Urban densification pattern detection method and system

Abstract

The present application relates to the field of urban planning technology, and in particular to a method and system for detecting urban densification patterns, the method comprising: dividing a city into blocks based on a traffic network provided by an electronic map, using blocks as the minimum analysis unit, and identifying the main functional attributes of each block; obtaining a two-dimensional structural representation and a three-dimensional height representation of each block; resampling the two-dimensional structural representation and the three-dimensional height representation of each block to the same resolution and unifying the spatial benchmark; assigning different weights to the two-dimensional structural representation and the three-dimensional height representation of each block according to the main functional attributes of each block; constructing a building density index as a representation of urban land densification utilization based on the two-dimensional structural representation and the three-dimensional height representation of each block and the weights assigned thereto; obtaining building density indices for multiple periods, performing time series trend analysis on the building density indices for the multiple periods through a linear regression model, and judging the current urban densification pattern based on the analysis results.

Description

Technical Field

[0001] The present application relates to the field of urban planning technology, and in particular to a method and system for detecting urban densification patterns. Background Art

[0002] In the context of global urbanization, changes in urban landscapes are primarily manifested in outward expansion and densification. Densification, the structural transformation within existing urban areas, manifests itself as an increase in the density of impervious surfaces and the replacement of low-rise buildings with high-rise structures. It is considered an effective means of improving land use efficiency and reducing land carbon emissions. Therefore, timely monitoring of the spatiotemporal characteristics of urban densification is crucial for sustainable urban development and urban planning and management.

[0003] Currently, multi-source remote sensing provides multi-dimensional spatiotemporal data for urban densification monitoring, offering an effective approach for large-scale, low-cost characterization of urban densification processes. Compared to monitoring city-wide pixel-level transformations (inter-class changes), sub-pixel classification not only addresses the issue of mixed pixels but also facilitates quantitative analysis of the internal characteristics of the same land class, making it crucial for accurately capturing the temporal dynamics of re-densification. However, these methods typically characterize urban densification processes in two dimensions and lack detailed three-dimensional landscape information, limiting their ability to accurately depict the longitudinal density growth of cities. Summary of the Invention

[0004] In order to at least to some extent overcome the problem that most of the characterizations of urban densification processes in related technologies are based on two-dimensional space and lack a detailed description of the longitudinal density growth of cities, the present application provides an urban densification pattern detection method and system.

[0005] The scheme of this application is as follows:

[0006] According to a first aspect of an embodiment of the present application, a method for detecting an urban densification pattern is provided, comprising:

[0007] Divide the city's blocks based on the transportation network provided by the electronic map, using the block as the minimum analysis unit to identify the main functional attributes of each block;

[0008] Obtain a 2D structural representation and a 3D height representation of each block;

[0009] Resample the 2D structural representation and 3D height representation of each block to the same resolution to unify the spatial benchmark;

[0010] Different weights are assigned to the two-dimensional structural representation and three-dimensional height representation of each block according to its main functional attributes;

[0011] Based on the two-dimensional structural representation and three-dimensional height representation of each block and the weights assigned to them, a building density index is constructed as a representation of urban land densification.

[0012] Obtain the building density index of multiple periods, conduct time series trend analysis on the building density index of multiple periods through linear regression model, and judge the current urban densification pattern based on the analysis results.

[0013] Preferably, obtaining a two-dimensional structural representation of each block includes:

[0014] Obtain high-resolution multispectral image data of the city;

[0015] The abundance value of each landscape element in the city is calculated based on the high-resolution multispectral image data.

[0016] Preferably, calculating the area ratio of each landscape element in the high-resolution multispectral image includes:

[0017] Based on the linear spectral mixture decomposition model, the abundance value of the end member corresponding to each landscape element was calculated by the fully constrained least squares method.

[0018] Preferably, the landscape elements include:

[0019] High-reflectivity objects, low-reflectivity objects, vegetation and soil.

[0020] Preferably, obtaining a three-dimensional height representation of each block includes:

[0021] Obtain radar image data of the city;

[0022] The building heights of the city are calculated based on the radar image data.

[0023] Preferably, calculating the building heights of a city based on the radar image includes:

[0024] The urban building height is defined as the average height of the grid within the block;

[0025] Constructing a VVH index to represent the three-dimensional building heights of the city through dual-polarization backscatter coefficients VV and VH in the radar image;

[0026] Build a logarithmic transformation of building height model;

[0027] The VVH index is input into the building height model to obtain the building heights of the city.

[0028] Preferably, before inputting the VVH index into the building height model, the method further comprises:

[0029] The building height model is calibrated based on the ascending orbit data and the descending orbit data respectively.

[0030] Preferably, different weights are assigned to the two-dimensional structural representation and three-dimensional height representation of each block according to the main functional attributes of each block, including:

[0031] The weights of the two-dimensional structural representation and three-dimensional height representation of each block are determined based on the frequency density and proportion index of POIs (Point of Interest) with different functions in each block.

[0032] Preferably, a time series trend analysis of the building density index over multiple periods is performed using a linear regression model, including:

[0033] Based on the SG smoothed time series Y, the linear regression model is used to analyze the time series trend of the building density index over multiple periods.

[0034] The significance of the time series trend was verified by the Mann-Kendall (MK) test.

[0035] According to a second aspect of an embodiment of the present application, a system for detecting an urban densification pattern is provided, comprising:

[0036] processor and memory;

[0037] The processor and the memory are connected via a communication bus:

[0038] The processor is configured to call and execute the program stored in the memory;

[0039] The memory is used to store a program, and the program is at least used to execute an urban densification pattern detection method as described in any one of the above items.

[0040] The technical solution provided by this application may have the following beneficial effects:

[0041] This technical solution comprehensively utilizes two-dimensional structural representation and three-dimensional height representation. Compared with traditional methods based on two-dimensional remote sensing images, it can more comprehensively capture changes in urban spatial structure, especially vertical information such as building height, and more accurately reflect the actual situation of urban densification.

[0042] By incorporating the primary functional attributes of blocks and assigning them different weights, the building density index can be made more consistent with actual urban planning logic. For example, the densification needs and characteristics of commercial, residential, and industrial areas differ significantly, and this differentiated weighting improves the model's explanatory power and adaptability.

[0043] Resample 2D and 3D data to a unified resolution to achieve spatial alignment of different data sources, improve spatial consistency after data fusion, and provide a reliable basis for subsequent analysis.

[0044] As a quantitative indicator of urban densification, the building density index combines spatial structure and height information to reflect land use intensity and the degree of spatial development, becoming an important measurement tool for urban spatial development. Using the building density index over multiple periods combined with a linear regression model for time series trend analysis can identify patterns of urban densification (such as rapid densification, steady densification, stable states, and declining states), providing a basis for urban planning and management, assisting in the formulation of scientific spatial expansion or redevelopment strategies, and supporting decision-making for urban planning, resource management, and sustainable development.

[0045] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0047] Figure 1 This is a flow chart of a method for detecting urban densification patterns provided by one embodiment of the present application;

[0048] Figure 2 This is a structural diagram of an urban densification pattern detection system provided in one embodiment of the present application.

[0049] Reference numerals: processor-21; memory-22. DETAILED DESCRIPTION

[0050] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0051] Example 1

[0052] Figure 1 This is a flow chart of a method for detecting urban densification patterns provided by an embodiment of the present application, with reference to Figure 1 , a method for detecting urban densification patterns, comprising:

[0053] S11: Divide the city into blocks based on the transportation network provided by the electronic map, using the block as the minimum analysis unit and identifying the main functional attributes of each block;

[0054] In practice, the traffic network provided by Open Street Map can be used to divide the city into blocks, with blocks as the smallest analysis unit. Combined with the categories and densities of commercial, residential, industrial, etc. provided by Points of Interest (POIs), the main functional attributes of the blocks can be identified.

[0055] S12: Obtain the two-dimensional structural representation and three-dimensional height representation of each block;

[0056] Obtain a two-dimensional structural representation of each block, including:

[0057] Obtain high-resolution multispectral image data of the city;

[0058] The abundance values ​​of various landscape elements in the city are calculated based on high-resolution multispectral image data.

[0059] In actual practice, high-resolution multispectral image data of cities can be obtained through Landsat-8, Sentinel-2, etc.

[0060] It should be noted that urban mixed space is generally composed of four basic landscape elements, namely high-reflectivity landforms, low-reflectivity landforms, vegetation and soil.

[0061] High-albedo features refer to buildings and roads primarily made of concrete, cement, metal, and glass. Low-albedo features are primarily concentrated in old urban areas dominated by older buildings and in heavy industrial areas with severe dust pollution. Vegetation includes green vegetation in urban parks and other green areas. Bare land is scattered throughout cities, including vacant land with low vegetation cover, or areas where soil has been exposed due to building demolition.

[0062] Calculate the area proportion of each landscape feature in high-resolution multispectral imagery, including:

[0063] Based on the linear spectral mixture decomposition model, the abundance value of the end member corresponding to each landscape element was calculated by the fully constrained least squares method.

[0064] The ultimate goal of spectral mixture decomposition is to obtain the abundance value of end members. The general linear spectral mixture decomposition model expression is:

[0065] ;

[0066] Among them, i is the remote sensing image band, is the spectral reflectance of the pixel in band i, m is the number of end members, for each end member j (1≤j≤m), is the reflectivity of end member j in band i, is the area percentage of end member j in the pixel, that is, the abundance value, is the residual between the observed emissivity and the model fitted reflectivity.

[0067] Taking into account that the endmember abundance values ​​estimated under actual conditions have actual physical significance, the fully constrained least squares method is generally used to solve the abundance value of each endmember in the pixel, and finally obtain the abundance value image of the corresponding endmember.

[0068] The constraints added by the fully constrained least squares method are:

[0069] .

[0070] Obtain a 3D height representation of each block, including:

[0071] Obtain radar image data of the city;

[0072] Calculate building heights in a city based on radar imagery data.

[0073] In actual practice, radar image data of cities can be obtained through SAR, InSAR and other methods.

[0074] Calculate building heights in cities based on radar images, including:

[0075] The urban building height is defined as the average height of the grid within the block;

[0076] The VVH index is constructed to represent the three-dimensional building height of the city through the dual-polarization backscatter coefficients VV and VH in the radar image;

[0077] Build a logarithmic transformation of building height model;

[0078] Input the VVH index into the building height model to obtain the building height of the city.

[0079] It should be noted that the dual-polarization backscatter coefficients VV and VH are common parameters in radar remote sensing data, especially in imaging radar systems such as synthetic aperture radar (SAR). The following is a basic overview and meaning of them:

[0080] VV polarization (Vertical transmit - Vertical receive): vertical polarization transmission and vertical polarization reception, that is, the transmitting and receiving antennas are both polarized in the vertical direction.

[0081] VH Polarization (Vertical transmit - Horizontal receive): Vertically polarized transmission and horizontally polarized reception. That is, the transmitting antenna is vertically polarized, and the receiving antenna is horizontally polarized.

[0082] The VVH metric is generally based on the backscatter coefficients of the VV and VH polarization channels, reflecting their relative or combined relationship. Its purpose is to utilize the complementary information of dual polarization to enhance sensitivity to different scattering characteristics.

[0083] In this embodiment, the dual-polarization backscatter coefficients VV and VH in the radar image are used to construct the VVH index to represent the three-dimensional building heights in the city as follows:

[0084] ;

[0085] is the relative influence parameter of VH polarization on VVH, which is initially set to 5 based on prior knowledge.

[0086] Model the logarithmic transformation of building heights:

[0087] ;

[0088] Where H is the logarithmic transformation value of the building height, and a, b, and c are the parameters to be estimated.

[0089] It should be noted that before inputting the VVH index into the building height model, the method also includes:

[0090] The building height model is calibrated based on ascending and descending data respectively.

[0091] In this example, the building height model was calibrated based on both ascending and descending data. For the ascending data, a=-2.16, b=-0.45, and c=4.78; for the descending data, a=-0.61, b=-0.79, and c=2.31.

[0092] S13: Resample the 2D structural representation and 3D height representation of each block to the same resolution and unify the spatial reference;

[0093] The two-dimensional structural representation and three-dimensional height representation of each block were resampled to the same resolution, the spatial benchmark was unified, and the block scale was used as the basic unit for index calculation and analysis.

[0094] S14: Assign different weights to the 2D structural representation and 3D height representation of each block based on its main functional attributes;

[0095] S15: Based on the two-dimensional structural representation and three-dimensional height representation of each block and the weights assigned to them, a building density index is constructed as a representation of urban land densification;

[0096] The expression of building density index is:

[0097] × +β× +γ× ;

[0098] in, is constructed for the building density index, 、 are the mean impervious surface abundance at the block scale and the normalized maximum impervious surface abundance in the study area, respectively; 、 are the mean background vegetation and soil abundance at the block scale and the normalized maximum background vegetation and soil abundance in the study area, respectively; 、 are the mean building height at the block scale and the normalized maximum building height in the study area, respectively; α, β, and γ are dynamic adjustment parameters, which are determined according to the type of different urban functional areas where the block is located.

[0099] Among them, the abundance of impervious surface is the sum of the abundance of high albedo and ground albedo features.

[0100] In this embodiment, the weights of the two-dimensional structural representation and the three-dimensional height representation of each block are determined according to the frequency density and proportion index of POIs with different functions in each block.

[0101] For example, the commercial center is dominated by vertical intensity, with α=0.4 and β=0.6; the industrial area is dominated by plane expansion, with α=0.4 and β=0.6; and the residential area is a balanced development type, with α=0.5 and β=0.5.

[0102] S16: Obtain building density indexes for multiple periods, perform time series trend analysis on the building density indexes for multiple periods using a linear regression model, and determine the current urban densification pattern based on the analysis results.

[0103] Specifically include:

[0104] Based on the SG smoothed time series Y, the linear regression model is used to analyze the time series trend of the building density index over multiple periods.

[0105] The significance of the time series trend was verified by the MK test.

[0106] In this embodiment, a linear regression model is selected to analyze the time series trend. The linear regression model is an existing model, and its expression is as follows:

[0107] ;

[0108] in, is the sequence number of the SG smoothed time series Y ( ), N is the length of the time series signal (N=30), is the SG smoothed time series Y value at i, Slope is the rate of change of the SG smoothed time series Y. If Slope>0, it means that the trend of the time series signal is increasing, otherwise it is decreasing.

[0109] Because the nonparametric MK test has no restrictions on the distribution of samples and is not significantly affected by outliers, this method is often not used to test trends in ecological and environmental time series. Therefore, this technical solution uses the MK test method based on time series trend detection to verify the significance of time series trends.

[0110] The MK test can determine whether there is a mutation in the time series, and if so, it can determine the time when the mutation occurred.

[0111] The null hypothesis (H0) of the nonparametric MK test is that the time series samples all come from a population with independent realizations and that these samples are equally distributed, while the alternative hypothesis (H1) is that the time series exhibits a monotonic trend. For a time series Y with N independent samples, for any two points k in the time series, j ≤ N. If k ≠ j, then the test statistic S is defined as:

[0112] ;

[0113] Among them, the sgn function can be defined as:

[0114] ;

[0115] Since the statistic S of the MK test method obeys the normal distribution and E[S]=0, the variance of S Var(S) can be defined as:

[0116] ;

[0117] Where p is the parallel array in the time series Y, t j is the frequency of the p-value in the parallel array. Since the statistic S of the MK test follows a normal distribution, we can perform a Z transformation on it to obtain:

[0118] ;

[0119] Through the MK test method, when Z is greater than 0 and when the absolute value of Z is greater than 2.576, the densification pattern of the current city is judged to be rapid densification; when Z is greater than 0 and when the absolute value of Z is greater than 1.96, the densification pattern of the current city is judged to be steady densification; when Z is greater than 0 and when the absolute value of Z is less than 1.96 (1.645), the densification pattern of the current city is judged to be stable; when Z is less than 0, the trend of the time series is downward, and the densification pattern of the current city is judged to be recessionary.

[0120] This technical solution comprehensively utilizes two-dimensional structural representation and three-dimensional height representation. Compared with traditional methods based on two-dimensional remote sensing images, it can more comprehensively capture changes in urban spatial structure, especially vertical information such as building height, and more accurately reflect the actual situation of urban densification.

[0121] By incorporating the primary functional attributes of blocks and assigning them different weights, the building density index can be made more consistent with actual urban planning logic. For example, the densification needs and characteristics of commercial, residential, and industrial areas differ significantly, and this differentiated weighting improves the model's explanatory power and adaptability.

[0122] Resample 2D and 3D data to a unified resolution to achieve spatial alignment of different data sources, improve spatial consistency after data fusion, and provide a reliable basis for subsequent analysis.

[0123] As a quantitative indicator of urban densification, the building density index combines spatial structure and height information to reflect land use intensity and the degree of spatial development, becoming an important measurement tool for urban spatial development. Using the building density index over multiple periods combined with a linear regression model for time series trend analysis can identify patterns of urban densification (such as rapid densification, steady densification, stable states, and declining states), providing a basis for urban planning and management, assisting in the formulation of scientific spatial expansion or redevelopment strategies, and supporting decision-making for urban planning, resource management, and sustainable development.

[0124] Example 2

[0125] Figure 2 This is a schematic diagram of a system for detecting urban densification patterns provided by an embodiment of the present application, with reference to Figure 2 , an urban densification pattern detection system, comprising:

[0126] processing,21 and memory,22;

[0127] The processor 21 and the memory 22 are connected via a communication bus:

[0128] The processor 21 is used to call and execute the program stored in the memory 22;

[0129] The memory 22 is used to store a program, and the program is used to execute at least one of the urban densification pattern detection methods in the above embodiments.

[0130] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0131] It should be noted that, in the description of this application, the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. In addition, in the description of this application, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0132] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0133] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0134] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0135] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0136] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0137] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0138] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for detecting urban densification patterns, characterized in that: include: Divide the city's blocks based on the transportation network provided by the electronic map, using the block as the minimum analysis unit to identify the main functional attributes of each block; Obtain a 2D structural representation and a 3D height representation of each block; Resample the 2D structural representation and 3D height representation of each block to the same resolution to unify the spatial benchmark; Different weights are assigned to the two-dimensional structural representation and three-dimensional height representation of each block according to its main functional attributes; Based on the two-dimensional structural representation and three-dimensional height representation of each block and the weights assigned to them, a building density index is constructed as a representation of urban land densification. Obtain building density indices for multiple periods, conduct time series trend analysis on these indices using a linear regression model, and determine the current urban densification pattern based on the analysis results; Different weights are assigned to the 2D structural representation and 3D height representation of each block based on its main functional attributes, including: The weights of the two-dimensional structural representation and three-dimensional height representation of each block are determined based on the frequency density and proportion index of POIs with different functions in each block.

2. The method according to claim 1, characterized in that Obtain a two-dimensional structural representation of each block, including: Obtain high-resolution multispectral image data of the city; The abundance value of each landscape element in the city is calculated based on the high-resolution multispectral image data.

3. The method according to claim 2, characterized in that Calculating the area ratio of each landscape element in the high-resolution multispectral image, including: Based on the linear spectral mixture decomposition model, the abundance value of the end member corresponding to each landscape element was calculated by the fully constrained least squares method.

4. The method according to claim 2, characterized in that The landscape elements include: High-reflectivity objects, low-reflectivity objects, vegetation and soil.

5. The method according to claim 1, wherein Obtain a 3D height representation of each block, including: Obtain radar image data of the city; The building heights of the city are calculated based on the radar image data.

6. The method according to claim 5, characterized in that Calculating building heights in the city based on the radar image, including: The urban building height is defined as the average height of the grid within the block; Constructing a VVH index to represent the three-dimensional building heights of the city through the dual-polarization backscatter coefficients VV and VH in the radar image; Build a logarithmic transformation of building height model; The VVH index is input into the building height model to obtain the building heights of the city.

7. The method according to claim 6, characterized in that Before inputting the VVH index into the building height model, the method further includes: The building height model is calibrated based on the ascending orbit data and the descending orbit data respectively.

8. The method according to claim 1, characterized in that The time series trend analysis of the building density index over multiple periods was performed using a linear regression model, including: Based on the SG smoothed time series Y, the linear regression model is used to analyze the time series trend of the building density index over multiple periods. The significance of the time series trend was verified by the MK test.

9. An urban densification pattern detection system, characterized in that: include: processor and memory; The processor and the memory are connected via a communication bus: The processor is configured to call and execute the program stored in the memory; The memory is used to store a program, and the program is used to at least execute the urban densification pattern detection method described in any one of claims 1-8.

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