Air pollution estimation algorithm using change rate related to height of color component extracted from image data

Abstract

To provide a method and a system for a terminal having difficulty in fixing an angle of view to estimate aerosol concentration.SOLUTION: A method for estimating the aerosol concentration in a target area by using a computer includes: a physical quantity acquisition step of acquiring a physical quantity from a sample image obtained by photographing the target area; a numerical value for estimation acquisition step of acquiring a numerical value for estimation for estimating the aerosol concentration by using the physical quantity; and an aerosol concentration estimation step of estimating the aerosol concentration by using the numerical value for estimation. The physical quantity includes brightness for each of a plurality of color components at the plurality of positions of the sample image, and the height of the plurality of positions in the target area. The numerical value for estimation includes the rate of change of the ratio between the brightness of a first color and the brightness of a second color in the brightness of the plurality of color components, relative to the height.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an air pollution estimation algorithm that utilizes the rate of change of color components in image data. More specifically, the present invention relates to a method and a system for estimating aerosol concentration based on the rate of change of the height of a physical quantity in captured image data. [Background technology]

[0002] Japanese Patent Application Laid-Open No. 2023-061680 (Patent Document 1) describes a method and system for estimating aerosol concentration. In this method, the image range to be analyzed is narrowed down to a narrow range such as the upper sky portion, the lower sky portion, the boundary with the horizon, and the building portion, and a physical quantity correlated with the aerosol concentration is extracted for each range.

[0003] This method of estimating aerosol concentration has the problem that it requires a fixed angle of view using a fixed camera or the like. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2023-061680 A Summary of the Invention [Problem to be solved by the invention]

[0005] An invention described in this specification aims to provide a method for estimating aerosol concentration without requiring a fixed angle of view, and a system for implementing the method. [Means for solving the problem]

[0006] The present invention is basically based on the finding that by analyzing a sample image taken of a target area and using the rate of change in color components, intensities of each color component, and the ratio of these from the sample image, the aerosol concentration can be estimated without fixing the angle of view.

[0007] An invention relates to a method for estimating an aerosol concentration in a target area by a computer, the method including a physical quantity acquisition step (S101), an estimation value acquisition step (S102), and an aerosol concentration estimation step (S103). The physical quantity acquisition step (S101) is a step for acquiring physical quantities from a sample image captured of a target area. The physical quantities include physical quantities for each of a plurality of color components at a plurality of positions at different heights in the sample image, and heights for the plurality of positions. The estimation value acquisition step (S102) is a step for obtaining estimation values ​​for estimating the aerosol concentration using the obtained physical quantities. The estimation values ​​include the rate of change in the magnitude of the values ​​of the physical quantities for each of the multiple color components. The aerosol concentration estimation step (S103) is a step for estimating the aerosol concentration using the estimation value.

[0008] An example of the physical quantity for each of the plurality of color components includes a luminance of a first color component and a luminance of a second color component, and an example of the estimation value includes a rate of change of the magnitude of the ratio of the luminance of the first color component and the luminance of the second color component.

[0009] An example of the physical amount for each of the multiple color components includes the intensity of a first color component, and an example of the estimation value includes a rate of change in the magnitude of the intensity of the first color component.

[0010] An example of a sample image is an image taken using a mobile device.

[0011] One invention relates to a computer-based aerosol concentration estimation system 1. This system 1 includes an imaging unit 3, a physical quantity acquisition unit 5, an estimation value acquisition unit 7, and an aerosol concentration estimation unit 9. The photographing unit 3 is an element for photographing a sample image. The physical quantity acquisition unit 5 is an element for acquiring a predetermined physical quantity from a captured sample image. The physical quantity includes physical quantities for each of a plurality of color components at a plurality of positions with different heights in the sample image, and heights at the plurality of positions. The estimation value acquisition unit 7 is an element for obtaining estimation values ​​for estimating the aerosol concentration using the obtained physical quantities. The estimation values ​​include the rate of change in the height of the values ​​of the physical quantities for each of the multiple color components. The aerosol concentration estimation unit 9 is an element for estimating the aerosol concentration using the estimation numerical value. Effect of the Invention

[0012] According to the above invention, the aerosol concentration in a target area can be obtained from a sample image captured without fixing the angle of view. Therefore, this specification can provide a method for estimating the aerosol concentration by simply capturing a sample image with a mobile terminal or the like for which it is difficult to fix the angle of view, and a system for implementing the method. [Brief description of the drawings]

[0013] [Figure 1] FIG. 1 is a flow chart illustrating an example of a method for estimating aerosol concentration in a target area. [Diagram 2] FIG. 2 is a block diagram illustrating an example of a system for estimating aerosol concentration. [Diagram 3] FIG. 3 is a conceptual diagram showing an example of the configuration of an aerosol concentration estimation system. [Figure 4] FIG. 4 is a conceptual diagram for explaining the aerosol concentration estimation process using machine learning. [Diagram 5] FIG. 5 is a conceptual diagram showing an example of pattern matching. [Figure 6] FIG. 6 is a photograph in place of a drawing showing an example of a sample image. [Figure 7]FIG. 7 is a graph, instead of a drawing, showing the relationship between the luminance ratio between B and G (horizontal axis) and the height of a predetermined area (position of the part excluding buildings and the like) of a sample image. [Figure 8] Figure 8 is a graph, instead of a drawing, showing the relationship between the vertical rate of change (horizontal axis) of the blue to green luminance ratio (B / G) and the concentration (vertical axis) of suspended particulate matter (SPM) with a particle size of 10 μm or less. [Figure 9] Figure 9 is a graph, instead of a drawing, showing the relationship between the vertical rate of change (horizontal axis) of the green to red luminance ratio (G / R) and the concentration (vertical axis) of suspended particulate matter (SPM) with a particle size of 10 μm or less. [Figure 10] Figure 10 is a graph, instead of a drawing, showing the relationship between the vertical rate of change (horizontal axis) of the blue to red luminance ratio (B / R) and the concentration (vertical axis) of suspended particulate matter (SPM) with a particle size of 10 μm or less. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] Hereinafter, the embodiments of the present invention will be described with reference to the drawings. The present invention is not limited to the embodiments described below, and includes appropriate modifications of the embodiments described below within the scope obvious to those skilled in the art.

[0015] 1 is a flow chart showing an example of a method for estimating an aerosol concentration in a target area. S indicates a step. An invention described in this specification relates to a method for estimating an aerosol concentration in a target area using a computer.

[0016] 2 is a block diagram showing an example of a system for estimating an aerosol concentration. This system 1 has an imaging unit 3, a physical quantity acquisition unit 5, an estimation value acquisition unit 7, and an aerosol concentration estimation unit 9. This system is a computer-based system, and each process is performed by the computer.

[0017] The computer has an input unit, an output unit, a control unit, a calculation unit, and a storage unit, and each element is connected by a bus or the like so as to be able to exchange information. For example, the storage unit may store a control program or various information. When predetermined information is input from the input unit, the control unit reads out the control program stored in the storage unit. Then, the control unit appropriately reads out the information stored in the storage unit and transmits it to the calculation unit. Also, the control unit appropriately transmits the input information to the calculation unit. The calculation unit performs calculation processing using the various information received and stores it in the storage unit. The control unit reads out the calculation result stored in the storage unit and outputs it from the output unit. In this way, various processes and each process are executed. The various units and means execute these various processes. The computer may have a processor, and the processor may realize various functions and various processes. The computer may be standalone. A part of the functions of the computer may be distributed between a server and a terminal. In that case, it is preferable that the server and the terminal are able to exchange information through a network such as the Internet or an intranet. The computer may include a processor and a memory coupled to the processor. The memory may store instructions that, when executed by the processor, cause the computer to perform various processes or function as various elements. The computer may be provided with various teaching data to build a learning model and realize various calculations by machine learning. In this case, the computer may perform various analyses using a learning model created by machine learning and deep learning of AI (artificial intelligence).

[0018] FIG. 3 is a conceptual diagram showing an example of the configuration of an estimation system for aerosol concentration. This system may include a terminal connected to a network such as the Internet or an intranet, and a server connected to the network. Of course, a single computer or a mobile terminal may function as the device of the present invention, or multiple servers may exist. In the example of FIG. 3, input terminals 31, 33, 35, and 37 are connected to a server 41 via a network 39. Examples of input terminals are a camera 31, a smartphone 33, a portable game machine 35, and a notebook computer 37. Any device having a photographing unit such as these can function as an input terminal of this system. The photographed sample image may be processed within these terminals, or may be sent to the server 41 and processed in the server. It is preferable that various information is exchanged between the mobile terminal and the server wirelessly. Information may be exchanged between the terminal and the server using optical information.

[0019] Next, an example of a method for estimating the aerosol concentration in a target area using a computer will be described. As shown in Fig. 1, this method includes a physical quantity acquisition step (S101), an estimation value acquisition step (S102), and an aerosol concentration estimation step (S103). This series of algorithms for estimating the aerosol concentration is called SNAP-CII.

[0020] The physical quantity acquisition step (S101) is a step for obtaining physical quantities from a sample image of a target area. The target area may be an area where a spatial photograph can be taken. The target area may be a certain region, or an area photographed from a certain location. The physical quantities are numerical values ​​used to obtain estimation numerical values ​​used to estimate the aerosol concentration in the aerosol concentration estimation step. To obtain a sample image, a sample image photographed by a known photographing device may be input to a computer. The sample image may be a digital image of a space obtained by photographing the target area by a photographing device. The physical quantities may be for each pixel, or may be for a specific area of ​​the sample image.

[0021] Examples of physical quantities include physical quantities for each of a plurality of color components at a plurality of positions at different heights in the sample image, and heights for the plurality of positions. The target area is preferably, for example, an area of ​​the sample where no buildings or the like exist. An example of the physical quantity includes information on the intensity of each of a plurality of color components at a plurality of positions in the sample image and the heights in the target area for the plurality of positions. Another example of the physical quantity is the intensity of each color component. An example of the intensity of each color component is the luminance or reflectance of each RGB value. The values ​​for each color, such as RGB, may be corrected by gamma correction or the like. The three axes of the color components are not limited to RGB, and any color components may be used. The three axes of the color components are not limited to RGB, and may be any three axes, such as CMY, CIE XYZ, CIE xyY, CIE u'v'Y, CIE LUV, CIE LAB, and CIE LCh.

[0022] The photographing unit 3 photographs a sample image. The sample image of the target area is input to a computer and appropriately stored in a storage unit. At this time, one or more pieces of information of the photographing location (identification information), the photographing angle, the distance from the target space, the photographing time, and the weather conditions at the time of photographing may also be input to the computer and appropriately stored in the storage unit in a state associated with the input sample image.

[0023] The physical quantity acquisition unit 5 may divide the sample image into a plurality of regions and extract the regions for estimating the aerosol concentration. The sample image may be analyzed to exclude regions with obstacles such as buildings and trees from the regions for calculating the aerosol concentration. Furthermore, in the sky region, the amount of clouds may be analyzed and excluded from the regions for calculating the aerosol concentration, or a coefficient or constant (correction value) for calculating the aerosol concentration may be obtained. The physical quantity acquisition unit 5 may obtain information on the height in the target region. The information on the height in the target region may be the height [m] of each position. In this case, the physical quantity acquisition unit 5 may obtain the height of each position or each pixel using an algorithm for estimating the height of each position or each pixel from the obtained sample image. Furthermore, the information on the height in the target region may be the relative position between the upper side and the lower side of the target region to be analyzed. In this case, the physical quantity acquisition unit 5 may obtain the height of each position or each pixel from the relative position in the target region. Furthermore, the physical quantity acquisition unit 5 analyzes the sample image to obtain the intensity of each of the plurality of color components for each position or each pixel. The physical quantity acquisition unit 5 may also obtain the intensity of each of a plurality of color components for each position or each pixel. A method for obtaining the intensity of each of a plurality of color components for each position or each pixel or the intensity of each color component is publicly known as described in JP 2023-061680 A. The physical quantity obtained by the physical quantity acquisition unit 5 is appropriately stored in a storage unit.

[0024] The estimation value acquisition step (S102) is a step for obtaining an estimation value for estimating the aerosol concentration using the obtained physical quantity. The estimation value is a rate of change in the height of the value of the physical quantity for each of a plurality of color components. An example of the estimation value is a rate of change in the height of the ratio of the luminance of the first color component and the luminance of the second color component. Another example of the estimation value is a rate of change in the height of the luminance of the first color component. Another example of the estimation value is a rate of change in the height of the reflectance of the first color component. The computer reads out the physical quantity obtained in the physical quantity acquisition step from the storage unit. Then, the control unit reads out necessary values ​​from the storage unit as appropriate based on the command of the program, and causes the calculation unit to perform a calculation to obtain the estimation value using the values ​​together with the physical quantity. The computer stores the obtained estimation value in the storage unit as appropriate. A method for obtaining a rate of change in the height of a physical quantity is known. The simplest method for obtaining a rate of change in the height of a physical quantity is to divide the difference between the physical quantities at two points by the difference in the heights of the two points. Furthermore, a graph showing the relationship between the physical quantity and the height in a specific region can be created, and the rate of change in height (Δh) can be found by calculating the slope of the graph.

[0025] Specifically, the estimation numerical value acquisition unit 7 uses the physical quantity for each color component of the pixel of the sample image and the height in the target area to determine the rate of change in height of the physical quantity for each color component in the area where the sample image was taken. For example, if the position of the sample image or the pixel position is (x, y), then y corresponds to the height (h). For example, the estimation numerical value acquisition unit 7 reads out the intensity of each color for multiple points with the same x and different y from the storage unit. The estimation numerical value acquisition unit 7 reads out the intensity of the first color and the intensity of the second color (for example, the luminance of blue (B) and the luminance of green (G) at a pixel) and determines the ratio (B / G) of the intensity of the first color to the intensity of the second color. For example, R R , R G and R B Let be the luminance of each R, G, and B. An example of the ratio of the luminance of the first color to the luminance of the second color is R B / R G , R G / R R , and R R / RB The computer control unit is the R R R G R B The value is used in the calculation. B / R G , R G / R R , and R R / R B Then, the control unit of the computer executes the calculation process to obtain the R B / R G , R G / R R , and R R / R B The value of is appropriately stored in the storage unit. The estimation value acquisition unit 7 performs calculations to obtain the value at multiple points with different y. The estimation value acquisition unit 7 uses the ratio (B / G) of the intensity of the first color to the intensity of the second color for multiple points with different y to obtain the rate of change (rate of change in height) of the ratio (B / G) of the intensity of the first color to the intensity of the second color. Similar calculations may be performed for other colors. In this example, the ratio of the luminance of the first color to the luminance of the second color is used as the feature value. For other feature values, if the rate of change in height is correlated with the aerosol concentration, it can be used as the estimation value. In the embodiment, it was confirmed that the rate of change of the ratio of the intensity of the first color to the intensity of the second color is correlated with the aerosol concentration. From this, it is inferred that the rate of change of the intensity of each color is also correlated with the aerosol concentration. It is also inferred that the rate of change of the intensity of any color is also correlated with the aerosol concentration.

[0026] The aerosol concentration estimation step (S103) is a step for estimating the aerosol concentration using the estimation value. The computer reads out the estimation value from the storage unit. The control unit of the computer estimates the aerosol concentration using the estimation value based on the instructions of the program.

[0027] FIG. 4 is a conceptual diagram for explaining an aerosol concentration estimation process using machine learning. The computer may be equipped with, for example, a machine learning engine, and may estimate the aerosol concentration by machine learning using the estimation numerical value. The determined aerosol concentration may be stored in a storage unit. As shown in FIG. 4, the machine learning engine receives a plurality of estimation numerical values ​​(for example, a change rate related to the luminance of the first color) that are feature amounts and correct answer data (for example, a numerical value of the aerosol concentration in the target area provided by the Ministry of the Environment) as teacher data. By repeating the operation of providing this teacher data, the learning model is trained to create a trained model. The trained model can be a trained model by inputting the estimation numerical values ​​obtained from a sample image into the learning model and recognizing a correlation with the obtained aerosol concentration. By repeatedly providing this teacher data and test data, the accuracy of the trained model can be improved.

[0028] The aerosol concentration estimation unit 9 estimates the aerosol concentration using the estimation value. For example, the aerosol concentration estimation unit 9 has the above-mentioned trained model, and inputs the estimation value read from the storage unit into the trained model. Then, the aerosol concentration estimation unit 9 can obtain aerosol concentration related information. The aerosol concentration related information obtained in this manner may be appropriately stored in the storage unit or may be output. Examples of the aerosol concentration related information may be an estimated value of the aerosol concentration in the target area, information such as an index indicating the aerosol concentration (for example, the aerosol concentration is extremely high, high, normal, or low), or image information such as an icon corresponding to the aerosol concentration. In addition, the computer may use the estimated aerosol concentration to obtain other values ​​such as visibility, visibility, viewability, air pollution level, or air quality index. As for the meteorological condition determination process, the one described later may be appropriately used.

[0029] Aerosol concentration output process (S104) The estimated aerosol concentration may be output as appropriate. For example, estimated values ​​of aerosol concentration are output to a server from multiple points. Then, information on aerosol concentration can be collected over a wide area. As a result, for example, if the aerosol concentration is high near a certain factory and low away from the factory, it is estimated that the factory is the pollution source. Also, for example, if the computer is a user's mobile terminal (including a smartphone, a mobile phone, and a wristwatch), the aerosol concentration may be displayed on the display unit of the mobile terminal. Also, the aerosol concentration may be output to a server that can exchange information with the computer. In this way, information on aerosol concentration is output from multiple users' mobile terminals and collected in the server.

[0030] The computer may also determine the level of air pollution based on the estimated aerosol concentration. In this case, the control unit may use the estimated aerosol concentration to read a threshold value from the storage unit, and determine the level of air pollution by comparing the estimated aerosol concentration with the threshold value. The determined level of air pollution may be appropriately stored in the storage unit or output. The computer may also output the aerosol concentration and the level of air pollution together with weather forecast information.

[0031] This specification also provides a program for causing a computer (or a processor) to function as the above-mentioned aerosol concentration estimation system, and an information recording medium that can be read by a computer storing such a program. This invention can also be used as an application installed in a mobile terminal such as a smartphone or a tablet. Then, a user can easily measure the aerosol concentration by using the mobile terminal on which the application is installed.

[0032] Weather condition determination process Next, the meteorological condition determination process shown in FIG. 1 will be described. The meteorological condition determination process is an optional process for determining whether or not the meteorological conditions are suitable for estimating the aerosol concentration (whether or not the shooting location matches the fine weather conditions). Another example of the meteorological condition determination process is a process for determining the meteorological conditions used in determining when the method for estimating the aerosol concentration differs depending on the meteorological conditions or when the parameters for estimating the aerosol concentration differ depending on the meteorological conditions. The meteorological condition determination process is not particularly limited as long as it can achieve the above. This process may also be performed in the advance preparation process.

[0033] An example of the weather condition determination step includes a weather condition input step (S121) and a condition determination step (S122). The weather condition input step (S121) is a step for inputting information required for determination into a computer, and the condition determination step (S122) is a step for determining weather conditions based on the input information. A specific example of the weather condition determination step is determining weather conditions using global solar radiation (GSI) and diffuse solar radiation (DSI). A daylighting system is described in the pamphlet of International Publication WO2018 / 100957. The weather detection unit of this daylighting system includes a direct solar radiation meter that detects direct solar radiation and a global solar radiation meter that detects global solar radiation. This system calculates the amount of diffuse solar radiation by subtracting the amount of direct solar radiation from the amount of global solar radiation, and calculates the ratio of the amount of diffuse solar radiation to the amount of direct solar radiation. As described above, a system for determining the amount of global solar radiation and the amount of diffuse solar radiation using a direct pyranometer and a pyranometer is known. The computer may be connected to the direct pyranometer and the pyranometer, and may be configured to input observation data from the direct pyranometer and the pyranometer.

[0034] An example of a weather condition determination process is GSI > 550 W / m 2In this example, the global solar radiation (GSI) and diffuse solar radiation (DSI) are input to the system. The system stores the input global solar radiation (GSI) and diffuse solar radiation (DSI) in a memory unit as appropriate. Based on instructions from the program, the control unit reads the global solar radiation (GSI) and a threshold value (for example, 550 W / m 2 ) is read out. The control unit causes the calculation unit to perform a calculation to compare the global solar radiation (GSI) with a threshold value, and if the global solar radiation (GSI) is greater than the threshold value, the process proceeds to the next step. On the other hand, the control unit causes the calculation unit to perform a calculation to compare the global solar radiation (GSI) with a threshold value, and if the global solar radiation (GSI) is less than the threshold value, the process determines that the weather conditions are not sunny. In the next step, the control unit reads out the global solar radiation (GSI) and the diffuse solar radiation (DSI) from the storage unit, and causes the calculation unit to perform a calculation process to obtain DSI / GSI based on the program's instructions. When this process is performed by hardware, a reverse circuit and a multiplication circuit may be used to obtain 1 / global solar radiation (GSI) from the global solar radiation (GSI), and the obtained value may be multiplied by the diffuse solar radiation (DSI) to obtain DSI / GSI. The control unit stores the obtained DSI / GSI in the storage unit. The control unit reads out the DSI / GSI and a threshold value (e.g., 0.15) from the storage unit, and causes the calculation unit to perform a calculation to compare the DSI / GSI with the threshold value based on the instructions of the program. If the result shows that the DSI / GSI is smaller than the threshold value, it is determined that the weather conditions are fine. Information indicating that the weather conditions are fine may be appropriately stored in the storage unit, or may be used for the next process.

[0035] The calculation unit of the computer may read out the sample image from the storage unit, perform pattern matching processing based on the instructions of the program, and perform calculations to classify the sample image into sunny areas, cloudy areas, rain, buildings, trees, and others, and judge the weather conditions based on the obtained calculation results. At this time, for example, a threshold value serving as a judgment criterion may be read out from the storage unit and compared with the threshold value to judge the weather conditions.

[0036] Weather conditions of the target area may be input from an input unit of the computer and stored in the storage unit. Then, the control unit of the computer may cause the calculation unit to perform appropriate calculation processing based on instructions from the program, and determine whether the input weather conditions (e.g., one or more conditions of weather, temperature, humidity, and precipitation probability) are suitable for estimating the aerosol concentration. Also, a coefficient for calculating the aerosol concentration as appropriate may be used based on the input weather conditions, and may be used in the process of estimating the aerosol concentration described below.

[0037] For example, if the meteorological conditions at the time of measurement are determined to be suitable for estimating the aerosol concentration in the meteorological condition determination step, a step of estimating the aerosol concentration, which will be described below, may be performed.

[0038] FIG. 5 is a conceptual diagram showing an example of pattern matching. In the example of FIG. 5, a region including a part (region with obstacles) that is estimated to be a building or a tree from the RGB values ​​of each pixel included in the sample image is removed from the region for estimating the aerosol concentration, and the region of the sample image that exists above the region with obstacles is set as the region for estimating the aerosol concentration. In the example of FIG. 5, the sample image 51 is classified into a region 53 that is not used for estimating the aerosol concentration because of an obstacle such as a building, a region 55 above that where there are no clouds, and a region 57 further above that where there are clouds. In this example, the computer that performed the image analysis may set the target region to the region 55 where there are no clouds and the region 57 further above that where there are clouds, or may set the region 55 where there are no clouds as the target region. EXAMPLES

[0039] In this example, the amount of solar radiation at the time and place where the sample image was taken was obtained from other satellite data, and the conditions for clear skies with few clouds were determined. Image analysis techniques were used to extract features related to aerosol concentration from the sample image. A machine learning model was constructed using the features as input variables to calculate the aerosol concentration.

[0040] The aerosol concentration estimation method and system of the present invention were verified using image data taken in Fukuoka City for two years from January 2021 to December 2022. Here, an example is shown in which the feature amount is the rate of change of RGB ratio. Concentrations of suspended particulate matter (SPM) with a particle size of 10 μm or less in aerosols were classified into three classes using Random Forest (RF), K-nearest Neighbor (K-NN), and Support Vector Machine (SVM). The classification threshold was set at 10 μg / m based on the annual average and daily average of the environmental standards set by the World Health Organization. 3 , and 30 μg / m 3 The criteria for determining a clear sky was that the ratio (DSI / GSI) of the two variables, global solar radiation (GSI) and diffuse solar radiation (DSI), from the solar radiation data observed by the Himawari 8 satellite was 0.15 or less. Furthermore, in order to remove clouds that were not completely removed and were reflected in the image, only pixels that satisfied (BR) / (B+R)>0.2 were analyzed. For this target range, the vertical change rates of B / G, G / R, and B / R against the relative pixel coordinates in the vertical direction were calculated. The correct answer data used to build the machine learning model was ground observation data from the Ministry of the Environment's Wide-area Air Pollutant Monitoring System. The dataset processed by the above method was randomly divided into training and testing data in a ratio of 6:4, and a model built using only the training data was applied to the testing data, and the accuracy rate was calculated. As a result, accuracy rates of 72%, 69%, and 72% were achieved for RF, K-NN, and SVM, respectively.

[0041] In this embodiment, a sample image was obtained. FIG. 6 is a photograph in lieu of a drawing showing an example of a sample image. Three components (RGB, CIE XYZ, etc.) extracted from the sample image and the vertical change rate of the ratio were extracted as feature quantities. FIG. 7 is a graph in lieu of a drawing showing the relationship between the brightness ratio of B and G (horizontal axis) and the height of a predetermined area (position of the part excluding buildings, etc.) of the sample image. As shown in FIG. 7, the change rate showed an almost constant value from the top to the bottom of the sky. FIG. 8 is a graph in lieu of a drawing showing the relationship between the vertical change rate (horizontal axis) of the brightness ratio of blue and green (B / G) and the concentration (vertical axis) of suspended particulate matter (SPM) with a particle size of 10 μm or less. FIG. 9 is a graph in lieu of a drawing showing the relationship between the vertical change rate (horizontal axis) of the brightness ratio of green and red (G / R) and the concentration (vertical axis) of suspended particulate matter (SPM) with a particle size of 10 μm or less. FIG. 10 is a graph, instead of a drawing, showing the relationship between the vertical rate of change (horizontal axis) of the blue to red luminance ratio (B / R) and the concentration (vertical axis) of suspended particulate matter (SPM) with a particle size of 10 μm or less. FIGS. 8 to 10 show that there is a correlation between the vertical rate of change of the luminance of the color components and the vertical rate of change of the luminance ratio of the color components and the aerosol concentration. For this reason, it is considered possible to construct a machine learning model and obtain a trained model using these change rates as feature quantities (teaching data), for example. By constructing such a machine learning model, there is no need to specify the range of the sky (such as the upper, middle, or lower part of the sky). Furthermore, it was also found that the lower the aerosol concentration, the larger the change rate is, so it is effective as a feature quantity for calculating the aerosol concentration.

[0042] According to the embodiment, Weather Report operated by Weathernews Inc. (registered trademark) is an example of a service that has a high affinity with the present invention. This service is a weather forecast service based on users posting daily photos of the sky. If the present invention is used in such a user-participation weather forecast service, aerosol concentrations can be estimated from photos posted by users, and ultimately, every citizen will be able to observe aerosols. This is expected to increase the general public's understanding and awareness of not only air pollution but also global atmospheric observation, and has high potential for social applications in addition to academic significance. [Industrial Applicability]

[0043] The present invention can be used in fields such as weather forecasting. [Explanation of symbols]

[0044] 1 System 3. Photography Department 5 Physical quantity acquisition part 7. Estimation value acquisition section 9. Aerosol concentration estimation section

Claims

1. 1. A method for estimating aerosol concentration in a region of interest by a computer, comprising: The computer, a physical quantity acquisition step of acquiring a physical quantity from a sample image obtained by photographing the target area; an estimation value acquisition step of obtaining an estimation value for estimating an aerosol concentration using the physical quantity; an aerosol concentration estimation step of estimating an aerosol concentration using the estimation value; Including, the physical quantities include physical quantities for a plurality of color components at a plurality of positions having different heights in the sample image, and the heights for the plurality of positions; the estimation value includes a rate of change of the height of a value related to a physical quantity for each of the plurality of color components; A method for estimating aerosol concentrations in a target area.

2. A method for estimating an aerosol concentration in a target area according to claim 1, comprising: the physical quantities for each of the plurality of color components include an intensity of a first color component and an intensity of a second color component; A method for estimating aerosol concentration in a region of interest, wherein the estimation value comprises a rate of change of a ratio of an intensity of a first color component to an intensity of a second color component with respect to the height.

3. A method for estimating an aerosol concentration in a target area according to claim 1, comprising: the physical quantities for each of the plurality of color components include an intensity of a first color component; The method of estimating aerosol concentration in a region of interest, wherein the estimating value comprises a rate of change of intensity of a first color component with respect to the height.

4. A method for estimating an aerosol concentration in a target area according to claim 1, comprising: A method for estimating aerosol concentration in a target area, wherein the sample image is an image taken using a mobile device.

5. 1. A computer-based aerosol concentration estimation system, comprising: A photographing unit that photographs a sample image; a physical quantity acquisition unit for acquiring a predetermined physical quantity from the sample image; an estimation value acquisition unit that acquires an estimation value for estimating an aerosol concentration using the physical quantity; an aerosol concentration estimation unit that estimates an aerosol concentration using the estimation value; having the physical quantities include physical quantities for a plurality of color components at a plurality of positions having different heights in the sample image, and the heights for the plurality of positions; the estimation value includes a rate of change of the height of a value related to a physical quantity for each of the plurality of color components; Aerosol concentration estimation system.

6. A program for causing a computer to function as an aerosol concentration estimation system, comprising: The aerosol concentration estimation system comprises: A photographing unit that photographs a sample image; a physical quantity acquisition unit for acquiring a predetermined physical quantity from the sample image; an estimation value acquisition unit that acquires an estimation value for estimating an aerosol concentration using the physical quantity; an aerosol concentration estimation unit that estimates an aerosol concentration using the estimation value; having the physical quantities include physical quantities for a plurality of color components at a plurality of positions having different heights in the sample image, and the heights for the plurality of positions; the estimation value includes a rate of change of the height of a value related to a physical quantity for each of the plurality of color components; program.

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