Building pattern recognition method and system based on morphological coding

Abstract

The present invention relates to the field of map production technology, and more particularly to a building pattern recognition method and system based on morphological coding. This method uses vector building data to perform a gridded description of buildings and their adjacent relationships, employs a non-uniform Fourier transform to encode building morphology, and learns the encoded features through a spatial morphological coding network to obtain building pattern classification results. The present invention can better capture both local and global features of buildings, overcome the limitations of traditional two-dimensional image recognition methods for complex building structures, improve the accuracy of building pattern recognition, and more effectively process complex spatial relationships between buildings. Even in high-density urban areas, it can maintain high recognition performance. Compared to data processing methods based on high-resolution remote sensing images, the method significantly reduces computational complexity and can meet the needs of rapid identification of large-scale urban buildings.

Description

Technical Field

[0001] The present invention relates to the technical field of map making, and in particular to a building pattern recognition method and system based on morphological coding. Background Art

[0002] With the acceleration of urbanization, the number and density of buildings in urban areas are increasing. Effectively classifying and identifying buildings has become a critical issue in urban planning, building management, and the development of smart cities. Building pattern recognition not only assists urban planners in optimizing land use but also provides effective data support for automated building management, disaster warning, and intelligent monitoring.

[0003] Traditional building pattern recognition methods rely on image processing algorithms based on texture and shape features. These methods typically rely on high-quality remote sensing imagery. However, in practice, obtaining high-resolution imagery is expensive, and image quality fluctuates significantly under varying weather and lighting conditions, affecting recognition accuracy. Furthermore, recognition methods based on two-dimensional images often struggle with complex building structures and are unable to effectively capture the building's geometric features and spatial relationships.

[0004] In recent years, with the widespread application of geographic information systems (GIS), research methods for pattern recognition based on vector building data have continued to innovate and improve, and their application areas are constantly expanding. Compared with image data, vector data can more accurately describe the geometric shape and spatial location of buildings, and has higher scalability and accuracy. However, existing building pattern recognition methods based on vector data mostly focus on simple geometric feature extraction and fail to effectively utilize the spatial relationships between buildings and their morphological characteristics. In the field of building pattern recognition, how to combine the geometric shape and spatial proximity of buildings for more accurate pattern recognition has become a major technical challenge. Summary of the Invention

[0005] To this end, the present invention provides a building pattern recognition method and system based on morphological coding, which achieves more accurate and robust building classification and recognition by combining the geometric morphological features and spatial adjacency of buildings.

[0006] According to the design scheme provided by the present invention, on the one hand, a building pattern recognition method based on morphological coding is provided, comprising:

[0007] Obtain open source vector building data and construct a building pattern recognition sample dataset with building pattern labels based on the open source vector building data;

[0008] The spatial proximity relationship between buildings in the sample dataset is extracted using the triangulated network of building outline vertices. Based on the spatial proximity relationship and using the second-order simplex grid, the gridded spatial features of the buildings in the sample dataset and the gridded spatial features of the corresponding adjacent buildings are obtained.

[0009] The gridded spatial features of the building and the adjacent buildings are spatially morphologically coded using non-uniform Fourier transform to obtain the spatial morphological coding features of the building and the adjacent buildings;

[0010] The spatial morphology coding features of the building and the adjacent buildings are input into the building spatial morphology coding model, and the building spatial morphology coding features and the adjacent buildings spatial morphology coding features in the sample data set are used to iteratively train the building spatial morphology coding model to obtain a building pattern recognition target model;

[0011] For the vector building data to be identified, the building pattern recognition target model is used to identify the pattern to which the corresponding vector building data belongs;

[0012] Among them, the building spatial morphology coding model includes a relationship feature coding network for learning the relationship features between buildings using the spatial morphology coding features of adjacent buildings, a building feature coding network for learning building features using the building spatial morphology coding features and the relationship features between buildings, and a building pattern prediction network for predicting and identifying building patterns based on building features.

[0013] As the building pattern recognition method based on morphological coding of the present invention, a building pattern recognition sample dataset with building pattern labels is constructed based on open source vector building data, including:

[0014] For the open source vector building data, the pattern labels of each building in the vector building data are calibrated to obtain a sample dataset of building pattern recognition;

[0015] The building pattern recognition sample dataset is divided into a training dataset and a test dataset according to a preset ratio, so that the building space morphology coding model is iteratively trained using the training dataset, and the building space morphology coding model after iterative training is tuned using the test dataset.

[0016] As a building pattern recognition method based on morphological coding of the present invention, further, the spatial proximity relationship between buildings in the sample data set is extracted using the triangulated network of building outline vertices, including:

[0017] Interpolate and encrypt the building outlines in the specified area of ​​the sample dataset and the polygon outlines in the area of ​​the same size;

[0018] The interpolated and encrypted contour points are used as triangle vertices, and the Delaunay triangulation is constructed with the building contour edge and the polygon contour edge and triangle edge overlap as the constraint condition.

[0019] The triangles between two buildings in the Delaunay triangulation are retained, and other triangles are deleted to determine the buildings with triangle connections as adjacent buildings;

[0020] A building spatial proximity graph is constructed with the center of each building as a vertex and the lines connecting the centers of adjacent buildings as edges to represent the spatial proximity relationship between buildings based on the building spatial proximity graph.

[0021] The building pattern recognition method based on morphological coding of the present invention further comprises: obtaining the gridded spatial features of the buildings in the sample data set and the gridded spatial features of the corresponding adjacent buildings based on the spatial proximity relationship and using a second-order simplex grid;

[0022] Construct a plane rectangular coordinate system, scale the building to the specified interval of the coordinate system based on the coordinate origin, number the vertices of the building's outer contour in counterclockwise order, and add the coordinates of the building's outer contour vertices and the origin to the point set;

[0023] Connect the outer contour vertices of two adjacent buildings and the coordinate origin to form a triangle, and add the triangle vertex index to the edge set;

[0024] Obtaining vectors for describing other attribute characteristics of the building, and constructing a second-order simplex network for gridding description of the building characteristics based on the point set, edge set and other attribute characteristic vectors;

[0025] The second-order simplex network is used to obtain the grid description features of buildings and the grid description features of corresponding adjacent buildings.

[0026] As a building pattern recognition method based on morphological coding of the present invention, further, spatial morphological coding is performed on the gridded spatial features of the building and the adjacent buildings using non-uniform Fourier transform, comprising:

[0027] A second-order simplex triangle density function is set based on a second-order simplex network, and the Fourier transform of the density function is obtained by density superposition;

[0028] The density function at each fundamental frequency is taken as the weighted sum of the Fourier transforms of each second-order simplex triangle to obtain a non-uniform Fourier transform of the density function at the specified fundamental frequency group, wherein the non-uniform Fourier transform of the second-order simplex triangle at the corresponding fundamental frequency is calculated based on the symbol content distortion factor in the weighted sum of the Fourier transforms of each second-order simplex triangle;

[0029] The non-uniform Fourier transform of the density function under the specified base frequency group is used to perform non-uniform Fourier coding on the gridded spatial characteristics of the building and the gridded spatial characteristics of the adjacent buildings, so as to obtain the spatial morphological coding characteristics of both the building and the adjacent buildings based on the coding results.

[0030] As a building pattern recognition method based on morphological coding of the present invention, further, using the building spatial morphological coding features and the spatial morphological coding features of adjacent buildings in the sample data set to iteratively train the building spatial morphological coding model, the method includes:

[0031] Inputting the spatial morphological coding features of neighboring buildings into a relational feature coding network to learn the relationship features between buildings using the relational feature coding network, wherein the relational feature coding network is constructed based on Transformer;

[0032] The learned relationship features are used as a relationship matrix to measure the correlation between buildings. The relationship matrix and the building spatial morphology encoding features are input into the building feature encoding network to learn building features. The building feature encoding network consists of a graph convolutional network and a Transformer.

[0033] The building features are input into the building pattern prediction network, in which the multi-layer perceptron is used to reshape the building shape and the pattern category probability of each building is obtained through the softmax function, and the building pattern category corresponding to the maximum probability is used as the building pattern prediction output.

[0034] As the building pattern recognition method based on morphological coding of the present invention, further, the building space morphological coding model is iteratively trained, and further comprises:

[0035] The cross entropy function is used as the target loss function for model training, so as to iteratively train the building spatial morphology coding model using the target loss function.

[0036] In another aspect, the present invention further provides a building pattern recognition system based on morphological coding, comprising: a sample construction module, a feature gridding module, a feature morphological coding module, a model training module and a target recognition module, wherein:

[0037] A sample construction module is used to obtain open source vector building data and construct a building pattern recognition sample dataset with building pattern labels based on the open source vector building data;

[0038] A feature gridding module is used to extract the spatial proximity relationship between buildings in the sample data set using the triangulated network of building outline vertices, and obtain the gridded spatial features of the buildings in the sample data set and the gridded spatial features of the corresponding adjacent buildings based on the spatial proximity relationship and using a second-order simplex grid;

[0039] A feature morphology coding module is used to perform spatial morphology coding on the gridded spatial features of the building and the adjacent buildings using non-uniform Fourier transform to obtain the spatial morphology coding features of the building and the spatial morphology coding features of the adjacent buildings;

[0040] a model training module, configured to input the spatial morphological coding features of the building and its neighboring buildings into the building spatial morphological coding model, and iteratively train the building spatial morphological coding model using the spatial morphological coding features of the building and the spatial morphological coding features of the neighboring buildings in the sample data set, thereby obtaining a target model for building pattern recognition;

[0041] The target recognition module is used to identify the pattern of the vector building data to be identified by using the building pattern recognition target model;

[0042] Among them, the building spatial morphology coding model includes a relationship feature coding network for learning the relationship features between buildings using the spatial morphology coding features of adjacent buildings, a building feature coding network for learning building features using the building spatial morphology coding features and the relationship features between buildings, and a building pattern prediction network for predicting and identifying building patterns based on building features.

[0043] Beneficial effects of the present invention:

[0044] The present invention uses non-uniform Fourier transform to encode the morphological features of buildings, which can better capture the local and global features of buildings, overcome the limitations of traditional two-dimensional image recognition methods under complex building structures, and thus significantly improve the accuracy of building pattern recognition; by incorporating the spatial adjacency of buildings into the feature learning process, the present invention can more effectively handle the complex spatial relationships between buildings, and maintain high recognition performance even in high-density urban areas; using vector building data for pattern recognition significantly reduces the computational complexity compared to data processing methods based on high-resolution remote sensing images, can meet the needs of rapid recognition of large-scale urban buildings, and has good application prospects in the field of map production. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 Schematic diagram of the building pattern recognition process based on morphological coding in an embodiment;

[0046] Figure 2Schematic diagram of the building spatial morphology coding network workflow in the embodiment. DETAILED DESCRIPTION

[0047] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention is further described in detail below with reference to the accompanying drawings and technical solutions.

[0048] In the field of building pattern recognition, how to combine the geometric form and spatial adjacent relationship of buildings to perform more accurate pattern recognition, the embodiment of the present invention, see Figure 1 As shown, a building pattern recognition method based on morphological coding is provided, comprising:

[0049] S101. Obtain open source vector building data, and construct a building pattern recognition sample dataset with building pattern labels based on the open source vector building data.

[0050] Specifically, for the open source vector building data, the pattern labels of each building in the vector building data are calibrated to obtain a building pattern recognition sample dataset; the building pattern recognition sample dataset is divided into a training dataset and a test dataset according to a preset ratio, so that the training dataset is used to iteratively train the building spatial morphology coding model, and the test dataset is used to tune the iteratively trained building spatial morphology coding model.

[0051] The open source vector building data may be sourced from OpenStreetMap, complying with the requirements of its Open Database License (ODbL), and including necessary attribution information.

[0052] S102. Extract the spatial proximity relationship between buildings in the sample data set using the building outline vertex triangulation network, and obtain the gridded spatial features of the buildings in the sample data set and the gridded spatial features of the corresponding adjacent buildings based on the spatial proximity relationship and using a second-order simplex grid.

[0053] Specifically, a constraint-based Delaunay triangulation algorithm can be used to generate a triangulated network of building outline vertices to determine the spatial proximity relationship between buildings and describe the spatial proximity relationship between buildings. The spatial proximity relationship between buildings in the sample dataset is extracted using the triangulated network of building outline vertices.

[0054] First, the building outlines within the study area and the outlines of polygons of the same size are interpolated and encrypted. Then, a Delaunay triangulation is constructed using the encrypted points as triangle vertices, ensuring that the building outline edges and the polygon outline edges of the study area coincide with the triangle edges as a constraint. Next, only the triangles between two buildings are retained, and all other triangles are deleted. Finally, buildings with triangles connecting them are considered adjacent buildings, otherwise they are considered non-adjacent buildings. A building spatial proximity graph G is then constructed, using building centers as vertices and lines connecting the centers of adjacent buildings as edges.

[0055] Among them, the coordinates of the vector building data are standardized and a second-order simplex grid is constructed to describe the spatial characteristics of the building and its adjacent buildings, and the grid description feature S is obtained. B and S A Specific steps may include:

[0056] Define the second-order simplex network structure as shown in formula (1):

[0057]

[0058] Construct a plane rectangular coordinate system with the origin x o =(0,0), scale the building to the interval [0.5,1.5], number the vertices of the building's outer contour in counterclockwise order, and add the coordinates of these vertices and the origin to the point set V; connect two adjacent vertices and the coordinate origin to form a triangle, and add the vertex indices of these triangles to the edge set E; vector D is used to describe other attribute characteristics of the building, and in this embodiment, it can be set to a constant vector 1.

[0059] S103. Perform spatial morphology coding on the gridded spatial features of the building and the adjacent buildings using non-uniform Fourier transform to obtain the spatial morphology coding features of the building and the spatial morphology coding features of the adjacent buildings.

[0060] The grid description feature S is described using non-uniform Fourier transform B and S A Perform spatial morphological coding to obtain the spatial morphological coding features F of the building and its adjacent buildings B and F A The specific steps may include the following:

[0061] First, for the second-order simplex grid S (2) The nth second-order simplex triangle in The density function is defined as shown in formula (2), and the Fourier transform of the density function is obtained by density superposition formula (3).

[0062] Density function As shown in formula (2):

[0063]

[0064] Density overlay function As shown in formula (3):

[0065]

[0066] In a group of N w The fundamental frequency is The non-uniform Fourier transform under is expressed as follows:

[0067]

[0068] Among them, each fundamental frequency w k ∈R 2 on It can be written as each second-order simplex triangle The weighted sum of the Fourier transform of is shown in formula (5):

[0069]

[0070] in The nth second-order simplex triangle has a base frequency of w k The non-uniform Fourier transform of is shown in formula (6):

[0071]

[0072] Where μ(·):S (2) →{-1,1} is the decision Sign function of the area. is the distortion factor, Orthogonal to the unit second-order simplex ratio. is the symbol content distortion factor, and the calculation process is shown in formula (7):

[0073]

[0074] Through the above encoding process, non-uniform Fourier encoding of the building morphology is obtained.

[0075] S104. Input the spatial morphology coding features of the building and the adjacent buildings into the building spatial morphology coding model, and iteratively train the building spatial morphology coding model using the building spatial morphology coding features and the adjacent buildings spatial morphology coding features in the sample data set to obtain a building pattern recognition target model.

[0076] Among them, the building spatial morphology coding model includes a relationship feature coding network for learning the relationship features between buildings using the spatial morphology coding features of adjacent buildings, a building feature coding network for learning building features using the building spatial morphology coding features and the relationship features between buildings, and a building pattern prediction network for predicting and identifying building patterns based on building features.

[0077] The spatial morphological encoding features F of the neighboring buildings A Input the relational feature encoding network composed of Transformer modules to learn the relationship feature E between buildings; on the other hand, the spatial morphology encoding feature F of the building B The building relationship features E are input into a building feature encoding network composed of a graph convolutional network (GCN) and a Transformer module to learn the building features H. The learned building features H are then input into the building pattern prediction model to learn the final building pattern prediction probability values. The loss value is calculated by comparing the building pattern prediction probability values ​​with the actual building pattern category labels. When the loss value converges, the network training is stopped and the building spatial morphology encoding network in the current converged state is used as the building pattern recognition network for the building pattern recognition task. If convergence is not reached, the network is retrained.

[0078] The key role of the Transformer module is to capture the wide-area dependencies between input features. Its specific steps can be summarized as follows:

[0079] Use the weight matrix W Q 、W K and W V Map the input features to query, key, and value vectors; calculate the dot product between the query and the key to obtain the attention score and scale it; convert the attention score into a probability distribution through the Softmax function; and use the weighted sum of the feature values ​​as the final context information.

[0080] The attention score calculation process is shown in formula (8) and formula (9):

[0081]

[0082] Q=X×WQ, K=X×WK, V=X×WV (9)

[0083] The graph convolution module updates features by aggregating domain building features. The update calculation process is as shown in formula (10):

[0084] X c =σ(EHW+b) (10)

[0085] The building space data after graph structuring is represented as G = {H, E, A}, where H∈R m×N is the building characteristic value, E∈R P×m×m is the correlation eigenvalue of adjacent buildings or the proximity matrix of the graph, m is the number of buildings in the current building complex, N is the building eigenvalue dimension, and P is the correlation eigenvalue dimension of adjacent buildings. G ∈R P×m×Q is the output of the convolution operation, σ is the nonlinear activation function, W∈R P×N×Q is the model weight parameter, b∈R P×Q is the model bias parameter, and Q is the embedding dimension of the model output feature.

[0086] The building feature H is input into the Transformer module for feature encoding to further mine the building spatial features. The obtained building spatial features are input into the multi-layer perceptron for feature reshaping to obtain the feature vector of [number of buildings, number of pattern categories]. The feature vector is input into the Softmax function to calculate the pattern category probability of each building. The calculation formula of the Softmax function is shown in formula (11):

[0087]

[0088] In the above formula, p i represents the probability that the predicted building corresponds to the i-th category, K represents the total number of categories, and a represents the building characteristics.

[0089] The cross entropy loss function is used to control the gradient update during training. The calculation formula is as follows:

[0090]

[0091] In the above formula, M is the number of categories; y ic is a sign function, which takes 1 if the true category of sample i is equal to c, otherwise it takes 0; ic is the output probability of predicting sample i belonging to category c.

[0092] S105 , for the vector building data to be identified, using the building pattern recognition target model to identify the pattern to which the corresponding vector building data belongs.

[0093] Using vector building data, a gridded description of buildings and their adjacent relationships is created. Building morphology is encoded using a non-uniform Fourier transform. The encoded features are then learned through a spatial morphology coding network to obtain building pattern classification results. Compared to traditional image-based recognition methods, the feature mining through building morphology coding technology in this embodiment enhances the objectivity of the model and effectively improves the accuracy and efficiency of building pattern recognition.

[0094] Furthermore, based on the above method, an embodiment of the present invention also provides a building pattern recognition system based on morphological coding, comprising: a sample construction module, a feature gridding module, a feature morphological coding module, a model training module and a target recognition module, wherein:

[0095] A sample construction module is used to obtain open source vector building data and construct a building pattern recognition sample dataset with building pattern labels based on the open source vector building data;

[0096] A feature gridding module is used to extract the spatial proximity relationship between buildings in the sample data set using the triangulated network of building outline vertices, and obtain the gridded spatial features of the buildings in the sample data set and the gridded spatial features of the corresponding adjacent buildings based on the spatial proximity relationship and using a second-order simplex grid;

[0097] A feature morphology coding module is used to perform spatial morphology coding on the gridded spatial features of the building and the adjacent buildings using non-uniform Fourier transform to obtain the spatial morphology coding features of the building and the spatial morphology coding features of the adjacent buildings;

[0098] a model training module, configured to input the spatial morphological coding features of the building and its neighboring buildings into the building spatial morphological coding model, and iteratively train the building spatial morphological coding model using the spatial morphological coding features of the building and the spatial morphological coding features of the neighboring buildings in the sample data set, thereby obtaining a target model for building pattern recognition;

[0099] The target recognition module is used to identify the pattern of the vector building data to be identified by using the building pattern recognition target model;

[0100] Among them, the building spatial morphology coding model includes a relationship feature coding network for learning the relationship features between buildings using the spatial morphology coding features of adjacent buildings, a building feature coding network for learning building features using the building spatial morphology coding features and the relationship features between buildings, and a building pattern prediction network for predicting and identifying building patterns based on building features.

[0101] Unless otherwise specifically stated, the relative steps, numerical expressions and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0102] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0103] The units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of each embodiment have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation is not considered to be beyond the scope of the present invention.

[0104] Those skilled in the art will appreciate that all or part of the steps in the above method can be performed by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a magnetic disk, or an optical disk. Alternatively, all or part of the steps in the above embodiment can be implemented using one or more integrated circuits. Accordingly, each module / unit in the above embodiment can be implemented in the form of hardware or software functional modules. The present invention is not limited to any specific combination of hardware and software.

[0105] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A building pattern recognition method based on morphological coding, characterized in that: Include: Obtain open source vector building data and construct a building pattern recognition sample dataset with building pattern labels based on the open source vector building data; The spatial proximity relationship between buildings in the sample dataset is extracted using the triangulated network of building outline vertices. Based on the spatial proximity relationship and using the second-order simplex grid, the gridded spatial features of the buildings in the sample dataset and the gridded spatial features of the corresponding adjacent buildings are obtained. The gridded spatial features of the building and the adjacent buildings are spatially morphologically coded using non-uniform Fourier transform to obtain the spatial morphological coding features of the building and the adjacent buildings; The spatial morphology coding features of the building and the adjacent buildings are input into the building spatial morphology coding model, and the building spatial morphology coding features and the adjacent buildings spatial morphology coding features in the sample data set are used to iteratively train the building spatial morphology coding model to obtain a building pattern recognition target model; For the vector building data to be identified, the building pattern recognition target model is used to identify the pattern to which the corresponding vector building data belongs; Among them, the building spatial morphology coding model includes a relationship feature coding network for learning the relationship features between buildings using the spatial morphology coding features of adjacent buildings, a building feature coding network for learning building features using the building spatial morphology coding features and the relationship features between buildings, and a building pattern prediction network for predicting and identifying building patterns based on building features.

2. The building pattern recognition method based on morphological coding according to claim 1, characterized in that: Based on open source vector building data, a building pattern recognition sample dataset with building pattern labels is constructed, including: For the open source vector building data, the pattern labels of each building in the vector building data are calibrated to obtain a sample dataset of building pattern recognition; The building pattern recognition sample dataset is divided into a training dataset and a test dataset according to a preset ratio, so that the building space morphology coding model is iteratively trained using the training dataset, and the building space morphology coding model after iterative training is tuned using the test dataset.

3. The building pattern recognition method based on morphological coding according to claim 1, characterized in that: The spatial proximity relationship between buildings in the sample dataset is extracted using the triangulated network of building outline vertices, including: Interpolate and encrypt the building outlines in the specified area of ​​the sample dataset and the polygon outlines in the area of ​​the same size; The interpolated and encrypted contour points are used as triangle vertices, and the Delaunay triangulation is constructed with the building contour edge and the polygon contour edge and triangle edge overlap as the constraint condition. The triangles between two buildings in the Delaunay triangulation are retained, and other triangles are deleted to determine the buildings with triangle connections as adjacent buildings; A building spatial proximity graph is constructed with the center of each building as a vertex and the lines connecting the centers of adjacent buildings as edges to represent the spatial proximity relationship between buildings based on the building spatial proximity graph.

4. The building pattern recognition method based on morphological coding according to claim 1 or 3, characterized in that: Based on the spatial proximity relationship and using the second-order simplex grid, the gridded spatial features of the buildings in the sample dataset and the gridded spatial features of the corresponding adjacent buildings are obtained, including: Construct a plane rectangular coordinate system, scale the building to the specified interval of the coordinate system based on the coordinate origin, number the vertices of the building's outer contour in counterclockwise order, and add the coordinates of the building's outer contour vertices and the origin to the point set; Connect the outer contour vertices of two adjacent buildings and the coordinate origin to form a triangle, and add the triangle vertex index to the edge set; Obtaining vectors for describing other attribute characteristics of the building, and constructing a second-order simplex network for gridding description of the building characteristics based on the point set, edge set and other attribute characteristic vectors; The second-order simplex network is used to obtain the grid description features of buildings and the grid description features of corresponding adjacent buildings.

5. The building pattern recognition method based on morphological coding according to claim 1, characterized in that: The spatial morphology encoding of the gridded spatial features of both buildings and adjacent buildings is performed using non-uniform Fourier transform, including: A second-order simplex triangle density function is set based on a second-order simplex network, and the Fourier transform of the density function is obtained by density superposition; Taking the Fourier transform of the density function at each fundamental frequency as the weighted sum of the Fourier transforms of each second-order simplex triangle to obtain a non-uniform Fourier transform of the density function at the specified fundamental frequency group, wherein the non-uniform Fourier transform of the second-order simplex triangle at the corresponding fundamental frequency is calculated based on the sign content distortion factor in the weighted sum of the Fourier transforms of each second-order simplex triangle; The non-uniform Fourier transform of the density function under the specified base frequency group is used to perform non-uniform Fourier coding on the gridded spatial characteristics of the building and the gridded spatial characteristics of the adjacent buildings, so as to obtain the spatial morphological coding characteristics of both the building and the adjacent buildings based on the coding results.

6. The building pattern recognition method based on morphological coding according to claim 1, characterized in that: The building spatial morphology coding features and the adjacent building spatial morphology coding features in the sample dataset are used to iteratively train the building spatial morphology coding model, including: Inputting the spatial morphological coding features of neighboring buildings into a relational feature coding network to learn the relationship features between buildings using the relational feature coding network, wherein the relational feature coding network is constructed based on Transformer; The learned relationship features are used as a relationship matrix to measure the correlation between buildings. The relationship matrix and the building spatial morphology encoding features are input into a building feature encoding network to learn building features using the building feature encoding network. The building feature encoding network is composed of a graph convolutional network and a Transformer. The building features are input into the building pattern prediction network, in which the multi-layer perceptron is used to reshape the building shape and the pattern category probability of each building is obtained through the softmax function, and the building pattern category corresponding to the maximum probability is used as the building pattern prediction output.

7. The building pattern recognition method based on morphological coding according to claim 1 or 6, characterized in that: The building spatial morphology encoding model is iteratively trained and also includes: The cross entropy function is used as the target loss function for model training, so as to iteratively train the building spatial morphology coding model using the target loss function.

8. A building pattern recognition system based on morphological coding, characterized in that: It includes: sample construction module, feature gridding module, feature morphology encoding module, model training module and target recognition module, among which, A sample construction module is used to obtain open source vector building data and construct a building pattern recognition sample dataset with building pattern labels based on the open source vector building data; A feature gridding module is used to extract the spatial proximity relationship between buildings in the sample data set using the triangulated network of building outline vertices, and obtain the gridded spatial features of the buildings in the sample data set and the gridded spatial features of the corresponding adjacent buildings based on the spatial proximity relationship and using a second-order simplex grid; A feature morphology coding module is used to perform spatial morphology coding on the gridded spatial features of the building and the adjacent buildings using non-uniform Fourier transform to obtain the spatial morphology coding features of the building and the spatial morphology coding features of the adjacent buildings; a model training module, configured to input the spatial morphological coding features of the building and its neighboring buildings into the building spatial morphological coding model, and iteratively train the building spatial morphological coding model using the spatial morphological coding features of the building and the spatial morphological coding features of the neighboring buildings in the sample data set, thereby obtaining a target model for building pattern recognition; The target recognition module is used to identify the pattern of the vector building data to be identified by using the building pattern recognition target model; Among them, the building spatial morphology coding model includes a relationship feature coding network for learning the relationship features between buildings using the spatial morphology coding features of adjacent buildings, a building feature coding network for learning building features using the building spatial morphology coding features and the relationship features between buildings, and a building pattern prediction network for predicting and identifying building patterns based on building features.

9. An electronic device, characterized in that: include: at least one processor, and a memory coupled to the at least one processor; The memory stores a computer program, and the computer program can be executed by the at least one processor to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 7 can be implemented.

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