Urban spatial form recognition method, device and equipment and storage medium

Abstract

The invention relates to the technical field of geographic information processing, particularly provides an urban spatial form recognition method, device and equipment and a storage medium, and aims to solve the problem that spatial planning design optimization measures cannot be effectively formulated through general spatial form characterization and classification. In order to achieve the purpose, the urban spatial form recognition method comprises the steps that a spatial form data set of a target area is obtained, the target area is divided into a plurality of road grid areas based on the spatial form data set, each road grid area comprises a plurality of first measurement indexes, and each road grid area comprises a plurality of second measurement indexes based on the spatial form data set; respectively calculating the variance expansion coefficient of each first measurement index of each road grid region, determining a plurality of candidate spatial form types included in the target region based on each variance expansion coefficient, and determining the target spatial form type of each road grid region based on the plurality of candidate spatial form types and each variance expansion coefficient. According to the method, the efficiency and stability of urban spatial form recognition are remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of geographic information processing technology, and in particular to a method, device, equipment and storage medium for identifying urban spatial morphology. Background Art

[0002] Urban spatial form is a numerical representation of specific urban details that is abstracted and simplified. Accurate and effective identification of urban spatial form types can be used to provide current descriptions, comparisons, and references for future optimization and adjustment in urban planning and landscape design.

[0003] Existing urban spatial morphology type recognition technology is still limited to relatively simple spatial data, such as constructing an index system based on data such as area and volume ratio (numerical type), land function and use (categorical type), and remote sensing images (spatial grid). Although a single or combined index system constructed using such data can roughly divide spatial morphology from a relatively macroscopic scale, it is difficult to effectively distinguish spatial morphology categories based solely on such data in the face of increasingly complex, multi-dimensional, and complicated urban spatial morphology. Overly general spatial morphology representation and classification cannot be effectively used to formulate targeted spatial planning and design optimization measures. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present application proposes an urban space morphology recognition method, device, equipment and storage medium.

[0005] In a first aspect, an embodiment of the present application provides a method for identifying urban spatial morphology, including: obtaining a spatial morphology dataset of a target area; dividing the target area into a plurality of road grid areas based on the spatial morphology dataset, each of the road grid areas including a plurality of first measurement indicators; calculating the variance expansion coefficients of the first measurement indicators of each of the road grid areas based on the spatial morphology dataset; determining a plurality of candidate spatial morphology types contained in the target area based on each of the variance expansion coefficients; and determining a target spatial morphology type for each of the road grid areas based on the plurality of candidate spatial morphology types and the variance expansion coefficients.

[0006] In the second aspect, an embodiment of the present application provides an urban spatial morphology identification device, including: an acquisition module, used to acquire a spatial morphology data set of a target area; a division module, used to divide the target area into multiple road grid areas based on the spatial morphology data set, each of the road grid areas including multiple first measurement indicators; a calculation module, used to calculate the variance expansion coefficients of each of the first measurement indicators of each of the road grid areas based on the spatial morphology data set; a first determination module, used to determine multiple candidate spatial morphology types contained in the target area based on each of the variance expansion coefficients; and a second determination module, used to determine the target spatial morphology type of each of the road grid areas based on the multiple candidate spatial morphology types and each of the variance expansion coefficients.

[0007] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described in the first aspect above is implemented.

[0008] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect above is implemented.

[0009] The technical solution provided in the embodiments of the present application has at least the following technical effects or advantages:

[0010] The urban spatial morphology recognition method of the embodiment of the present application obtains a spatial morphology data set of a target area, divides the target area into multiple road grid areas based on the spatial morphology data set, each road grid area includes multiple first measurement indicators, and calculates the variance expansion coefficients of each first measurement indicator of each road grid area based on the spatial morphology data set. Based on each variance expansion coefficient, multiple candidate spatial morphology types contained in the target area are determined, which can effectively integrate spatial morphology data, perform comprehensive first measurement indicator measurement at a unified spatial scale, and thus accurately and efficiently identify urban spatial morphology.

[0011] By determining the target spatial morphology type of each road grid area based on multiple candidate spatial morphology types and various variance expansion coefficients, the efficiency and accuracy of urban spatial morphology recognition are greatly improved.

[0012] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0014] Figure 1 A flow chart of a method for identifying urban space morphology provided in an embodiment of the present application is shown;

[0015] Figure 2 A schematic diagram of the structure of an urban space form recognition device provided in an embodiment of the present application is shown;

[0016] Figure 3 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0017] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0018] Existing urban spatial morphology type recognition technology is still limited to relatively simple spatial data, such as constructing an index system based on data such as area and volume ratio (numerical type), land function and use (categorical type), and remote sensing images (spatial grid). Although a single or combined index system constructed using such data can roughly divide spatial morphology from a relatively macroscopic scale, it is difficult to effectively distinguish spatial morphology categories based solely on such data in the face of increasingly complex, multi-dimensional, and complicated urban spatial morphology. Overly general spatial morphology representation and classification cannot be effectively used to formulate targeted spatial planning and design optimization measures.

[0019] Based on this, the embodiment of the present application provides a method for identifying urban space morphology. The solution of the embodiment of the present application is specifically described below in conjunction with the accompanying drawings.

[0020] See also Figure 1 A flow chart of a method for identifying urban space morphology is shown, and the method specifically comprises the following steps:

[0021] Step 101: Obtain a spatial morphological dataset of a target area.

[0022] In the embodiments of the present application, the spatial morphology dataset may refer to a multi-source multi-modal spatial morphology dataset, that is, a data set that integrates multiple sources and multiple modalities. The spatial morphology dataset may include three-dimensional building data, traffic network data, point of interest data, street view image data, number of active population, vegetation coverage data inverted from satellite images, and land parcel boundary data, etc., which are not specifically limited in the embodiments of the present application.

[0023] In one implementation, the spatial morphological data of the target area may be obtained from different sources such as satellite remote sensing, drone aerial photography, and ground observation stations as the spatial morphological data set of the target area.

[0024] Step 102: Based on the spatial morphology dataset, the target area is divided into a plurality of road grid areas, each road grid area includes a plurality of first measurement indicators.

[0025] In one implementation, the traffic network data and the land boundary data can be input into ArcGIS (Geographic Information System), and then the target area can be cut to obtain a plurality of road grid areas, each of which includes a plurality of first measurement indicators.

[0026] Furthermore, the first measurement indicator may include one or more of building height density, average building height, average building base circumference, average building area, floor area ratio, street aspect ratio, population, perception, style, and accessibility.

[0027] Step 103: Based on the spatial morphology data set, the variance expansion coefficient of each first measurement indicator of each road grid area is calculated respectively.

[0028] Since the calculation method of the variance inflation coefficient is a conventional technical means in the art, it will not be repeated in the embodiments of the present application.

[0029] Step 104: Based on each variance expansion coefficient, determine a plurality of candidate spatial morphological types included in the target area.

[0030] In one embodiment, based on the variance expansion coefficients of the first measurement indicators of each road grid area, the second measurement indicators of each road grid area that meet the preset conditions can be obtained respectively, and then based on the second measurement indicators of each road grid area, the multiple candidate spatial morphology types contained in the target area can be determined.

[0031] Furthermore, if the variance inflation coefficient of each first measurement indicator is less than a preset variance inflation coefficient threshold, it is determined that the first measurement indicator corresponding to the variance inflation coefficient meets the preset condition; otherwise, it is determined that the first measurement indicator corresponding to the variance inflation coefficient does not meet the preset condition.

[0032] It should be noted that the preset variance inflation coefficient threshold may be a coefficient threshold pre-set by a person skilled in the art according to actual needs, or may be a coefficient threshold obtained by adjusting a set coefficient threshold according to actual needs. The present application embodiment is not specifically limited, for example, the preset variance inflation coefficient threshold may be set to 10.

[0033] On the basis of the above-mentioned implementation manner, in some modified implementation manners, based on each second measurement indicator of each road grid area, multiple candidate spatial morphology types contained in the target area are determined, which can specifically include: based on each second measurement indicator, using a clustering algorithm to calculate multiple candidate spatial morphology types contained in the target area.

[0034] Furthermore, based on each second measurement indicator, a clustering algorithm is used to calculate multiple candidate spatial morphology types contained in the target area. Specifically, the silhouette coefficient can be used to calculate the optimal number of classifications for spatial morphology type division using each second measurement indicator, that is, the number of candidate spatial morphology types, and then the K-Means algorithm is used to calculate the number of candidate spatial morphology types based on each second measurement indicator.

[0035] For example, assuming that the second measurement indicators include building height density, average building height, average building base circumference, average building area, floor area ratio, and street height-to-width ratio, 10 candidate spatial morphological types can be obtained using the clustering algorithm, namely, medium-sized medium-strong low-density low-rise, medium-sized low-strong high-density low-rise, medium-sized high-strength high-density medium-high-rise, medium-sized low-density low-rise, medium-sized medium-strong medium-low-density low-rise, medium-sized medium-strong medium-density low-rise, small medium-strong high-density low-rise, mixed medium-strong high-density low-medium-rise, medium-sized high-strength medium-density high-rise, and large high-density low-rise.

[0036] By obtaining the second measurement indicators of each road grid area that meet the preset conditions based on the variance expansion coefficient of each first measurement indicator of each road grid area, and then determining the multiple candidate spatial morphology types contained in the target area based on the second measurement indicators of each road grid area, redundant first measurement indicators can be proposed, simplifying the indicator system of spatial morphology recognition, thereby greatly improving the accuracy of spatial morphology recognition.

[0037] Step 105: Determine the target spatial morphology type of each road grid area based on the multiple candidate spatial morphology types and each variance expansion coefficient.

[0038] In one embodiment, a balance function in a pre-constructed decision tree model can be used to select multiple third measurement indicators from each first measurement indicator in a first road grid area, where the first road grid area is any one of the multiple road grid areas. Then, the decision tree model is used to calculate the predicted variance expansion coefficient range of each third measurement indicator of each candidate spatial morphology type in the first road grid area. Finally, based on the predicted variance expansion coefficient range of each third measurement indicator of each candidate spatial morphology type and the variance expansion coefficient of each third measurement indicator, the target spatial morphology type of the first road grid area is determined.

[0039] Furthermore, based on the predicted variance expansion coefficient range of each third measurement indicator of each candidate spatial morphology type and the variance expansion coefficient of each third measurement indicator, the target spatial morphology type of the first road grid area is determined. Specifically, it can be: judging whether the variance expansion coefficient of each third measurement indicator is within the predicted variance expansion coefficient range; if the variance expansion coefficients of each third measurement indicator are all within the predicted variance expansion coefficient range, then the candidate spatial morphology type corresponding to the predicted variance expansion coefficient is the target spatial morphology type of the first road grid area.

[0040] On the basis of the above implementation, in some modified implementations, a balance function in a pre-constructed decision tree model is used to select multiple third measurement indicators from each first measurement indicator in the first road grid area. Specifically, the balance function in the pre-constructed decision tree model is used to determine the number of third measurement indicators, and then the decision tree model is used to calculate the feature importance value of each first measurement indicator, and then the number of third measurement indicators with the largest feature importance value is obtained, wherein the balance function includes:

[0041] Score=α×Accuracy-β×F_Number

[0042] Among them, Score represents the score of the output result of the decision tree model, α represents the preset first adjustment coefficient threshold, β represents the preset second adjustment coefficient threshold, Accuracy represents the accuracy of the decision tree model, and F_Number represents the number of the third measurement indicator.

[0043] The embodiments of the present application can effectively integrate multi-source and multi-modal data, significantly improve the efficiency and stability of urban spatial morphology identification by reducing data redundancy, simplifying the indicator system, and generating accurate predicted variance expansion coefficient ranges for each third measurement indicator of the candidate spatial morphology type, thereby improving the scientificity and practicality of urban spatial morphology identification.

[0044] The embodiment of the present application obtains a spatial morphology data set of a target area, divides the target area into multiple road grid areas based on the spatial morphology data set, each road grid area includes multiple first measurement indicators, and calculates the variance expansion coefficients of each first measurement indicator of each road grid area based on the spatial morphology data set. Based on each variance expansion coefficient, multiple candidate spatial morphology types contained in the target area are determined, which can effectively integrate spatial morphology data, perform comprehensive first measurement indicator measurement at a unified spatial scale, and further accurately and efficiently identify urban spatial morphology.

[0045] By determining the target spatial morphology type of each road grid area based on multiple candidate spatial morphology types and various variance expansion coefficients, the efficiency and accuracy of urban spatial morphology recognition are greatly improved.

[0046] See also Figure 2 The embodiment of the present application further provides an urban space form recognition device, which is used to execute the urban space form recognition method described in the above embodiment, and the device includes:

[0047] An acquisition module 201 is used to acquire a spatial morphological dataset of a target area;

[0048] A division module 202, for dividing the target area into a plurality of road grid areas based on the spatial morphology data set, each road grid area including a plurality of first measurement indicators;

[0049] A calculation module 203, used to calculate the variance expansion coefficient of each first measurement index of each road grid area based on the spatial morphology data set;

[0050] A first determination module 204 is used to determine a plurality of candidate spatial morphological types included in the target area based on each variance expansion coefficient;

[0051] The second determination module 205 is used to determine the target spatial morphology type of each road grid area based on multiple candidate spatial morphology types and each variance expansion coefficient.

[0052] The urban space form recognition device provided in the embodiment of the present application and the urban space form recognition method provided in the above embodiment are based on the same inventive concept and have the same beneficial effects as the methods adopted, operated or implemented therein.

[0053] The present application also provides an electronic device corresponding to the urban space form recognition method provided in the above embodiment. Figure 3 , which shows a schematic diagram of an electronic device provided by some embodiments of the present application. Figure 3As shown, the electronic device 30 may include: a processor 300, a memory 301, a bus 302 and a communication interface 303, wherein the processor 300, the communication interface 303 and the memory 301 are connected via the bus 302; the memory 301 stores a computer program that can be run on the processor 300, and when the processor 300 runs the computer program, the urban space form recognition method provided in any of the aforementioned embodiments of the present application is executed.

[0054] The memory 301 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one physical port 303 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0055] The bus 302 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 301 is used to store programs, and the processor 300 executes the programs after receiving the execution instructions. The urban space form recognition method disclosed in any implementation of the embodiment of the present application may be applied to the processor 300, or implemented by the processor 300.

[0056] The processor 300 may be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 300 or an instruction in the form of software. The above processor 300 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module may be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 301, and the processor 300 reads the information in the memory 301 and completes the steps of the above method in combination with its hardware.

[0057] The electronic device provided in the embodiment of the present application and the urban space form recognition method provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, operated or implemented therein.

[0058] An embodiment of the present application also provides a computer-readable storage medium corresponding to the urban space form recognition method provided in the aforementioned embodiment, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it will execute the urban space form recognition method provided in any of the aforementioned embodiments.

[0059] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0060] The embodiments of the present application also provide a computer program product corresponding to the urban space form recognition method provided in the aforementioned embodiments, including a computer program, which is executed by a processor to implement the urban space form recognition method provided in the aforementioned embodiments.

[0061] The computer-readable storage medium and computer program product provided in the above-mentioned embodiments of the present application are based on the same inventive concept as the urban space form recognition method provided in the embodiments of the present application, and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0062] It should be noted that:

[0063] The algorithm and display provided herein are not inherently related to any particular computer, virtual device or other equipment. Various general purpose devices can also be used together with the teachings based on this. According to the above description, it is obvious to construct the structure required for this type of device. In addition, the application is not directed to any specific programming language. It should be understood that various programming languages ​​can be utilized to implement the content of the application described herein, and the description of the specific language above is to disclose the best mode of implementation of the application.

[0064] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0065] Similarly, it should be understood that in order to streamline the present application and help understand one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present application, the various features of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be interpreted as reflecting the following intention: the claimed application requires more features than the features clearly stated in each claim. More specifically, as reflected in the claims below, the inventive aspects are less than all the features of the single embodiment disclosed above. Therefore, the claims following the specific embodiment are hereby expressly incorporated into the specific embodiment, wherein each claim itself serves as a separate embodiment of the present application.

[0066] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition they may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0067] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present application and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0068] The various component embodiments of the present application can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all functions of some or all components in the creation device of the virtual machine according to the embodiment of the present application. The application can also be implemented as a device or device program (e.g., computer program and computer program product) for executing part or all of the methods described herein. Such a program implementing the present application can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0069] It should be noted that the above embodiments illustrate the present application rather than limit the present application, and that those skilled in the art may design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets should not be constructed as a limitation to the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of multiple such elements. The present application may be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim that lists several devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names.

[0070] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for identifying urban space morphology, characterized in that: include: Obtain the spatial morphological dataset of the target area; Based on the spatial morphology dataset, the target area is divided into a plurality of road grid areas, each of the road grid areas includes a plurality of first measurement indicators; Based on the spatial morphological data set, respectively calculating the variance expansion coefficient of each of the first measurement indicators of each of the road grid areas; Based on each of the variance expansion coefficients, determining a plurality of candidate spatial morphological types included in the target area; Based on the plurality of candidate spatial morphology types and the variance expansion coefficients, a target spatial morphology type of each road grid area is determined.

2. The urban space morphology recognition method according to claim 1, characterized in that: The spatial morphology data set includes traffic road network data and land parcel boundary data. Based on the spatial morphology data set, the target area is divided into a plurality of road grid areas, including: Based on the traffic road network data and the land parcel boundary data, the target area is cut to obtain a plurality of road grid areas.

3. The urban space morphology recognition method according to claim 1, characterized in that: The first measurement indicator includes one or more of building height density, average building height, average building base circumference, average building area, floor area ratio, street height-to-width ratio, population, perception, style, and accessibility.

4. The urban space morphology recognition method according to claim 1, characterized in that: The determining, based on each of the variance expansion coefficients, a plurality of candidate spatial morphological types included in the target area comprises: Based on the variance expansion coefficients of the first metrics of the road grid areas, respectively, obtaining second metrics of the road grid areas where the first metrics meet preset conditions; Based on each of the second measurement indicators of each of the road grid areas, a plurality of the candidate spatial morphology types included in the target area are determined.

5. The urban space morphology recognition method according to claim 4, characterized in that: The determining, based on each of the second measurement indicators of each of the road grid areas, a plurality of candidate spatial morphology types included in the target area includes: Based on each of the second measurement indicators, a clustering algorithm is used to calculate a plurality of the candidate spatial morphological types included in the target area.

6. The urban space morphology recognition method according to claim 1, characterized in that: The step of determining the target spatial morphology type of each road grid area based on the plurality of candidate spatial morphology types and each variance expansion coefficient comprises: Using a balance function in a pre-built decision tree model, select a plurality of third measurement indicators from each of the first measurement indicators in a first road grid area, where the first road grid area is any one of the plurality of road grid areas; Using the decision tree model, calculating the predicted variance expansion coefficient range of each of the third measurement indicators of each of the candidate spatial morphology types in the first road grid area; The target spatial morphology type of the first road grid area is determined based on the predicted variance inflation coefficient range of each of the third metrics of each of the candidate spatial morphology types and the variance inflation coefficient of each of the third metrics.

7. The urban space morphology recognition method according to claim 6, characterized in that: The method of selecting a plurality of third metrics from each of the first metrics in the first road grid area by using the balance function in the pre-built decision tree model comprises: Determining the number of the third measurement indicator by using a balance function in a pre-built decision tree model; Using the decision tree model, calculating the feature importance value of each of the first measurement indicators; Obtain the number of the third measurement indicators with the largest feature importance values; The balancing functions include: Score=α×Accuracy-β×F_Number Among them, Score represents the score of the output result of the decision tree model, α represents the preset first adjustment coefficient threshold, β represents the preset second adjustment coefficient threshold, Accuracy represents the accuracy of the decision tree model, and F_Number represents the number of the third measurement indicator.

8. An urban space form recognition device, characterized in that: include: An acquisition module is used to acquire a spatial morphological dataset of a target area; A division module, configured to divide the target area into a plurality of road grid areas based on the spatial morphology data set, each of the road grid areas comprising a plurality of first measurement indicators; A calculation module, used for respectively calculating the variance expansion coefficient of each of the first measurement indicators of each of the road grid areas based on the spatial morphology data set; A first determination module, configured to determine a plurality of candidate spatial morphological types included in the target area based on each of the variance expansion coefficients; The second determination module is used to determine the target spatial morphology type of each of the road grid areas based on the plurality of candidate spatial morphology types and each variance expansion coefficient.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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