A method and device for extracting urban connectivity network and calculating urban connectivity strength

Abstract

The disclosed embodiment provides a method and device for extracting a city connectivity network and calculating city connectivity strength, wherein the method includes: obtaining a night light image, wherein the night light image includes night light remote sensing data of an area of ​​interest, wherein the area of ​​interest includes multiple target areas; determining a light path extraction area in the night light image based on road data of the area of ​​interest; performing light path extraction on the light path extraction area based on pixel values ​​of the light path extraction area to obtain a city connectivity network, wherein the city connectivity network includes multiple light paths between the target areas, and the light paths are paths connecting the night lights of the target areas. The method can extract a city connectivity network based on the night light image, thereby objectively analyzing the city connectivity strength based on the city connectivity network.

Description

Technical Field

[0001] The present disclosure relates to the field of remote sensing technology, and in particular to a method and device for extracting a city connectivity network and calculating city connection strength. Background Art

[0002] In recent years, with the development of information technology and the rise of the network society, there has been an increasing number of studies using Internet big data such as capital flow, passenger flow, information flow and technology flow to measure the flow of factors between regions, accurately analyze the relationship between cities and measure the intensity of urban connections.

[0003] However, these Internet big data are difficult to collect and the data quality is uneven, especially when it comes to cross-departmental or cross-regional data sharing. Some key data may be difficult to obtain. At the same time, data from different sources may vary greatly in accuracy, update frequency and completeness. It is difficult to use these data to measure the strength of urban connections.

[0004] In addition, some methods obtain urban connectivity indicator data based on government reports, industry statistics, and corporate annual reports. Due to the inconsistency of the dimensions of different data sources, various types of data need to be standardized. For example, the number of migrant population and passenger traffic are standardized according to the total population or city size of the city. The preliminary processing work is very cumbersome, and the indicator selection and weight determination process may be subject to the subjective influence of researchers or experts, resulting in less objective results.

[0005] There is still a lack of a method to quickly, efficiently and accurately estimate the intensity of urban connections. Summary of the invention

[0006] In view of this, the embodiments of the present disclosure provide at least one method and device for extracting a city connectivity network and calculating city connection strength.

[0007] Specifically, the embodiments of the present disclosure are implemented through the following technical solutions:

[0008] In a first aspect, a method for extracting a city connectivity network is provided, the method comprising:

[0009] Acquire a night light image, wherein the night light image includes night light remote sensing data of an area of ​​interest, wherein the area of ​​interest includes a plurality of target areas;

[0010] Determine a light path extraction area in the night light image according to the road data of the area of ​​interest;

[0011] Based on the pixel value of the light pathway extraction area, light pathway extraction is performed on the light pathway extraction area to obtain a city connectivity network, wherein the city connectivity network includes multiple light pathways between the target areas, and the light pathways are paths connecting the night lights of the target areas.

[0012] In combination with any embodiment provided by the present disclosure, before determining the light path extraction area in the night light image according to the road data of the area of ​​interest, the method further includes:

[0013] Local image enhancement and global image enhancement are performed on the night light image to obtain an image-enhanced night light image.

[0014] In combination with any of the embodiments provided in the present disclosure, the local image enhancement of the night light image includes:

[0015] Calculating the local entropy of the night light image;

[0016] Adjusting the contrast of the night light image based on the local entropy and the adjustment coefficient;

[0017] Based on the local logarithmic enhancement algorithm, the adjusted luminous image is stretched.

[0018] In combination with any embodiment provided in the present disclosure, the step of determining the light path extraction area in the night light image according to the road data of the area of ​​interest includes:

[0019] According to the road data of the area of ​​interest, the night light image is cropped to obtain an initial extraction area;

[0020] Determining a valid road extraction threshold according to the pixel value distribution of the initial extraction area;

[0021] A light path extraction area in the initial extraction area is determined according to the effective road extraction threshold.

[0022] In combination with any of the embodiments provided in the present disclosure, the night light image includes image data of multiple different gain modes;

[0023] The step of determining a valid road extraction threshold according to the pixel value distribution of the initial extraction area includes:

[0024] Determine a valid road extraction threshold corresponding to each gain mode according to the pixel value distribution of the initial extraction area in the image data of each gain mode;

[0025] The step of determining the light path extraction area in the initial extraction area according to the effective road extraction threshold comprises:

[0026] Determine, according to the effective road extraction threshold corresponding to the gain mode, an effective path extraction area in the initial extraction area corresponding to the gain mode;

[0027] The light path extraction area in the night light image is determined according to the intersection of the effective path extraction areas corresponding to the multiple gain modes.

[0028] In combination with any of the embodiments provided in the present disclosure, the step of extracting light pathways from the light pathway extraction area based on pixel values ​​of the light pathway extraction area to obtain a city connectivity network includes:

[0029] Based on the pixel values ​​of the light path extraction area, skeleton processing is performed on the light path extraction area to obtain path spatial distribution data, wherein the path spatial distribution data includes a skeleton for characterizing night light distribution;

[0030] Based on the boundaries of multiple regions in the region of interest, the skeleton in the spatial distribution data of the pathway is interrupted to obtain multiple segments of the pathway;

[0031] Traversing the plurality of paths to identify a target path connecting the target area;

[0032] The adjacent target pathways are reorganized to obtain the light pathway;

[0033] Based on a plurality of said light pathways, a city connectivity network is obtained.

[0034] In a second aspect, a method for calculating city connection strength is provided, the method comprising:

[0035] Acquire a night light image and a city connectivity network, wherein the city connectivity network is obtained by the city connectivity network extraction method described in any of the above embodiments, and the night light image includes night light remote sensing data of an area of ​​interest, and the area of ​​interest includes multiple target areas;

[0036] The city connection strength between the target areas is determined according to the pixel values ​​of the light pathways in the city connection network in the night light image.

[0037] In combination with any embodiment provided by the present disclosure, the city connection strength is the resource flow strength, and determining the city connection strength between the target areas according to the pixel value of the light path in the city connection network in the night light image includes:

[0038] For each light path in the urban connectivity network, determining the resource flow intensity of the light path according to the pixel values ​​of the pixels that the light path passes through in the night light image;

[0039] The resource flow intensity between the target areas is determined according to the resource flow intensity of the light pathways between the target areas.

[0040] In a third aspect, a city connectivity network extraction device is provided, the device comprising:

[0041] A data acquisition module, used to acquire night light images, wherein the night light images include night light remote sensing data of an area of ​​interest, wherein the area of ​​interest includes a plurality of target areas;

[0042] An area determination module, used for determining a light path extraction area in the night light image according to the road data of the area of ​​interest;

[0043] A path extraction module is used to extract light paths in the light path extraction area based on the pixel values ​​of the light path extraction area to obtain a city connectivity network, wherein the city connectivity network includes multiple light paths between the target areas, and the light paths are paths connecting the night lights in the target areas.

[0044] In a fourth aspect, a device for calculating city connection strength is provided, the device comprising:

[0045] An acquisition module, used for acquiring night light images and a city connectivity network, wherein the city connectivity network is obtained by the city connectivity network extraction method described in any of the above embodiments, and the night light images include night light remote sensing data of an area of ​​interest, and the area of ​​interest includes multiple target areas;

[0046] The calculation module is used to determine the urban connection strength between the target areas according to the pixel values ​​of the light pathways in the urban connection network in the night light image.

[0047] The city connectivity network extraction method provided by the embodiment of the present disclosure extracts light pathways from night light images through image processing technology to obtain a city connectivity network. The city connectivity network can be used to analyze city connectivity relationships, especially to estimate the strength of city connections. Different from other methods, this method uses unified night light image data to extract the city connectivity network, which greatly reduces the difficulty of data acquisition and does not require data dimension adjustment and standardization. At the same time, this method does not need to introduce subjective influences from experts, and can objectively analyze the city's mobility based on the obtained city connectivity network, thereby achieving an objective, rapid, efficient and accurate estimation of the city's connectivity strength. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in one or more embodiments of the present disclosure or related technologies, the drawings required for use in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in one or more embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0049] Figure 1is a flow chart of a method for extracting a city connectivity network according to at least one embodiment of the present disclosure;

[0050] Figure 2 is a flow chart of a method for calculating city connection strength shown in at least one embodiment of the present disclosure;

[0051] Figure 3 It is a flowchart of a method for extracting a city connectivity network and calculating city connectivity strength according to at least one embodiment of the present disclosure;

[0052] Figure 4 is a schematic diagram of a city connectivity network shown in at least one embodiment of the present disclosure

[0053] Figure 5 is a block diagram of a city connectivity network extraction device shown in at least one embodiment of the present disclosure;

[0054] Figure 6 is a block diagram of a device for calculating city connection strength shown in at least one embodiment of the present disclosure;

[0055] Figure 7 It is a schematic diagram of the hardware structure of an electronic device shown in at least one embodiment of the present disclosure. DETAILED DESCRIPTION

[0056] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with this specification. Instead, they are merely examples of devices and methods consistent with some aspects of this specification as detailed in the appended claims.

[0057] The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit this specification. The singular forms "a", "the" and "the" used in this specification and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0058] It should be understood that although the terms first, second, third, etc. may be used in this specification to describe various information, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this specification, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0059] Urban connectivity intensity is a concept that describes the degree of interconnection between cities. The strength of urban connectivity directly affects regional economic development, cooperation and competition between cities, and the formation and evolution of urban agglomerations. It involves multiple aspects such as economy, society, culture, and transportation.

[0060] In order to estimate the urban connection strength objectively and efficiently, the inventors paid attention to the application of remote sensing technology in the field of urban analysis.

[0061] As the main technical means of analyzing urban agglomerations, Earth observation technology uses various sensors carried by aerospace vehicles and various ground platforms to conduct various detection activities on the earth's environment and human activities within which human beings live. It has the advantages of high temporal and spatial resolution and fast update frequency. It can effectively obtain data and information on urban land use, spatial structure description, thermal environment inversion, impervious surface extraction, etc.; at the same time, Earth observation technology also has great application potential in estimating urban population, social economy and other indicators, and has achieved remarkable results in estimating urban electricity consumption, population and GDP (Gross DomesticProduct) indicators.

[0062] Night light remote sensing is an optical remote sensing technology that can detect nighttime dim light. The night light remote sensing images collected by this remote sensing technology are referred to as night light images.

[0063] Night light images objectively reflect the society's industrial production, commercial activities and energy consumption levels by capturing visible light radiation sources such as city lights, night fishing boats, and forest fires, and introduce population activities and socio-economic relations into remote sensing information. Currently, a large number of studies have proved that there is a high correlation between night light data and human activities by evaluating parameters such as light area, light distribution, and light intensity, and have great application potential in detecting urban spatial network relationships.

[0064] Taking into account that night light images can obtain information that daytime remote sensing cannot obtain, and that artificial light sources in urban areas are the main source of stable bright light at night, especially the lighting sources on roads connecting various regions can reflect the degree of mutual connection between regions, the present disclosure proposes a method for extracting urban connectivity networks and calculating urban connectivity strength. The urban connectivity network is extracted based on night-time low-light remote sensing data. The urban connectivity network characterizes the connection relationship between target areas. The night-time light intensity of the light pathways in the urban connectivity network is used to calculate the connection strength between target areas. No artificial scoring factors are introduced, which is objective. In addition, due to the use of remote sensing images, data acquisition is easy.

[0065] The following first describes the city connectivity network extraction method provided by the present invention. The extracted city connectivity network can be used to estimate the city connection strength, and can also be used to study and analyze other urban issues, such as energy consumption, total freight volume, and population estimation.

[0066] The target area in this embodiment is the area whose urban connection strength or other urban problems are to be analyzed. This embodiment does not limit the administrative area or geographical area referred to by the target area. It can be a city, township, urban district, etc., or it can be an area with different geographical locations, such as northern Jiangsu, southern Jiangsu, northeastern Jiangsu, northwestern Jiangsu, and central and southern Jiangsu.

[0067] This embodiment does not limit the number of target regions, and the target regions may be geographically adjacent regions, such as Nanjing and Zhenjiang, or geographically unconnected regions, such as Beijing, Shijiazhuang and Zhengzhou.

[0068] The region of interest includes multiple target regions, and in some embodiments, may also include regions adjacent to the target region. In the case where the target region is not geographically connected, the region of interest may also include regions connected to the target region. The specific region of interest may be determined according to actual research needs. For example, in the case where the target region is Beijing and Shijiazhuang, since Beijing and Shijiazhuang are not directly connected geographically, in addition to Beijing and Shijiazhuang, other cities connected to Beijing and Shijiazhuang may also be determined as regions of interest, such as Tianjin and Baoding.

[0069] Road data contains the location areas of roads, where "roads" refer to linear infrastructure used for transportation. They are channels connecting different target areas for people and vehicles to pass through.

[0070] For example, road data may include the following types: highways and expressways, including main roads within cities and expressways connecting cities; urban roads, which are the road systems within cities, including streets, main roads, secondary roads and alleys; rural roads, which are roads connecting rural or remote areas; small roads and walking paths; special-purpose roads, such as internal roads in industrial areas, mine roads, etc.; railways; waterways, etc.

[0071] like Figure 1 As shown, Figure 1 is a flow chart of a method for extracting a city connectivity network according to at least one embodiment of the present disclosure, and the method may include the following steps:

[0072] In step 102, a night light image is acquired.

[0073] Among them, night light images contain remote sensing data of nighttime lights in areas of interest.

[0074] This embodiment does not limit the method of acquiring the night light image.

[0075] Night light images can be collected by nighttime low-light sensors, such as DMSP / OLS (Defense Meteorological Satellite Program / Operational Linescan System) sensors, VIIRS (Visible Infrared Imaging Radiometer Suite) sensors, and SDGSAT-1 (Sustainable Development Science Satellite 1) sensors.

[0076] The night-light images collected by night-time low-light sensors will have a "light spillover effect", which refers to the phenomenon that in night-light images, brightness information diffuses from the actual light source position to its surrounding area. This effect causes the observation range of city lights to appear larger in the image than the actual range, affecting the accuracy of the image and the analysis results.

[0077] In one embodiment, in order to reduce the light spillover effect of the night light image and enhance its image quality in low-brightness areas, thereby improving the quality of light path extraction in subsequent processing, the night light image may be subjected to image enhancement processing after being acquired. In other embodiments, the night light image acquired in this step may also be an image that has been subjected to image enhancement processing.

[0078] In one example, the image enhancement process may be to perform local image enhancement and global image enhancement on the night light image to obtain an image enhanced night light image.

[0079] In this example, local image enhancement is performed first and then global image enhancement. Local enhancement helps to make fine adjustments to the brightness and contrast of different areas, which can effectively reduce the impact of light spillover and avoid overexposure of highlight areas due to direct overall enhancement. In addition, night light images usually contain high-contrast light sources and low-brightness dark details. Local enhancement can enhance the details of the dark area without affecting the bright area, thereby better preserving the overall image hierarchy and details during subsequent global enhancement. Direct global image enhancement may cause overexposure of bright areas, loss of dark details, and even significantly amplify noise. Performing local enhancement first can lay a good foundation for global enhancement, making the enhancement effect more natural and balanced.

[0080] This example does not limit the methods used for local image enhancement and global image enhancement. For example, local image enhancement may be performed by local brightness adjustment, local gamma correction, etc., and global image enhancement may be performed by histogram equalization, contrast stretching, etc.

[0081] In one embodiment, before image enhancement, the acquired night light image may be subjected to data preprocessing, such as outlining the region of interest based on the boundary of the target area to be extracted and cropping the night light image to reduce the workload of subsequent image processing such as image enhancement; for example, the night light image may be subjected to de-striping processing to reduce striping artifacts and improve image quality.

[0082] In step 104, a light path extraction area in the night light image is determined based on the road data of the area of ​​interest.

[0083] In this embodiment, the road data of the area of ​​interest can be obtained from map data, for example, various map databases.

[0084] The resolution of the road data can match the resolution of the night light image to facilitate the determination of the light path extraction area. Exemplarily, the map data used can be Open Street Map, which is a free, open source, and editable map database created and maintained by volunteers around the world. This example can obtain the OpenStreet Map data in China and clip the data according to the boundary of the target area to be extracted. When the resolution of the night light image is 10m, the data can be rasterized to obtain road raster data with a resolution of 10m.

[0085] The light path extraction area is an area where there is a night light source on the road in the area of ​​interest. The night light source can be a street lamp, a car lamp or other light source, which is used for subsequent light path extraction.

[0086] In this step, the night light image can be cropped based on the road data of the area of ​​interest, and the area where the road is located in the night light image can be determined as the light path extraction area; or the road area in the road data can be mapped to the night light image based on the road data of the area of ​​interest to determine the light path extraction area.

[0087] Since some roads are not used at night, that is, there is no light source in the area where some roads are located, in order to determine a more effective light path extraction area, in one embodiment, determining the light path extraction area in the night light image according to the road data of the area of ​​interest includes:

[0088] According to the road data of the area of ​​interest, the night light image is cropped to obtain an initial extraction area;

[0089] Determining a valid road extraction threshold according to the pixel value distribution of the initial extraction area;

[0090] A light path extraction area in the initial extraction area is determined according to the effective road extraction threshold.

[0091] Specifically, based on the road data of each area in the area of ​​interest, the night light image is cropped, and the area where the road is located in the night light image is determined as the initial extraction area.

[0092] The effective road extraction threshold is a pixel value dynamically acquired based on the distribution of pixel values ​​in the initial extraction area, such as a grayscale value. Its function is to screen the pixels in the initial extraction area according to their pixel values. The pixel value represents the light brightness in the area where the pixel is located. Pixels with pixel values ​​higher than the effective road extraction threshold can be considered to have strong light brightness and can be used as effective light path extraction areas. Pixels with pixel values ​​lower than the effective road extraction threshold can be considered to have too low light brightness, which is likely to be noise and other interference information that occurs during image acquisition, and cannot be used as effective light path extraction areas.

[0093] This embodiment does not limit the method for determining the effective road extraction threshold. In one example, the distribution of pixel values ​​of each pixel in the initial extraction area may be counted, and after discarding background value data with a pixel value of 0, the lowest 10% of pixel values ​​in the counted data are used as the effective road extraction threshold. In other examples, the lowest 15% of pixel values ​​in the counted data may also be used as the effective road extraction threshold.

[0094] In order to further improve the accuracy of the light path extraction area, the light path extraction area can be determined by combining multiple night light images with different gain modes; in one embodiment, the night light image used can include image data of multiple different gain modes; the effective road extraction threshold is determined according to the pixel value distribution of the initial extraction area, including:

[0095] According to the pixel value distribution of the initial extraction area in the image data of each gain mode, the effective road extraction threshold corresponding to the gain mode is determined.

[0096] The step of determining the light path extraction area in the initial extraction area according to the effective road extraction threshold comprises:

[0097] Determine, according to the effective road extraction threshold corresponding to the gain mode, an effective path extraction area in the initial extraction area corresponding to the gain mode;

[0098] The light path extraction area in the night light image is determined according to the intersection of the effective path extraction areas corresponding to the multiple gain modes.

[0099] Among them, the gain mode refers to the different amplification levels used by the sensor under different lighting conditions. Image data of different gain modes are images captured when the sensor is set in different gain modes. For example, image data of multiple different gain modes may include: low-gain image data, which refers to the data collected by the sensor under low gain settings. By reducing the gain, the bright details in the image can be preserved; high-gain image data, which refers to the data collected by the sensor under high gain settings, which can still capture more details in low light conditions; high-low gain image data, which combines high-gain and low-gain image data, with the purpose of retaining both bright and dark details in the same image.

[0100] Since the gain modes of the sensor are set to different levels, the pixel value distributions in the image data of different gain modes are different. In this embodiment, for the image data of each gain mode, the distribution of the pixel values ​​of the initial extraction area is statistically analyzed to determine the effective road extraction threshold corresponding to the gain mode. For example, when there are three gain modes, three effective road extraction thresholds are determined respectively.

[0101] For each gain mode, data is screened for pixels in the initial extraction area of ​​the gain mode according to the effective road extraction threshold of the gain mode, and the effective path extraction area corresponding to the gain mode is determined to obtain multiple effective path extraction areas. The overlapping parts of the effective path extraction areas corresponding to these different gain modes are more likely to be valid data. Therefore, the intersection of these effective path extraction areas is determined as the light path extraction area in the night light image.

[0102] In other examples, the intersection and union may be used as the light path extraction area in the night light image, so that the light path extraction area covers a more comprehensive range.

[0103] In step 106, based on the pixel values ​​of the light pathway extraction area, light pathway extraction is performed on the light pathway extraction area to obtain a city connectivity network.

[0104] The city connection network includes a plurality of light pathways between the target areas, and the light pathways are paths connecting the night lights of the target areas.

[0105] Considering that the pixel value of the area where the light source is located in the light path extraction area is higher than the pixel value of the area without the light source, the light path can be extracted according to the pixel value. This embodiment does not limit the method of extracting the light path. For example, the light path extraction area can be binarized first, and then the center line of the binarized image can be extracted, and the extracted center line is used as the light path.

[0106] In addition to the light pathways connecting the target area, there may be some irrelevant pathways in the light pathway extraction area, such as pathways connecting the target area and non-target areas. For example, when the two target areas are not geographically adjacent, the area of ​​interest includes other areas other than the target area, and these areas may have some pathways irrelevant to the target area. That is, there are night lights irrelevant to the connection relationship with the target area in the light pathway extraction area. In order to remove the influence of night lights irrelevant to the target area, in one embodiment, this step may include the following processing:

[0107] Based on the pixel values ​​of the light path extraction area, skeleton processing is performed on the light path extraction area to obtain path spatial distribution data, wherein the path spatial distribution data includes a skeleton for characterizing night light distribution;

[0108] Based on the boundaries of multiple regions in the region of interest, the skeleton in the spatial distribution data of the pathway is interrupted to obtain multiple segments of the pathway;

[0109] Traversing the plurality of paths to identify a target path connecting the target area;

[0110] The adjacent target pathways are reorganized to obtain the light pathway;

[0111] Based on a plurality of said light pathways, a city connectivity network is obtained.

[0112] Exemplarily, the obtained light path extraction area can be binarized, and based on the Python language, the binarized data can be skeletonized to obtain a skeleton with a certain width as the path spatial distribution data.

[0113] The skeleton is interrupted based on the boundaries of multiple regions in the area of ​​interest, where the multiple regions include target regions and non-target regions, and the interrupted path information is traversed to identify the path data with a boundary connection relationship with the target region as the target path, and the target paths of any two adjacent segments are optionally reorganized to convert the paths passing through multiple regions into paths passing through the target region. For example, when there are three or more cities in the area of ​​interest and the target regions to be analyzed are two of them, the paths passing through multiple cities are converted into paths passing through two cities, and a city connection network is constructed based on this.

[0114] The city connectivity network extraction method provided by the embodiment of the present disclosure extracts light pathways from night light images through image processing technology to obtain a city connectivity network, and the city connectivity network can be used to estimate the city connectivity strength. Different from other methods, this method extracts the city connectivity network with unified night light image data, which greatly reduces the difficulty of data acquisition and does not require data dimension adjustment and standardization. At the same time, this method does not need to introduce subjective influence of experts, and can objectively analyze the city's mobility capacity based on the obtained city connectivity network, thereby achieving objective, rapid, efficient and accurate estimation of the city's connectivity strength.

[0115] After the steps of the above embodiment, the city connection strength between target areas can be determined according to the pixel values ​​of the light paths in the city connection network in the night light image. The city connection strength calculation method provided by the present disclosure is described below.

[0116] like Figure 2 As shown, Figure 2 is a flow chart of a method for calculating city connection strength according to at least one embodiment of the present disclosure, and the method may include the following steps:

[0117] In step 202, night light images and city connectivity networks are acquired.

[0118] Among them, the urban connectivity network is obtained by the urban connectivity network extraction method of any of the above embodiments, and the night light image can be the night light image obtained in step 102 of the above embodiment. The night light image contains night light remote sensing data of the area of ​​interest, and the area of ​​interest includes multiple target areas.

[0119] In step 204, the city connection strength between the target areas is determined according to the pixel values ​​of the light pathways in the city connection network in the night light image.

[0120] In this embodiment, the city connection strength is used to indicate the closeness of the connection between the target area, and specifically may be the resource flow intensity.

[0121] The pixel value of the light pathway in the night light image represents the intensity of the night light in the light pathway. The higher the pixel value corresponding to the light pathway between the target areas, the stronger the night light, the busier the road traffic on the light pathway, and the closer the connection between the target areas. Conversely, the lower the pixel value corresponding to the light pathway, the weaker the night light, the idling of the road traffic on the light pathway, and the looser the connection between the target areas.

[0122] This embodiment does not limit the specific method of determining the city connection strength between target areas based on the pixel values ​​of the light paths in the night light image. For example, the average value of all pixel values ​​of all light paths in the city connection network in the night light image can be taken to represent the city connection strength, the sum of all pixel values ​​of all light paths in the city connection network in the night light image can be used to represent the city connection strength, and the city connection strength can also be determined by combining data such as the number of light paths and the width of the light paths.

[0123] In one embodiment, the night light image can be radiometrically calibrated to convert the pixel value of each pixel in the night light image into radiant brightness to better characterize the night light intensity. Then, the urban connection strength between target areas can be determined based on the radiant brightness of the light pathways in the urban connectivity network in the night light image.

[0124] In one embodiment, this step may be:

[0125] For each light path in the urban connectivity network, determining the resource flow intensity of the light path according to the pixel values ​​of the pixels that the light path passes through in the night light image;

[0126] The resource flow intensity between the target areas is determined according to the resource flow intensity of the light pathways between the target areas.

[0127] In this embodiment, the resource flow intensity of each light path can be determined first, and then the resource flow intensity between target areas can be determined. The average value of the pixel values ​​passed by each light path in the night light image can be taken to characterize the resource flow intensity of the light path, and then the resource flow intensity of each light path can be used to determine the resource flow intensity between target areas, which can be specifically taken as an average value, a sum value, etc.

[0128] In one example, LPB and CS indicators were constructed to characterize the intensity of resource flow delivered by the lighting pathway and the intensity of resource flow between target areas.

[0129] For each lighting path L, we have:

[0130]

[0131] Among them, Li represents the radiance of the i-th pixel in the light path L, and N represents the sum of the number of pixels that the light path passes through. For every two target areas m and n, there are:

[0132]

[0133] Among them, j represents the jth lighting pathway connecting area m and area n, and N represents the total number of lighting pathways connecting city m and city n.

[0134] The city connection strength calculation method in this embodiment is based on the city connectivity network, and can achieve fast, efficient, accurate and objective estimation of city connection strength.

[0135] The following is a more detailed description of the method for extracting the city connectivity network and calculating the city connectivity strength in conjunction with a specific embodiment. Figure 3 shown.

[0136] The night light image used in this embodiment is a nighttime low light load image, using the SDGSAT-1GLI load panchromatic band 4-level standard product data. SDGSAT-1 4-level data is a product that has been orthorectified based on ground control points and digital elevation models and output in a format specification after relative radiation correction, band registration, HDR fusion, RPC processing, and orthorectification. The road data used is Open Street Map road data. The target area analyzed is a city.

[0137] The specific steps include:

[0138] Step 1: Data preprocessing

[0139] This step preprocesses the night light images and Open Street Map road data based on Python language and remote sensing image processing software. For night light images, the preprocessing steps include data destriping and data cropping; for OpenStreet Map road data, the preprocessing steps include data cropping and data rasterization.

[0140] The SDGSAT-1GLI payload panchromatic band image data in this embodiment includes three types of products, namely low-gain image data products (PL), high-gain image data products (PH) and high-low gain image data (HDR) products. This embodiment performs de-striping on the acquired SDGSAT-1GLI payload level 4 standard product data based on Python language to obtain PH, PL and HDR, a total of three bands with a resolution of 10m, and outlines the area of ​​interest according to the city boundary to be extracted, and crops the image.

[0141] This embodiment obtains Open Street Map data in China and clips the data according to the city boundaries to be extracted. The Open Street Map data is vector data, which is rasterized to obtain road raster data with a resolution of 10m.

[0142] Step 2: Image Enhancement

[0143] This step adopts an image enhancement method for urban night low-light images. This method combines local image enhancement technology with global image enhancement technology, which can effectively reduce the light spillover effect of night-light images, enhance the image quality in low-brightness areas, and effectively enhance the image extraction quality of light paths.

[0144] (1) Local image enhancement.

[0145] When performing local image enhancement on a night light image in this embodiment: calculating the local entropy of the night light image; adjusting the contrast of the night light image based on the local entropy and the adjustment coefficient; and performing image stretching on the adjusted night light image based on a local logarithmic enhancement algorithm.

[0146] This embodiment constructs an image enhancement method that combines local entropy processing and local logarithmic stretching. The method can effectively highlight areas with higher brightness in the image, reduce the light spillover effect, and retain detailed information of areas with lower brightness.

[0147] First, perform local entropy enhancement on the night light image. For example, a spherical structure element with a radius of 3 can be used to calculate the local entropy of the night light image. represents the local neighborhood with the center point (x, y). Then the calculation result of its local entropy E(x, y) is:

[0148] E(x,y)=-∑ i p i logp i (3)

[0149] Among them, p i is the gray value of pixel i in the neighborhood The probability of appearing in .

[0150] Based on the local entropy of the acquired night light image, an adjustment coefficient α is used to adjust the image contrast. The adjusted image I ′ The calculation formula for (x,y,k) is:

[0151] I ′ (x,y,k)= I(x,y,k)·exp(α·E(x,y)) (4)

[0152] Among them, I ′(x, y, k) and I(x, y, k) represent the pixel values ​​of the kth band of the enhanced night light image and the input night light image at the position (x, y), respectively, k = PH, PL, HDR, and the adjustment coefficient α = 1.5.

[0153] Based on the acquired local entropy enhanced image, the local logarithmic enhancement algorithm is used to further stretch the image and distinguish the foreground pixels from the background pixels. The calculation formula is as follows:

[0154]

[0155] Among them, O(x, y, k) is the image pixel value after local enhancement, μ=1, and c is the variance of the image structure element with a radius of 3.

[0156] In the above image processing process, local entropy processing and local logarithmic stretching can both enhance images adaptively by region, which is suitable for images with non-uniform illumination. In night-light images with uneven illumination, this combination can ensure that bright areas are not overexposed and dark areas have clear details, resulting in a more balanced visual effect. In addition, local logarithmic stretching will enhance darker areas to a greater extent, while enhancing high-brightness areas less, thereby improving the dynamic range of the image and enriching the layering of the picture. Combined with local entropy processing, it can further balance the grayscale distribution of different areas, so that both dark and bright details are improved, effectively reducing the light spillover effect of night-light images.

[0157] (2) Global image enhancement.

[0158] Based on the locally enhanced night-light image, this embodiment performs global histogram normalization enhancement on the entire night-light image to further enhance the image contrast in a balanced manner. The grayscale value of the balanced image is:

[0159] I equalized (x,y,k)=round(CDF norm ( I(x,y,k))×(L-1)) (6)

[0160] Among them, round is the rounding function, CDF norm is the cumulative distribution function of the image grayscale histogram, and L is the number of image grayscale levels.

[0161] Step 3: Dynamically determine the image segmentation threshold and extract global pathway information

[0162] This embodiment designs a multi-band dynamic threshold segmentation method based on the road data of each city to determine the segmentation threshold of the image (i.e., the effective road extraction threshold), and performs information extraction processing on the image based on the segmentation threshold to obtain the city road distribution data.

[0163] (1) Multi-band dynamic threshold segmentation method

[0164] Firstly, based on the rasterized Open Street Map data, the enhanced image was cropped and mapped, and the distribution data of night lights (i.e., pixel values) on OpenStreet Map roads in different bands (i.e., image data in three different gain modes, PH, PL, and HDR) were counted respectively. After discarding the background value data, the distribution data of the bottom 10% of the statistical data were obtained as the effective road extraction threshold, and the data in the PH, PL, and HDR bands were screened and determined respectively, and the intersection of the three-band data was taken as the effective light pathway extraction range.

[0165] (2) Global pathway extraction

[0166] The obtained light pathway extraction range is binarized, and based on the Python language, the binarized image is skeletonized and the centerline extracted to obtain the spatial distribution data of the city's pathways.

[0167] Step 4: Extraction and strength calculation of urban connectivity network

[0168] Based on the acquired spatial distribution data of pathways in the entire region, this step adopts the identification and reorganization processing of multi-city connection pathways, interrupts and reorganizes the spatial distribution data of pathways in multiple cities, obtains the processed spatial distribution data of pathways between cities, and extracts and calculates the intensity of the urban connectivity network based on the acquired spatial distribution data of pathways between cities and the pre-processed night light images.

[0169] (1) Identification and reorganization of multi-city connection pathways

[0170] Based on the acquired spatial distribution data of pathways between cities and the city boundary data in the road data, this step can be based on the Python language, and the pathway data passing through three or more cities (that is, the area of ​​interest contains three or more cities) are selected, and the spatial distribution data of the pathways are interrupted based on the city boundaries, and the interrupted pathway information is traversed to identify pathway data with boundary connection relationships, and the pathway data of any two adjacent segments are optionally reorganized, and the pathways passing through multiple cities are converted into pathways passing through two cities, so that they are more in line with the geographical connection relationship, and a city connection network is constructed based on this.

[0171] (2) Path strength calculation

[0172] Based on the reorganized urban connection network and the pre-processed night light images, the LPB and CS indicators can be used in this step to characterize the resource flow intensity transmitted by the light pathway and the resource flow intensity between cities.

[0173] like Figure 4 As shown, Figure 4 A schematic diagram of a city connectivity network extracted based on the method of the above embodiment is shown, wherein different intensity values ​​of resource flow intensity transmitted by each light channel are represented by different colors.

[0174] Compared with other methods for calculating the strength of urban connections, this embodiment uses night-light images and image processing technology to extract urban connectivity networks and estimate urban connectivity strength. Different from other methods, this method uses unified night-light image data to extract and estimate connectivity, which greatly reduces the difficulty of data acquisition and does not require data dimension adjustment and standardization. At the same time, this method does not need to introduce subjective influences from experts, objectively analyzes urban mobility, and achieves rapid, efficient, and accurate estimation of urban connectivity strength. Extracting urban connection pathways and estimating urban connectivity strength can intuitively display the coordinated and cooperative relationship between cities, effectively tap into the collaborative relationship between cities, regulate the coordinated development of urban areas, optimize regional resource allocation, build an open and efficient innovative resource sharing network, and promote the shared development and green development of urban agglomerations.

[0175] like Figure 5 As shown, Figure 5 is a block diagram of a city connectivity network extraction device shown in at least one embodiment of the present disclosure, the device comprising:

[0176] A data acquisition module 51 is used to acquire night light images, wherein the night light images include night light remote sensing data of an area of ​​interest, wherein the area of ​​interest includes a plurality of target areas;

[0177] An area determination module 52, for determining a light path extraction area in the night light image according to the road data of the area of ​​interest;

[0178] The path extraction module 53 is used to extract the light path in the light path extraction area based on the pixel value of the light path extraction area to obtain a city connectivity network, wherein the city connectivity network includes multiple light paths between the target areas, and the light paths are paths connecting the night lights in the target areas.

[0179] In some embodiments, the data acquisition module 51 is further used to perform local image enhancement and global image enhancement on the night light image to obtain an image-enhanced night light image.

[0180] In some embodiments, when the data acquisition module 51 is used to perform local image enhancement on the night light image, it is specifically used to:

[0181] Calculating the local entropy of the night light image;

[0182] Adjusting the contrast of the night light image based on the local entropy and the adjustment coefficient;

[0183] Based on the local logarithmic enhancement algorithm, the adjusted luminous image is stretched.

[0184] In some embodiments, the region determination module 52 is configured to:

[0185] According to the road data of the area of ​​interest, the night light image is cropped to obtain an initial extraction area;

[0186] Determining a valid road extraction threshold according to the pixel value distribution of the initial extraction area;

[0187] A light path extraction area in the initial extraction area is determined according to the effective road extraction threshold.

[0188] In some embodiments, the night light image includes image data of a plurality of different gain modes;

[0189] The region determination module 52, when used to determine the effective road extraction threshold according to the pixel value distribution of the initial extraction region, is specifically used to:

[0190] Determine a valid road extraction threshold corresponding to each gain mode according to the pixel value distribution of the initial extraction area in the image data of each gain mode;

[0191] The area determination module 52, when used to determine the light path extraction area in the initial extraction area according to the effective road extraction threshold, is specifically used to:

[0192] Determine, according to the effective road extraction threshold corresponding to the gain mode, an effective path extraction area in the initial extraction area corresponding to the gain mode;

[0193] The light path extraction area in the night light image is determined according to the intersection of the effective path extraction areas corresponding to the multiple gain modes.

[0194] In some embodiments, the path extraction module 53 is specifically used to:

[0195] Based on the pixel values ​​of the light path extraction area, skeleton processing is performed on the light path extraction area to obtain path spatial distribution data, wherein the path spatial distribution data includes a skeleton for characterizing night light distribution;

[0196] Based on the boundaries of multiple regions in the region of interest, the skeleton in the spatial distribution data of the pathway is interrupted to obtain multiple segments of the pathway;

[0197] Traversing the plurality of paths to identify a target path connecting the target area;

[0198] The adjacent target pathways are reorganized to obtain the light pathway;

[0199] Based on a plurality of said light pathways, a city connectivity network is obtained.

[0200] like Figure 6 As shown, Figure 6 is a block diagram of a city connection strength calculation device shown in at least one embodiment of the present disclosure, and the device also includes:

[0201] An acquisition module 61 is used to acquire night light images and a city connectivity network, wherein the city connectivity network is obtained by the city connectivity network extraction method described in any of the above embodiments, and the night light images include night light remote sensing data of an area of ​​interest, and the area of ​​interest includes multiple target areas;

[0202] The calculation module 62 is used to determine the urban connection strength between the target areas according to the pixel values ​​of the light pathways in the urban connection network in the night light image.

[0203] In some embodiments, the calculation module 62 is specifically used to: for each light pathway in the urban connectivity network, determine the resource flow intensity of the light pathway according to the pixel values ​​of the pixels through which the light pathway passes in the night light image; and determine the resource flow intensity between the target areas according to the resource flow intensity of the light pathways between the target areas.

[0204] The implementation process of the functions and effects of each module in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, which will not be repeated here.

[0205] The present disclosure also provides an electronic device, such as Figure 7 As shown, the electronic device includes a memory 71 and a processor 72, wherein the memory 71 is used to store computer instructions that can be executed on the processor, and the processor 72 is used to implement the method of urban connectivity network extraction and / or urban connection strength calculation described in any embodiment of the present disclosure when executing the computer instructions.

[0206] The embodiments of the present disclosure also provide a computer program product, which includes a computer program / instruction. When the computer program / instruction is executed by a processor, the method for extracting a city connectivity network and / or calculating a city connection strength described in any embodiment of the present disclosure is implemented.

[0207] The embodiments of the present disclosure also provide a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for extracting a city connectivity network and / or calculating a city connection strength described in any embodiment of the present disclosure is implemented.

[0208] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this specification. A person of ordinary skill in the art can understand and implement it without paying creative labor.

[0209] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0210] Those skilled in the art will readily appreciate other embodiments of the specification after considering the specification and practicing the invention claimed herein. The specification is intended to cover any variations, uses or adaptations of the specification that follow the general principles of the specification and include common knowledge or customary techniques in the art that are not claimed in the specification. The specification and examples are to be considered exemplary only, and the true scope and spirit of the specification are indicated by the following claims.

[0211] It should be understood that the present description is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present description is limited only by the appended claims.

[0212] The above description is only a preferred embodiment of this specification and is not intended to limit this specification. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this specification should be included in the scope of protection of this specification.

Claims

1. A method for extracting a city connectivity network, characterized in that: The method comprises: Acquire a night light image, wherein the night light image includes night light remote sensing data of an area of ​​interest, wherein the area of ​​interest includes a plurality of target areas; Determine a light path extraction area in the night light image according to the road data of the area of ​​interest; Based on the pixel values ​​of the light pathway extraction area, the light pathway extraction area is skeletonized to obtain pathway spatial distribution data, and the pathway spatial distribution data includes a skeleton for characterizing night-time light distribution; based on the boundaries of multiple regions in the region of interest, the skeleton in the pathway spatial distribution data is interrupted to obtain multiple pathways; the multiple pathways are traversed to identify target pathways connecting the target regions; adjacent target pathways are reorganized to obtain light pathways; based on multiple light pathways, an urban connectivity network is obtained; the urban connectivity network includes multiple light pathways between the target regions, and the light pathways are paths of night-time lights connecting the target regions.

2. The method according to claim 1, characterized in that Before determining the light path extraction area in the night light image according to the road data of the area of ​​interest, the method further includes: Local image enhancement and global image enhancement are performed on the night light image to obtain an image-enhanced night light image.

3. The method according to claim 2, characterized in that The locally enhancing the night light image comprises: Calculating the local entropy of the night light image; Adjusting the contrast of the night light image based on the local entropy and the adjustment coefficient; Based on the local logarithmic enhancement algorithm, the adjusted luminous image is stretched.

4. The method according to claim 1, characterized in that: The step of determining the light path extraction area in the night light image according to the road data of the area of ​​interest includes: According to the road data of the area of ​​interest, the night light image is cropped to obtain an initial extraction area; Determining a valid road extraction threshold according to the pixel value distribution of the initial extraction area; A light path extraction area in the initial extraction area is determined according to the effective road extraction threshold.

5. The method according to claim 4, characterized in that The night light image includes image data of multiple different gain modes; The step of determining a valid road extraction threshold according to the pixel value distribution of the initial extraction area includes: Determine a valid road extraction threshold corresponding to each gain mode according to the pixel value distribution of the initial extraction area in the image data of each gain mode; The step of determining the light path extraction area in the initial extraction area according to the effective road extraction threshold comprises: Determine, according to the effective road extraction threshold corresponding to the gain mode, an effective path extraction area in the initial extraction area corresponding to the gain mode; The light path extraction area in the night light image is determined according to the intersection of the effective path extraction areas corresponding to the multiple gain modes.

6. A method for calculating city connection strength, characterized in that: The method comprises: Acquire a night light image and a city connectivity network, wherein the city connectivity network is obtained by the city connectivity network extraction method according to any one of claims 1 to 5, wherein the night light image comprises night light remote sensing data of an area of ​​interest, and the area of ​​interest comprises a plurality of target areas; The city connection strength between the target areas is determined according to the pixel values ​​of the light pathways in the city connection network in the night light image.

7. The method according to claim 6, characterized in that The city connection strength is the resource flow strength. The city connection strength between the target areas is determined according to the pixel value of the light path in the city connection network in the night light image, including: For each light path in the city connection network, determining the resource flow intensity of the light path according to the pixel values ​​of the pixels passed by the light path in the night light image; The resource flow intensity between the target areas is determined according to the resource flow intensity of the light pathways between the target areas.

8. A city connection network extraction device, characterized in that: The device comprises: A data acquisition module, used to acquire night light images, wherein the night light images include night light remote sensing data of an area of ​​interest, wherein the area of ​​interest includes a plurality of target areas; An area determination module, used for determining a light path extraction area in the night light image according to the road data of the area of ​​interest; A pathway extraction module is used to skeletonize the light pathway extraction area based on the pixel values ​​of the light pathway extraction area to obtain pathway spatial distribution data, wherein the pathway spatial distribution data includes a skeleton for characterizing nighttime light distribution; based on the boundaries of multiple regions in the region of interest, the skeleton in the pathway spatial distribution data is interrupted to obtain multiple pathways; the multiple pathways are traversed to identify target pathways connecting the target regions; adjacent target pathways are reorganized to obtain light pathways; based on multiple light pathways, an urban connectivity network is obtained; the urban connectivity network includes multiple light pathways between the target regions, and the light pathways are paths connecting the nighttime lights of the target regions.

9. A device for calculating city connection strength, characterized in that: The device comprises: An acquisition module, used for acquiring night light images and a city connectivity network, wherein the city connectivity network is obtained by the city connectivity network extraction method according to any one of claims 1 to 5, wherein the night light images include night light remote sensing data of an area of ​​interest, and the area of ​​interest includes a plurality of target areas; The calculation module is used to determine the urban connection strength between the target areas according to the pixel values ​​of the light pathways in the urban connection network in the night light image.

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