Housing vacant area identification method, system and device and medium

Abstract

The invention discloses a housing vacant area identification method, system and device, and a medium. The method comprises the steps of obtaining a night light image and an urban land image of a target area; performing light intensity analysis based on the night light image and the urban land image to obtain an urban vacancy rate index of the target area; performing regression analysis by using the night light image to obtain a night light value change trend of the target area; and obtaining a housing vacancy area distribution condition of the target area based on the urban vacancy rate index and the night light change trend. The problem that in the prior art, population loss is difficult to dynamically capture and the influence of the population loss on housing vacancy causes poor vacant house identification precision is solved.

Description

Background Art

[0002] At present, the monitoring of vacant housing has become increasingly important. Existing technologies mainly identify and simulate vacant housing areas through statistical models, remote sensing data analysis, or case-based reasoning models, and these methods have been applied in urban planning and land use management.

[0003] However, these technologies are difficult to dynamically capture population loss and its impact on housing vacancy, resulting in limited application effects in complex urban expansion and contraction environments and poor accuracy in identifying vacant houses. Summary of the Invention

[0004] In order to overcome the problem that existing technologies are difficult to dynamically capture population loss and its impact on housing vacancy, resulting in poor accuracy in identifying vacant houses, this application provides a method, system, device, and medium for identifying vacant housing areas.

[0005] In a first aspect, to solve the above technical problems, this application provides a method for identifying vacant housing areas, including:

[0006] Obtain the night light image and urban land image of the target area;

[0007] Perform light intensity analysis based on the night light image and urban land image to obtain the urban vacancy rate index of the target area;

[0008] Use the night light image to perform regression analysis to obtain the changing trend of the night light value in the target area;

[0009] Based on the urban vacancy rate index and the changing trend of night light, obtain the distribution of vacant housing areas in the target area.

[0010] In a second aspect, this application also provides a system for identifying vacant housing areas, including:

[0011] An acquisition module for obtaining the night light image and urban land image of the target area;

[0012] A first analysis module for performing light intensity analysis based on the night light image and urban land image to obtain the urban vacancy rate index of the target area;

[0013] A second analysis module for using the night light image to perform regression analysis to obtain the changing trend of the night light value in the target area;

[0014] A distribution determination module for obtaining the distribution of vacant housing areas in the target area based on the urban vacancy rate index and the changing trend of night light.

[0015] In a third aspect, the present application also provides a computing device, including a memory, a processor, and a program stored on the memory and running on the processor. When the processor executes the program, the steps of a housing vacancy area recognition method as described above are implemented.

[0016] In a fourth aspect, the present application also provides a computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a terminal device, the terminal device is caused to execute the steps of a housing vacancy area recognition method.

[0017] The beneficial effects of the present application are as follows: By analyzing the light intensity of the nighttime light image and the urban land image of the target area, the urban vacancy rate index of the target area is obtained. The urban vacancy rate index can measure the housing vacancy degree of the target area. Then, regression analysis is performed using the nighttime light image to obtain the changing trend of the nighttime light value in the target area, which can dynamically capture the trend of population loss. Finally, based on the urban vacancy rate index and the changing trend of nighttime light, the housing vacancy distribution in the target area is obtained. In this way, by combining the two dimensions of housing vacancy influencing factors, namely the housing vacancy degree and the population loss trend of the target area, the real housing vacancy distribution in the target area is dynamically predicted, making the recognition of the housing vacancy distribution more immediate, and thus improving the recognition accuracy of vacant houses in the target area. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic flowchart of a housing vacancy area recognition method shown in an exemplary embodiment of the present application;

[0019] Figure 2 It is a schematic structural diagram of a housing vacancy area recognition system shown in an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0020] The following embodiments are further explanations and supplements to the present application and do not constitute any limitation to the present application.

[0021] The spatial distribution characteristics and changing laws of housing vacancy areas are important bases for evaluating urban shrinkage patterns. Population loss is the direct cause of housing vacancy areas. A lower population density will inevitably lead to a slowdown in the expansion of urban land near the vacancy areas. At the same time, the continuous dynamic loss of population reflects the potential possibility of housing vacancy in the area, and potential housing vacancy areas also limit the spatial development of the city.

[0022] Existing statistical models, remote sensing data analysis, or case-based reasoning models are used to identify and simulate housing vacancy areas. Although they have been applied in urban planning and land use management, these technologies have defects such as insufficient identification accuracy, difficulty in dynamically capturing population loss and its impact on housing vacancy, neglecting the spatial relationship between the vacancy area and surrounding land uses, and poor adaptability to the characteristics of special types of cities (such as resource-based cities). As a result, their application effects in complex urban expansion and contraction environments are limited.

[0023] To solve the above problems, embodiments of the present application provide a method, system, device, and medium for identifying housing vacancy areas. These embodiments will be described in detail below.

[0024] A method for identifying housing vacancy areas provided by an embodiment of the present application can be specifically executed by a server. It should be noted that the server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, Content Delivery Network (CDN), and big data and artificial intelligence platforms. There is no limitation here.

[0025] Please refer to Figure 1 , Figure 1 which shows a method for identifying housing vacancy areas according to an exemplary embodiment of the present application. As Figure 1 shown, the present application provides a method for identifying housing vacancy areas, including:

[0026] Step S11, obtaining a night light image and an urban land use image of the target area;

[0027] Step S12, performing light intensity analysis based on the night light image and the urban land use image to obtain the urban vacancy rate index of the target area;

[0028] Step S13, performing regression analysis using the night light image to obtain the change trend of the night light value in the target area;

[0029] Step S14, obtaining the distribution of housing vacancy areas in the target area based on the urban vacancy rate index and the night light change trend.

[0030] A method for identifying housing vacancy areas in this embodiment obtains the urban vacancy rate index of the target area by analyzing the light intensity of the nighttime light image and the urban land use image of the target area. The urban vacancy rate index can measure the degree of housing vacancy in the target area. Then, regression analysis is performed using the nighttime light image to obtain the changing trend of the nighttime light value in the target area, which can dynamically capture the trend of population loss. Finally, based on the urban vacancy rate index and the changing trend of nighttime lights, the housing vacancy distribution in the target area is obtained. In this way, by combining the two dimensions of housing vacancy influencing factors, namely the degree of housing vacancy and the trend of population loss in the target area, the real housing vacancy distribution in the target area is dynamically predicted, making the identification of the housing vacancy distribution more immediate, thereby improving the identification accuracy of vacant houses in the target area.

[0031] In this embodiment provided by this application, the larger the built-up area and the fewer the urban population, the lower the urban vacancy rate index, indicating a higher degree of housing vacancy. The urban vacancy rate index can show the relative situation between the urban land expansion speed and the urban population development speed, thereby effectively revealing the spatial differentiation pattern of the housing vacancy phenomenon and measuring the degree of housing vacancy.

[0032] Optionally, the nighttime light image includes the current nighttime image; obtaining the urban vacancy rate index of the target area based on the nighttime light image and the urban land use image includes:

[0033] Using the urban land use image to divide the pixels of the nighttime light image to obtain non-residential area pixels and residential area pixels;

[0034] Based on the pixel information of the non-residential area pixels and the residential area pixels, obtain the true light intensity of the residential area pixels;

[0035] Based on the urban land use image and the true light intensity, calculate the first urban vacancy rate index of the residential area pixels and determine that the second urban vacancy rate index of the non-residential area pixels is 0;

[0036] The calculation formula for the first urban vacancy rate index is as follows:

[0037]

[0038] Wherein, GTI represents the first urban vacancy rate index of the residential area pixel d in the nighttime light image, DN R,d represents the gray level of the residential area pixel d, and N d represents the number of pixels of the urban land use image contained in the residential area pixel d;

[0039] Based on the first urban vacancy rate index and the second urban vacancy rate index, form the urban vacancy rate index of the target area.

[0040] In this embodiment provided by the present application, by dividing the pixels of the night light image using the urban land image, the spatial relationship between the vacant area and the surrounding land can be taken into account. The night light image can be directly preliminarily divided into residential and non-residential pixels according to the urban land situation in the urban land image, and the first urban vacancy rate index of the residential pixels can be calculated only based on the urban land image and the true light intensity, reducing the calculation amount of the urban vacancy rate index, improving the calculation efficiency of the urban vacancy rate index, and further improving the recognition efficiency of the housing vacancy area distribution in the target area obtained by subsequent recognition.

[0041] Optionally, the night light image is currently divided into non-residential pixels and residential pixels by using the urban land image, including:

[0042] Overlay the urban land image and the night light image for data, and calculate the urban land occupation ratio of each pixel in the night light image; wherein, the pixel size of the night light image is larger than the pixel size of the urban land image;

[0043] The calculation formula of the urban land occupation ratio is as follows:

[0044]

[0045] Among them, UAR represents the urban land occupation ratio, N d represents the second pixel number of the pixels of the urban land image contained in a single pixel of the night light image; N t represents the maximum number of the pixels of the urban land image contained in a single pixel of the night light image;

[0046] The pixels in the night light image with an urban land occupation ratio less than the preset value are used as non-residential pixels; wherein, the preset value is 20%;

[0047] The pixels in the night light image with an urban land occupation ratio greater than or equal to the preset value are used as residential pixels.

[0048] In this embodiment provided by the present application, by overlaying the urban land image and the night light image, the inclusion relationship and quantity obtained from the overlap between each pixel in the night light image and the urban land pixels in the urban land image can be understood, thereby obtaining the urban occupation ratio of each pixel in the night light image. Then, the pixels in the night light image with an urban occupation ratio greater than or equal to a preset value are used as residential area pixels, which can eliminate the lights in non-residential areas such as commercial service land and roads in the night light image, and the pixels in the night light image with an urban occupation ratio less than the preset value are used as non-residential area pixels. This makes it convenient to calculate only the first urban vacancy rate index of non-residential area pixels to obtain an urban vacancy rate index with an accuracy meeting the requirements, reducing the calculation amount of the urban vacancy rate index, improving the calculation efficiency of the urban vacancy rate index, and further improving the recognition efficiency of the housing vacancy area distribution in the target area obtained in subsequent recognition.

[0049] In this embodiment provided by the present application, the urban land image of the target area is obtained by extracting urban land based on the land use classification data of the target area and resampling it to a resolution of 10m × 10m. Among them, the maximum number N of pixels of the urban land image contained in a single pixel in the night light image t is 2500,

[0050] Optionally, using the pixel information of non-residential area pixels and residential area pixels to obtain the true light intensity of residential area pixels, including:

[0051] Obtain the first original gray value and the first number of pixels of non-residential area pixels, and obtain the second original gray value of residential area pixels;

[0052] Using the first original gray value, the first number of pixels and the second original gray value, calculate the true light intensity of residential area pixels;

[0053] The calculation formula of the true light intensity is as follows:

[0054]

[0055] Among them, DN R,d represents the gray value of residential area pixel d, that is, the true light intensity of residential area pixel d, DN P,d represents the second original gray value of residential area pixel d, N non-resi represents the first number of pixels of non-residential area pixels, DN non-resi represents the sum of gray values of non-residential area pixels, DN non-resi,k represents the first original gray value of non-residential area pixel k.

[0056] In this embodiment provided by the present application, based on the first original gray value and the first number of pixels of non-residential area pixels, the influence of the first original gray value of non-residential area pixels is removed from the second original gray value of residential area pixels, so that the calculated true light intensity of residential area pixels is closer to the true value, thereby improving the accuracy of the true light intensity, improving the accuracy of the urban vacancy rate index calculated subsequently using the true light intensity, and further improving the accuracy of the housing vacancy area distribution of the target area obtained based on the urban vacancy rate index. Among them, the true light intensity of non-residential area pixels is determined to be 0.

[0057] In this embodiment provided by the present application, for the convenience of statistical analysis, the calculated true light intensity of residential area pixels is adjusted and restricted according to to carry out adjustment and restriction.

[0058] Optionally, the night light image includes a plurality of consecutive historical night images; regression analysis is performed using the night light image to obtain the change trend of the night light value of the target area, including:

[0059] Performing linear regression analysis on the pixels with the same position information in a plurality of consecutive historical night images to obtain the pixel linear regression coefficient corresponding to the position information;

[0060] Taking the pixel linear regression coefficient as the change trend of the pixel light value corresponding to the position information;

[0061] Based on the change trends of multiple pixel light values, the change trend of the night light value of the target area is formed.

[0062] In this embodiment provided by the present application, performing linear regression analysis on the pixels with the same position information in a plurality of consecutive historical night images can establish a linear regression mathematical model, thereby calculating the pixel linear regression coefficient corresponding to each position information. Taking the pixel linear regression coefficient as the change trend of the pixel light value corresponding to the position information, and forming the change trend of the night light value of the target area based on the change trends of multiple pixel light values. In this way, based on the change trend of the night light value of the target area obtained by linear regression, the change law of the light value of the target area can be understood, so that the law of population loss can be well reflected, which is convenient for subsequent dynamically predicting the true housing vacancy distribution of the target area based on the change trend of the night light value that can reflect the law of population loss, making the recognition timeliness of the housing vacancy distribution higher, and thus improving the recognition accuracy of the vacant houses in the target area.

[0063] In this embodiment provided by the present application, regression analysis is a statistical analysis method for determining the relationship between two or more variables. When there is only one independent variable and one dependent variable, the relationship between the two can be approximately represented by a straight line, and this regression analysis is simple linear regression analysis. For example, using the historical night images of the target area for each of the five years from 2011 to 2015 and from 2016 to 2020, with time as the x-axis and the night light intensity value of a single pixel in the historical night image as the y-axis, a linear regression model of the pixels corresponding to the same location information is constructed. Among them, the calculation method of the night light intensity value of a single pixel in the historical night image is the same as that of the true light intensity of a single pixel in the night light image in the foregoing embodiment.

[0064] Performing linear regression analysis on the pixels with the same location information in a series of consecutive historical night images, the linear regression model of a single pixel obtained is expressed as:

[0065] y = ax i +b+ε i ;

[0066]

[0067] Among them, y represents the night light intensity value of a single pixel in the historical night image, x i represents the time of the i-th year, i is a positive integer, a represents the pixel linear regression coefficient corresponding to this single pixel, that is, the change trend of the pixel light value, b represents the intercept of the night light intensity value, represents the average value of all years, represents the average value of the night light intensity of this single pixel in n years, ε i represents the random error, and i represents the year.

[0068] When a is greater than 0, it indicates that the change of the pixel light value shows an upward trend; when a is less than 0, it indicates that the change of the pixel light value shows a downward trend; when a is equal to 0, it indicates that the pixel light value does not change.

[0069] Optionally, based on the urban vacancy rate index and the night light change trend, the distribution of housing vacancy areas in the target area is obtained, including:

[0070] Based on a preset first vacancy classification strategy and the urban vacancy rate index, the vacancy degree level of each pixel in the night light image is obtained;

[0071] Based on a preset second vacancy classification strategy and the night light value change trend, the potential vacancy level of each pixel in the night light image is obtained;

[0072] Based on the vacancy degree level and the potential vacancy level, the distribution of housing vacancy areas in the target area is obtained.

[0073] In this embodiment provided by the present application, based on the preset first vacancy classification strategy and the second vacancy classification strategy, the vacancy degree levels and potential vacancy levels of the pixels corresponding to the urban vacancy rate index and the change trend of the night light value are respectively determined, and the housing vacancy area distribution of the target area is obtained based on the level parameters of these two dimensions of the vacancy degree level and the potential vacancy level. The considered factors are more comprehensive, so that the recognition accuracy of the vacant houses in the target area can be improved.

[0074] The second vacancy classification strategy is shown in Table 1. When the change trend of the pixel light value is less than -0.1, the potential vacancy level of the corresponding pixel is high; when the change trend of the pixel light value is greater than or equal to -0.1 and less than or equal to 0.1, the potential vacancy level of the corresponding pixel is medium; when the change trend of the pixel light value is greater than 0.1, the potential vacancy level of the corresponding pixel is low.

[0075] Table 1

[0076] Potential vacancy level Classification criteria High a<-0.1 Medium -0.1≤a≤0.1 Low a>0.1

[0077] Optionally, based on the preset first vacancy classification strategy and the urban vacancy rate index, the vacancy degree levels of each pixel in the night light image are obtained, including:

[0078] The urban vacancy rate index is classified using the mean-standard deviation to obtain one standard deviation;

[0079] The calculation formula of one standard deviation is as follows:

[0080]

[0081] Where, represents the average value of the urban vacancy rate index of each pixel in the night light image, x i represents the urban vacancy rate index of the i-th pixel in the night light image, n represents the number of pixels in the night light image, and σ represents one standard deviation;

[0082] Based on the preset first vacancy classification strategy, one standard deviation and the urban vacancy rate index, the vacancy degree levels of the pixels in the night light image corresponding to the urban vacancy rate index are determined.

[0083] In this embodiment provided by the present application, the first vacancy classification strategy is to divide the urban vacancy rate index into four vacancy degree levels as shown in Table 2 with one standard deviation as the reference value. In Table 2, when , the vacancy degree level of the corresponding pixel is high; when , the vacancy degree level of the corresponding pixel is relatively high; when , the vacancy degree level of the corresponding pixel is relatively low; when When the vacancy degree level of the corresponding pixel is low. The areas with high and very high vacancy degree levels are regarded as the existing housing vacancy areas.

[0084] Table 2

[0085]

[0086] Optionally, based on the vacancy degree level and the potential vacancy level, the distribution of housing vacancy areas in the target area is obtained, including:

[0087] Based on the preset vacancy area division strategy and the vacancy degree level and potential vacancy level of each pixel in the night light image, the housing area type of each pixel is obtained;

[0088] Based on the areas formed by the pixels corresponding to the same housing area type, a regional distribution position of the target area is formed, and the distribution of housing vacancy areas in the target area is obtained.

[0089] In this embodiment provided by the present application, based on the preset vacancy area division strategy and the vacancy degree level and potential vacancy level of each pixel in the night light image, the housing area type of each pixel is obtained, and based on the areas formed by the pixels corresponding to the same housing area type, a regional distribution position of the target area is formed, and the distribution of housing vacancy areas in the target area is obtained. In this way, by combining the two dimensions of housing vacancy influencing factors, namely the housing vacancy degree and the population loss trend of the target area, the real housing vacancy distribution of the target area is dynamically predicted, so that the recognition timeliness of the housing vacancy distribution is higher, thereby improving the recognition accuracy of the vacant houses in the target area, and further realizing the accurate division of housing vacancy areas.

[0090] The vacancy area division strategy is to superimpose the vacancy degree level and the potential vacancy level of each pixel in the night light image, so as to superimpose the existing housing vacancy areas and the potential vacancy areas, and the housing area type of each pixel is obtained as shown in Table 3. In Table 3, when the vacancy degree level is high / higher and the potential vacancy level is high / medium, the housing area type of the corresponding pixel is the housing vacancy area; when the vacancy degree level is low / lower and the potential vacancy level is medium / low, the housing area type of the corresponding pixel is the densely populated area; when the vacancy degree level is high / higher and the potential vacancy level is low, or when the vacancy degree level is low / lower and the potential vacancy level is high, the housing area type of the corresponding pixel is the mixed area.

[0091] Table 3

[0092]

[0093] A method for identifying housing vacancy areas in this application is based on the data of nighttime light images of the target area. By improving the application methods of the urban vacancy rate index and the linear regression model for nighttime light images, it can accurately identify existing and potential housing vacancy areas, dynamically capture the impact of population loss on housing vacancy, significantly improve the accuracy of housing vacancy area identification and dynamic monitoring capabilities, break through the limitations of traditional methods in capturing spatial dynamic changes and the restrictive effects of vacancy areas, and provide innovative technical support for the optimization of urban land use and sustainable development. It can be seen that the advantages of this application are as follows: (1) Solve the problem of insufficient accuracy in traditional housing vacancy area identification and significantly improve the accuracy of vacancy area identification; (2) The model makes full use of nighttime light data and the urban vacancy rate index to effectively capture the dynamic change characteristics of housing vacancy areas; (3) The housing vacancy area index can be used as a key constraint in the urban expansion simulation process. On the basis of accurately identifying existing and potential housing vacancy areas, it can more truly reflect the impact of population loss on the urban spatial pattern, significantly improve the accuracy and reliability of urban expansion simulation, and provide scientific support for urban land use planning.

[0094] Please refer to Figure 2 , Figure 2 which shows a housing vacancy area identification system according to an exemplary embodiment of this application. As Figure 2 shown, this application provides a housing vacancy area identification system 200, including:

[0095] An acquisition module 201, configured to acquire nighttime light images and urban land images of the target area;

[0096] A first analysis module 202, configured to perform light intensity analysis based on the nighttime light images and urban land images to obtain the urban vacancy rate index of the target area;

[0097] A second analysis module 203, configured to perform regression analysis using the nighttime light images to obtain the changing trend of nighttime light values in the target area;

[0098] A distribution situation determination module 204, configured to obtain the distribution situation of housing vacancy areas in the target area based on the urban vacancy rate index and the nighttime light change trend.

[0099] A housing vacancy area identification system according to this embodiment analyzes the light intensity of the night light image and the urban land image of the target area obtained by the acquisition module 201 through the first analysis module 202 to obtain the urban vacancy rate index of the target area. The urban vacancy rate index can measure the housing vacancy degree of the target area. Then, the second analysis module 203 uses the night light image for regression analysis to obtain the change trend of the night light value in the target area, which can dynamically capture the population loss trend. Finally, the distribution situation determination module 204 obtains the housing vacancy distribution situation in the target area based on the urban vacancy rate index and the night light change trend. In this way, by combining the two dimensions of housing vacancy influencing factors, namely the housing vacancy degree and the population loss trend of the target area, the real housing vacancy distribution situation in the target area is dynamically predicted, making the identification of the housing vacancy distribution situation more immediate, thereby improving the identification accuracy of the vacant houses in the target area.

[0100] Optionally, the night light image includes the current night image; the first analysis module is specifically configured to:

[0101] Use the urban land image to divide the night light image into non-residential area pixels and residential area pixels;

[0102] Based on the pixel information of the non-residential area pixels and the residential area pixels, obtain the true light intensity of the residential area pixels;

[0103] Based on the urban land image and the true light intensity, calculate the first urban vacancy rate index of the residential area pixels, and determine that the second urban vacancy rate index of the non-residential area pixels is 0;

[0104] Based on the first urban vacancy rate index and the second urban vacancy rate index, form the urban vacancy rate index of the target area.

[0105] Optionally, the first analysis module is specifically configured to:

[0106] Overlay the urban land image and the night light image for data calculation to obtain the urban land occupation ratio of each pixel in the night light image; wherein, the pixel size of the night light image is larger than the pixel size of the urban land image;

[0107] Use the pixels in the night light image with an urban land occupation ratio less than the preset value as non-residential area pixels;

[0108] Use the pixels in the night light image with an urban land occupation ratio greater than or equal to the preset value as residential area pixels.

[0109] Optionally, the first analysis module is specifically configured to:

[0110] Obtain the first original gray value and the first number of pixels of non-residential area pixels, and obtain the second original gray value of residential area pixels;

[0111] Use the first original gray value, the first number of pixels and the second original gray value to calculate the true light intensity of residential area pixels.

[0112] Optionally, the night light image includes a plurality of consecutive historical night images; the second analysis module is specifically configured to:

[0113] Perform linear regression analysis on pixels with the same position information in a plurality of consecutive historical night images to obtain the pixel linear regression coefficient corresponding to the position information;

[0114] Take the pixel linear regression coefficient as the change trend of the pixel light value corresponding to the position information;

[0115] Form the change trend of the night light value of the target area based on the change trends of a plurality of pixel light values.

[0116] Optionally, the distribution situation determination module is specifically configured to:

[0117] Based on the preset first vacancy classification strategy and the urban vacancy rate index, obtain the vacancy degree level of each pixel in the night light image;

[0118] Based on the preset second vacancy classification strategy and the change trend of the night light value, obtain the potential vacancy level of each pixel in the night light image;

[0119] Based on the vacancy degree level and the potential vacancy level, obtain the distribution situation of the housing vacancy area in the target area.

[0120] Optionally, the distribution situation determination module is specifically configured to:

[0121] Based on the preset vacancy area division strategy and the vacancy degree level and potential vacancy level of each pixel in the night light image, obtain the housing area type of each pixel;

[0122] Based on the area formed by pixels corresponding to the same housing area type to form a regional distribution position of the target area, obtain the distribution situation of the housing vacancy area in the target area.

[0123] It should be noted that a housing vacancy area identification system provided by the above embodiments and a housing vacancy area identification method provided by the above embodiments belong to the same concept. The specific manners in which each module and unit perform operations have been described in detail in the method embodiments, and will not be elaborated here. In practical applications, for the housing vacancy area identification system provided by the above embodiments, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the system is divided into different functional modules to complete all or part of the functions described above, and this will not be limited here either.

[0124] A computing device according to an embodiment of the present application includes a memory, a processor, and a program stored on the memory and running on the processor. When the processor executes the program, it implements some or all of the steps of the above housing vacancy area identification method.

[0125] Among them, the computing device can be a computer. Correspondingly, its program is computer software, and the parameters and steps in the computing device of the present application can refer to the parameters and steps in the embodiments of the housing vacancy area identification method described above, and will not be elaborated here.

[0126] A computer-readable storage medium according to an embodiment of the present application stores instructions that, when running, execute the steps of the above housing vacancy area identification method.

[0127] Among them, the computer-readable storage medium can be a transient computer-readable storage medium or a non-transient computer-readable storage medium.

[0128] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes one or more instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method of the embodiment of the present disclosure. The aforementioned computer-readable storage medium can be a non-transient computer-readable storage medium, including: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk, or an optical disc that can store program codes, or it can also be a transient computer-readable storage medium.

[0129] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, as well as the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0130] Those skilled in the art know that the present application can be implemented as a system, a method, or a computer program product. Therefore, the present disclosure can be specifically implemented in the following forms: it can be entirely hardware, or entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, which is generally referred to as a "module" or "system" in this article. In addition, in some embodiments, the present application can also be implemented in the form of a computer program product in one or more computer-readable media, which contains computer-readable program code. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above.

[0131] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0132] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for identifying vacant housing areas, characterized in that: include: Acquire nighttime light images and urban land images of the target area; Performing light intensity analysis based on the nighttime light image and the urban land image to obtain an urban vacancy rate index of the target area; Using the nighttime light image to perform regression analysis, obtaining a nighttime light value variation trend of the target area; Based on the urban vacancy rate index and the nighttime light change trend, the distribution of housing vacancy areas in the target area is obtained.

2. The method according to claim 1, characterized in that The nighttime light image includes a current nighttime image; and performing light intensity analysis based on the nighttime light image and the urban land image to obtain the urban vacancy rate index of the target area includes: Using the urban land image, the nighttime light image is divided into pixels to obtain non-residential area pixels and residential area pixels; Based on the pixel information of the non-residential area pixel and the residential area pixel, obtaining the real light intensity of the residential area pixel; Based on the urban land image and the real light intensity, a first urban vacancy rate index of the residential area pixel is calculated, and a second urban vacancy rate index of the non-residential area pixel is determined to be 0; The urban vacancy rate index of the target area is formed based on the first urban vacancy rate index and the second urban vacancy rate index.

3. The method according to claim 2, characterized in that The nighttime light image is obtained by dividing the nighttime light image into pixels by using the urban land image to obtain non-residential area pixels and residential area pixels, including: The urban land image and the night light image are superimposed to calculate the urban land area ratio of each pixel in the night light image; wherein the pixel size of the night light image is larger than the pixel size of the urban land image; Pixels in the nighttime light image where the urban land area ratio is less than a preset value are regarded as non-residential pixels; The pixels in the night light image whose urban area ratio is greater than or equal to the preset value are regarded as residential area pixels.

4. The method according to claim 2, characterized in that: The method of obtaining the real light intensity of the residential area pixel by using the pixel information of the non-residential area pixel and the residential area pixel comprises: Obtaining a first original grayscale value and a first pixel quantity of the non-residential area pixel, and obtaining a second original grayscale value of the residential area pixel; The actual light intensity of the pixels in the residential area is calculated using the first original grayscale value, the first number of pixels, and the second original grayscale value.

5. The method according to any one of claims 1 to 4, characterized in that: The nighttime light image includes a plurality of continuous historical nighttime images; the step of performing regression analysis using the nighttime light image to obtain a nighttime light value variation trend of the target area includes: Performing linear regression analysis on pixels with the same position information in a plurality of consecutive historical nighttime images to obtain linear regression coefficients of pixels corresponding to the position information; Taking the pixel linear regression coefficient as the pixel light value change trend corresponding to the position information; The nighttime light value variation trend of the target area is formed based on the light value variation trends of multiple pixels.

6. The method according to any one of claims 1 to 4, characterized in that: The obtaining of the distribution of housing vacancy areas in the target area based on the urban vacancy rate index and the nighttime light change trend includes: Based on a preset first vacancy classification strategy and the urban vacancy rate index, obtaining a vacancy level grade of each pixel in the night light image; Based on a preset second vacancy classification strategy and the nighttime light value change trend, a potential vacancy level of each pixel in the nighttime light image is obtained; Based on the vacancy level and the potential vacancy level, the distribution of housing vacancy areas in the target area is obtained.

7. The method according to claim 6, characterized in that The step of obtaining the distribution of housing vacancy areas in the target area based on the vacancy level and the potential vacancy level includes: Based on a preset vacant area division strategy and the vacancy level and the potential vacancy level of each pixel in the night light image, obtaining the housing area type of each pixel; An area distribution position of the target area is formed based on an area composed of pixels corresponding to the same housing area type, and the distribution of housing vacancy areas in the target area is obtained.

8. A housing vacant area identification system, characterized in that: include: An acquisition module is used to acquire nighttime light images and urban land images of a target area; A first analysis module is used to perform light intensity analysis based on the night light image and the urban land image to obtain an urban vacancy rate index of the target area; A second analysis module is used to perform regression analysis using the nighttime light image to obtain a trend of nighttime light value changes in the target area; A distribution determination module is used to obtain the distribution of housing vacancy areas in the target area based on the urban vacancy rate index and the nighttime light change trend.

9. A computing device comprising a memory, a processor, and a program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the steps of a housing vacancy area identification method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed on a terminal device, the terminal device executes the steps of a housing vacancy area identification method as described in any one of claims 1 to 7.

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