side boundary lines.

4. The bridge identification method based on spatial analysis according to claim 1, characterized in that, The step of calculating the number of waterway connections in the candidate bridge surface data based on the water surface vector data includes: Spatial intersection analysis is performed between the candidate bridge surface data and the connected water surface data to identify all independent water areas that are adjacent to the candidate bridge surface. Based on the results of the spatial intersection analysis, the independent water areas adjacent to the same candidate bridge surface are distinguished and counted to obtain the number of independent water areas adjacent to each candidate bridge surface. The number of independent water areas adjacent to each candidate bridge surface is associated with the corresponding candidate bridge surface attribute to obtain the number of water area connections in the candidate bridge surface data.

5. The bridge identification method based on spatial analysis according to any one of claims 1 to 4, characterized in that, The method further includes: Obtain the background map data of the target area; Register the geographic coordinates of the bridge deck data with the coordinate system of the background map data; The registered bridge surface data is overlaid and rendered on the background map in a preset visualization style to generate a distribution map containing the geographical locations of the bridges.

6. A bridge identification device based on spatial analysis, characterized in that, The bridge identification device based on spatial analysis includes: The data acquisition module is used to acquire road surface vector data and water surface vector data of the target area; The data preprocessing module is used to obtain connected water surface data based on the water surface vector data of the target area; The data preprocessing module is also used to perform spatial overlay analysis based on the connected water surface data and road surface vector data to obtain candidate bridge surface data; The bridge determination module is used to calculate the number of waterway connections in the candidate bridge surface data based on the water surface vector data. The bridge determination module is also used to determine bridge surface data based on the number of waterway connections of the candidate bridge surfaces; The data preprocessing module is further configured to perform outward expansion buffering on the water surface vector data to obtain expanded water surface data; perform adjacent patch fusion and in-surface hole filling on the expanded water surface data to obtain expanded continuous water surface data; and perform inward contraction buffering on the expanded continuous water surface data along the graphic boundary to obtain connected water surface data. The bridge determination module is further configured to perform layer intersection calculations based on the road surface vector data and the connected water surface data to obtain road surface data covering the water area; obtain road centerline data based on the road surface data covering the water area; perform double-sided buffering processing based on the road centerline data and a preset bridge width to obtain initial candidate bridge surface data; and perform clipping processing on the overlapping area between the initial candidate bridge surface data and the connected water surface data to obtain the candidate bridge surface data.

7. A bridge identification device based on spatial analysis, characterized in that, The spatial analysis-based bridge identification device includes: a memory, a processor, and a spatial analysis-based bridge identification program stored in the memory and executable on the processor, the spatial analysis-based bridge identification program being configured to implement the steps of the spatial analysis-based bridge identification method as described in any one of claims 1 to 5.

8. A storage medium, characterized in that, The storage medium stores a bridge identification program based on spatial analysis, which, when executed by a processor, implements the steps of the bridge identification method based on spatial analysis as described in any one of claims 1 to 5.

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