A method for evaluating the utilization efficiency of urban public service facilities based on high-resolution satellite data

Abstract

The present invention discloses a method for evaluating the utilization efficiency of urban public service facilities based on high-resolution satellite data, and relates to the field of satellite remote sensing technology. The method extracts the spatial scope of urban public service facilities through high-resolution satellite data; uses the acquired population location data to construct a model for the fluctuation of the flow of people in urban public service facilities in different time periods, and determines a reasonable flow interval; performs abnormal value detection on the flow of people in urban public service facilities; constructs an urban public service facility utilization rate evaluation model to evaluate the utilization rate of urban public service facilities. The present invention proposes a method for evaluating the utilization rate of urban public service facilities based on high-resolution satellite data. The result of the method is true and objective, avoiding the uncertainty generated by the reported data of urban public service facilities, and is helpful to truly and objectively grasp the opening status and utilization efficiency of urban public service facilities, and expand the application of satellite remote sensing in the fields of sports facility monitoring and supervision.

Description

Technical Field

[0001] The invention relates to the field of satellite remote sensing technology, and in particular to a method for evaluating the utilization efficiency of urban public service facilities based on high-resolution satellite data. Background Art

[0002] Remote sensing technology has the advantages and characteristics of wide range, long time series, large data volume and strong timeliness. High-resolution satellite remote sensing images truly and objectively reflect the spatial location and scope of urban public service facilities. Combined with population location data, it conducts monitoring and analysis of the openness of urban public service facilities, and provides technical support for scientific and efficient evaluation of the openness and utilization efficiency of urban public service facilities.

[0003] At present, the information on the openness of urban public service facilities is reported by local governments, and the authenticity of the data needs to be further verified. In response to the needs of relevant work on analyzing and assessing the reporting of information on the openness of urban public service facilities, an evaluation of the utilization efficiency of urban public service facilities based on high-resolution satellite data is carried out, which plays an important role in urging urban public service facilities to truthfully and accurately report information data and provide practical open services. Summary of the invention

[0004] The present invention discloses a method for evaluating the utilization efficiency of urban public service facilities based on high-resolution satellite data, and provides a relevant solution to the above-mentioned problem.

[0005] A method for evaluating the utilization efficiency of urban public service facilities based on high-resolution satellite data comprises the following steps:

[0006] S1, obtain satellite remote sensing data, digital surface model (DSM) data, urban public service facility point of interest (POI) data, and population location data;

[0007] S2, according to the different types of urban public service facilities, use satellite remote sensing data to extract and edit the spatial range of urban public service facilities, and calculate the monitoring area of ​​urban public service facilities in combination with DSM data;

[0008] S3, using population location data, constructs a model for the fluctuation of the flow of people in urban public service facilities at different time periods, determines a reasonable flow interval, and performs outlier detection on the flow of people in urban public service facilities;

[0009] S4. Based on the monitoring area of ​​urban public service facilities and the flow of people in urban public service facilities, an urban public service facility utilization evaluation model is constructed to evaluate the openness and utilization efficiency of urban public service facilities.

[0010] In some specific embodiments, in step S1, obtaining the location information of urban public service facilities, satellite remote sensing data, DSM data, and population location data, determining the spatiotemporal location information of urban public service facilities, and obtaining relevant satellite remote sensing data and population location data specifically include the following steps:

[0011] S11, obtaining the spatiotemporal location information of urban public service facilities, and cleaning, aggregating, and integrating the POI data of urban public service facilities;

[0012] S12, obtaining high-resolution satellite remote sensing image data, and performing pre-processing such as image orthorectification, image registration, image fusion, mosaic cutting, etc.;

[0013] S13, obtaining the population location data of urban public service facilities, and performing deduplication and cleaning processes.

[0014] S14, obtaining DSM data and performing preprocessing such as projection transformation and cropping.

[0015] In some specific embodiments, in step S2, according to different types of urban public service facilities, the spatial range of urban public service facilities is extracted and edited using satellite remote sensing data, and the monitoring area of ​​urban public service facilities is calculated, which specifically includes the following steps:

[0016] S21, using high-resolution satellite remote sensing images, uses deep learning semantic segmentation technology to preliminarily extract the scope of urban public service facilities;

[0017] S22, based on S21, edit the spatial scope of the venue. The spatial scope extraction principles of different types of urban public service facilities are different. For outdoor venues such as stadiums where the inner field is visible on remote sensing images, the inner field scope is extracted. For indoor venues such as gymnasiums, swimming pools, and diving halls, the building roof scope is extracted.

[0018] S23, calculate the height of the gymnasium building, convert the DSM data into point vector format, extract the gymnasium building outline and the elevation values ​​of all vectors within the gymnasium space range based on the urban public service facility space range, and calculate the height of the urban public service facility building in, It is the highest point of the stadium. It is the lowest point of the gymnasium;

[0019] S24, calculate the monitoring area of ​​urban public service facilities, based on the spatial scope area of ​​urban public service facilities ( ) and building height ( ), calculate the monitoring area of ​​urban public service facilities Indicates that the calculation result is rounded.

[0020] In some specific embodiments, in step S3, using population location data, constructing a human flow fluctuation model for urban public service facilities in different time periods, determining a reasonable human flow interval, and performing abnormal value detection on the human flow of urban public service facilities, specifically includes the following steps:

[0021] S31, based on the urban public service facility extraction results and population location data, statistics are made on the daily flow of people within the scope of the urban public service facilities within a period of time;

[0022] S32, using Fourier series to fit the changing trend of the flow of people in urban public service facilities over time, in, frequency, For time, is the fitting function cycle;

[0023] S33, calculate the reasonable flow interval, that is, the confidence interval of the fitting function in, is the average flow rate, is the significance level, for t value, is the standard deviation;

[0024] S34, conduct outlier detection on the flow of people in urban public service facilities. If the flow of people is greater than If the flow rate is less than If the flow rate is between the two, it is normal.

[0025] In some specific embodiments, in step S4, an urban public service facility utilization rate evaluation model is constructed based on the monitoring area of ​​the urban public service facilities and the flow of people in the urban public service facilities to evaluate the opening status and utilization efficiency of the urban public service facilities, which specifically includes the following steps:

[0026] S41, based on the monitoring results of abnormal values ​​of human flow, abnormal values ​​are not included in the evaluation of the opening and utilization efficiency of urban public service facilities;

[0027] S42, Constructing an evaluation model for the utilization rate of urban public service facilities in, is the utilization rate of urban public service facilities, Monitoring area for urban public service facilities, is the average daily flow of people, The smaller it is, the higher the utilization rate of urban public service facilities.

[0028] The present invention discloses a method for evaluating the utilization efficiency of urban public service facilities based on high-resolution satellite data, and relates to the field of satellite remote sensing technology. The method extracts the spatial scope of urban public service facilities through high-resolution satellite remote sensing data, and calculates the monitoring area of ​​urban public service facilities in combination with DSM data; uses the acquired population location data to construct a human flow fluctuation model for urban public service facilities in different time periods, and determines a reasonable human flow interval; performs abnormal value detection on the human flow of urban public service facilities; constructs an urban public service facility utilization efficiency evaluation model to evaluate the utilization efficiency of urban public service facilities. The present invention proposes a method for evaluating the utilization efficiency of urban public service facilities based on high-resolution satellite data. The result of the method is true and objective, avoiding the uncertainty generated by the reported data of urban public service facilities, and is helpful to truly and objectively grasp the opening status and utilization efficiency of urban public service facilities, and expand the application of satellite remote sensing in the fields of sports facility monitoring and supervision. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0030] Figure 1 A flow chart of a method for evaluating the utilization efficiency of urban public service facilities based on high-resolution satellite data provided by the present invention;

[0031] Figure 2 It is a trend diagram of the flow of people in urban public service facilities over time according to the present invention;

[0032] Figure 3 It is a Fourier series fitting result diagram of the change trend of the flow of people in urban public service facilities of the present invention;

[0033] Figure 4 It is a flow chart of another embodiment of a method for evaluating the utilization efficiency of urban public service facilities based on high-resolution satellite data provided by the present invention; DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, so that the processes, methods, systems, products or devices of a series of units are not necessarily limited to those units, but may include other units that are not clearly listed or inherent to these processes, methods, products or devices.

[0035] Reference Figure 1 , Figure 2 , Figure 3 and Figure 4 The method for evaluating the utilization efficiency of urban public service facilities based on high-resolution satellite data is shown in Figure 1. It uses high-resolution satellite remote sensing image data and population location data, and considers the relationship between the remote sensing monitoring area of ​​different types of urban public service facilities and the flow of people in urban public service facilities and the utilization efficiency of urban public service facilities. Figure 1 As shown, the following steps are included:

[0036] In step S1, the location information of urban public service facilities, satellite remote sensing data, digital surface model (DSM) data, and population location data are obtained to determine the spatiotemporal location information of urban public service facilities and obtain relevant satellite remote sensing data and population location data, which specifically includes the following steps:

[0037] S11, obtaining the spatiotemporal location information of urban public service facilities, and cleaning, aggregating, and integrating the POI data of urban public service facilities;

[0038] S12, obtaining high-resolution satellite remote sensing image data, and performing pre-processing such as image orthorectification, image registration, image fusion, mosaic cutting, etc.;

[0039] S13, obtaining the population location data of urban public service facilities, and performing deduplication and cleaning processes.

[0040] S14, obtaining DSM data and performing preprocessing such as projection transformation and cropping.

[0041] In step S2, according to different types of urban public service facilities, the spatial range of urban public service facilities is extracted and edited using satellite remote sensing data, and the monitoring area of ​​urban public service facilities is calculated in combination with DSM data; specifically, the following steps are included:

[0042] S21, using high-resolution satellite remote sensing images, uses deep learning semantic segmentation technology to preliminarily extract the scope of urban public service facilities;

[0043] S22, based on step S21, the spatial range of the venue is edited. The spatial range extraction principles of different types of urban public service facilities are different. For outdoor venues such as stadiums where the inner field of the venue is visible on the remote sensing image, the inner field range is extracted (generally including the track and field track and the internal football field, basketball court, etc.); for indoor venues such as gymnasiums, swimming pools, diving halls, etc., the building roof range is extracted;

[0044] S23, calculate the height of the gymnasium building, convert the DSM data into point vector format, extract the gymnasium building outline and the elevation values ​​of all vectors within the gymnasium space range based on the urban public service facility space range, and calculate the height of the urban public service facility building in, It is the highest point of the stadium. is the lowest point of the gymnasium. In this embodiment, rice, Meters, urban public service facility building height rice.

[0045] S24, calculate the monitoring area of ​​urban public service facilities, based on the spatial scope area of ​​urban public service facilities With building height Calculate the monitoring area of ​​urban public service facilities Indicates that the calculation result is rounded.

[0046] In this embodiment, the monitoring area of ​​urban public service facilities

[0047] In step S3, using population location data, a human flow fluctuation model of urban public service facilities in different time periods is constructed to determine a reasonable human flow range, and an outlier detection is performed on the human flow of urban public service facilities, which specifically includes the following steps:

[0048] S31, based on the urban public service facility extraction results and population location data, count the daily flow of people within the scope of the urban public service facilities within a period of time; for example: count the daily flow of people within the scope of the urban public service facilities within the time range of September 1 to October 31, 2023, and the results are as follows Figure 2 As shown;

[0049] S32, using Fourier series to fit the changing trend of the flow of people in urban public service facilities over time, in, frequency, For time, is the fitting function The cycle, when n =4, the fitting result is the best. The fitting curve is Figure 3 As shown;

[0050] S33, calculate the reasonable flow interval, that is, the confidence interval of the fitting function in, is the average flow rate, is the significance level, for t value, is the standard deviation, set the significance level =0.05, the confidence interval is (170, 330), that is, the reasonable flow range is between 170 and 330 people;

[0051] S34, conduct outlier detection on the flow of people in urban public service facilities. If the flow of people is greater than If the flow rate is less than If the flow of people is between the two, it is normal. According to the reasonable flow of people, if the flow of people is less than 170 people, it is abnormally small, and if the flow of people is greater than 330 people, it is abnormally large. The monitoring results show that from September 1 to October 31, 2023, there were 6 days with abnormally large flow of people in urban public service facilities and 8 days with abnormally small flow of people.

[0052] In step S4, based on the monitoring area of ​​urban public service facilities and the flow of people in urban public service facilities, an urban public service facility utilization efficiency evaluation model is constructed to evaluate the opening status and utilization efficiency of urban public service facilities, which specifically includes the following steps:

[0053] S41, based on the monitoring results of abnormal values ​​of human flow, abnormal values ​​are not included in the evaluation of the opening and utilization efficiency of urban public service facilities;

[0054] S42, Constructing an evaluation model for the utilization rate of urban public service facilities in, is the utilization rate of urban public service facilities, Monitoring area for urban public service facilities, is the average daily flow of people, The smaller it is, the higher the utilization rate of urban public service facilities. Calculation results show that from September 1 to October 31, 2023, the average utilization efficiency of urban public service facilities is 15.7 square meters per person.

[0055] It should be noted that the current evaluation of the openness and utilization efficiency of urban public service facilities only relies on local reported data, and the authenticity of the data needs further verification. Satellite remote sensing technology has the advantages and characteristics of wide range, long time series, large amount of data, and strong timeliness, and is widely used in monitoring and analysis in various fields.

[0056] The present invention is based on satellite remote sensing technology, based on high-resolution satellite remote sensing images, combined with urban public service facility POI data and population location data, to construct a passenger flow fluctuation model and an urban public service facility utilization efficiency evaluation model, which can truly and objectively evaluate the openness and utilization efficiency of urban public service facilities, help to verify the accuracy of urban public service facility reported data and supervise urban public service facilities to improve open services, and provide support for promoting a higher level of development of national fitness.

Claims

1. A method for evaluating the utilization efficiency of urban public service facilities based on high-resolution satellite data, characterized in that: Using high-resolution satellite remote sensing image data and population location data, the relationship characteristics between remote sensing monitoring areas of different types of urban public service facilities and the flow of people in urban public service facilities and the utilization efficiency of urban public service facilities are considered, including the following steps: S1, obtain satellite remote sensing data, digital surface model (DSM) data, urban public service facility point of interest (POI) data, and population location data; S2, according to the different types of urban public service facilities, use satellite remote sensing data to extract and edit the spatial range of urban public service facilities, and calculate the monitoring area of ​​urban public service facilities in combination with DSM data; S3, using population location data, constructs a model for the fluctuation of the flow of people in urban public service facilities at different time periods, determines a reasonable flow interval, and performs outlier detection on the flow of people in urban public service facilities; S4, based on the monitoring area of ​​urban public service facilities and the flow of people in urban public service facilities, build an urban public service facility utilization rate evaluation model to evaluate the opening status and utilization efficiency of urban public service facilities; In the step S1, the following steps are specifically included: S11, obtaining the spatiotemporal location information of urban public service facilities, and cleaning, aggregating and integrating the POI data of urban public service facilities; S12, obtaining high-resolution satellite remote sensing image data, and performing image orthorectification, image registration, image fusion, and mosaic cutting preprocessing; S13, obtaining the population location data of urban public service facilities, and performing deduplication and cleaning processing; S14, obtaining DSM data, performing projection transformation, and cropping preprocessing; In the step S2, the following steps are specifically included: S21, using high-resolution satellite remote sensing images, uses deep learning semantic segmentation technology to preliminarily extract the scope of urban public service facilities; S22, based on S21, edit the spatial scope of the venue. The spatial scope extraction principles of different types of urban public service facilities are different. For outdoor stadiums where the inner field of the venue is visible on the remote sensing image, the inner field scope is extracted. For indoor venues such as gymnasiums, swimming and diving halls, the building roof scope is extracted. S23, calculate the height of the gymnasium building, convert the DSM data into point vector format, extract the gymnasium building outline and the elevation values ​​of all vectors within the gymnasium space range based on the urban public service facility space range, and calculate the height of the urban public service facility building ,in, It is the highest point of the stadium. It is the lowest point of the gymnasium; S24, calculate the monitoring area of ​​urban public service facilities, based on the spatial scope area of ​​urban public service facilities ( ) and building height ( ), calculate the monitoring area of ​​urban public service facilities , Indicates that the calculation result is rounded; In the step S3, the following steps are specifically included: S31, based on the urban public service facility extraction results and population location data, statistics are made on the daily flow of people within the scope of the urban public service facilities within a period of time; S32, using Fourier series to fit the changing trend of the flow of people in urban public service facilities over time, ,in, , , , frequency, For time, is the fitting function S33, calculate the reasonable flow interval, that is, the confidence interval of the fitting function ,in, is the average flow rate, is the significance level, for t value, is the standard deviation; S34, conduct outlier detection on the flow of people in urban public service facilities. If the flow of people is greater than , then it is abnormally large. If the flow rate is less than , then it is abnormally small, if the flow rate is between the two, then it is normal; In the step S4, the following steps are specifically included: S41, based on the monitoring results of abnormal values ​​of human flow, abnormal values ​​are not included in the evaluation of the opening and utilization efficiency of urban public service facilities; S42, Constructing an evaluation model for the utilization rate of urban public service facilities ,in, is the utilization rate of urban public service facilities, Monitoring area for urban public service facilities, is the average daily flow of people, The smaller it is, the higher the utilization rate of urban public service facilities.

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