KR20210075533A - Vision-based Rainfall Information System and Methodology Using Deep Learning - Google Patents

Abstract

A system and a method for providing image-based precipitation information using deep learning are provided. After collecting and processing user's image data including precipitation information, the system learns the precipitation image included in the image data through deep learning to estimate the precipitation information of the image data. By providing a service that provides these estimates to users, it is possible to maximize the efficiency of precipitation observation and help respond to natural disasters and accidents.

Description

{ Vision-based Rainfall Information System and Methodology Using Deep Learning }

The present invention relates to a system and method for providing image-based precipitation information using deep learning, and more particularly, information such as ripples or traces of raindrops made by raindrops in a video image captured through a photographing module and SNS. It relates to a system and method for deep learning precipitation information by deep learning and providing precipitation information such as rainfall intensity and rainfall through it.

In general, precipitation observation technology relies on collecting and measuring precipitation information such as mass and volume of rainwater through separate equipment such as a rain gauge.

However, these observation technologies have limitations in installation depending on weather conditions such as wind and surrounding geographical features, and there is a problem in that it is difficult to observe immediately. In addition, spatiotemporal resolution is low, and there is a problem in that an observation error is large depending on the precision of an observation area or equipment.

Accordingly, radar, PARSIVAL, 2DVD, etc. are used as indirect precipitation observation technologies. Radar has the advantage of being easy to analyze spatiotemporal precipitation characteristics over a large area, but it requires a very high cost to install and operate, manage, and process data. There are great difficulties in

In addition, PARSIVEL, 2DVD is a technology that directly measures raindrops using an optical sensor and has a fairly high accuracy, but this also has restrictions on installation conditions and is much more expensive than a rain gauge, so there is a greater limitation in spatial resolution than a rain gauge.

Therefore, a technique for increasing the spatial density of precipitation observation at low cost is required for monitoring and appropriate response to sudden floods and localized heavy rains.

The technical task to be achieved by the present invention in order to solve the above problems is to improve the accuracy of weather observation by using it as additional information for spatial precipitation distribution calculations by the Korea Meteorological Administration, the Ministry of Environment, etc. It enables precipitation observation without the installation of additional equipment, and it is possible to build a spatio-temporal high-resolution precipitation observation network through social media activities and voluntary participation of citizens, and through this, accurate observation and prediction of flash floods and localized heavy rains are possible. to prevent meteorological disasters.

The problems to be solved of the present invention are not limited to those mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the following description.

As a means for solving the above technical problem, according to an embodiment of the present invention, an image-based precipitation information providing system using deep learning acquires an image generated by a user through a method such as shooting and sends it to a precipitation analysis server data collection unit to transmit; a precipitation analysis server that transmits analysis result data including precipitation information including precipitation intensity and precipitation amount to a service platform by utilizing deep learning based on the transmitted data; and a service platform that provides a service related to the analysis result data.

The data collection unit may include: a photographing module for a user to generate a daily image including precipitation information; and a determining unit that determines whether there is precipitation based on the image collected from the photographing module and transmits the collected image to the precipitation analysis server when it is determined that there is precipitation.

The photographing module is any one of outdoor photographing modules that generate a video image by constantly photographing a certain area, such as a camera, a digital camera, a CCTV, a black box, a recognition unit of an autonomous vehicle, etc. built in a user's smart terminal.

The determining unit determines whether precipitation is present by periodically detecting ripples and raindrops on the glass window from the collected images when the collected images are directly collected from the user terminal including the photographing module or the photographing module.

The determination unit, when the collected image is uploaded to the SNS platform by the user from the user terminal including the photographing module and collected through the SNS platform, the hash tag included in the post, the comment analysis, and ripples from the collected image , and detects raindrops on the glass window to determine the presence or absence of precipitation.

The precipitation analysis server may include: a controller configured to detect a precipitation image including precipitation information with respect to the transmitted data and estimate precipitation information through deep learning; and a memory for storing the detected precipitation image as learning data.

The control unit may include: an image extraction unit configured to extract a precipitation image including precipitation information from the input image when receiving one or more of the transmitted data; a modeling unit for generating an artificial neural network model according to the degree of precipitation by inputting the extracted precipitation image as learning data for deep learning; an estimator 213 for estimating the amount of precipitation of the transmitted data based on the artificial neural network model; and a transmission unit for transmitting the analysis result data of the precipitation estimation to the service platform.

The service platform tags the analysis result data together on the SNS platform when the data collection unit collects the image through the SNS platform, and adds the analysis result data to the image when the image is collected through a general shooting application. In the case of overlapping, and when images are collected through an in-vehicle camera such as CCTV or a black box, a service that provides analysis result data along with risk information and disaster information is provided to the user.

The service platform provides the analysis result data together with geotag information included in the precipitation image extracted from the precipitation analysis server to the weather-related service provider.

Meanwhile, according to another embodiment of the present invention, in the image-based precipitation information providing method using deep learning, the data collection unit acquires the image generated by the user through a method such as shooting and transmits it to the precipitation analysis server. ; A precipitation analysis step of transmitting, in the precipitation analysis server, analysis result data including precipitation information including precipitation intensity and precipitation amount, etc. to a service platform by using deep learning based on the transmitted data; and providing, by the service platform, a service related to the analysis result data.

The data collection step may include: a user photographing a daily image including precipitation information using a photographing module; and a precipitation determination step of determining the presence or absence of precipitation based on the image collected from the photographing module and transmitting the collected image to a precipitation analysis server when it is determined that there is precipitation.

The photographing module is any one of outdoor photographing modules that generate a video image by constantly photographing a certain area, such as a camera, a digital camera, a CCTV, a black box, a recognition unit of an autonomous vehicle, etc. built in a user's smart terminal.

In the precipitation determination step, when the collected image is directly collected from a photographing module or a user terminal including a photographing module, it is determined whether there is precipitation by periodically detecting ripples and raindrops on the glass window from the collected images.

In the precipitation determination step, when the collected image is uploaded to an SNS platform by a user from a user terminal including a photographing module and collected through the corresponding SNS platform, the hashtag included in the post, comment analysis, and the collected image It detects ripples and raindrops on the windows to determine the presence or absence of precipitation.

In the precipitation analysis step, the precipitation analysis server detects a precipitation image with respect to the transmitted data, stores the detected precipitation image as learning data in a memory, and estimates precipitation information through deep learning.

The precipitation analysis step may include: when the image extraction unit receives one or more of the transmitted data, extracting a precipitation image including precipitation information from the input image; The modeling unit generates an artificial neural network model according to the degree of precipitation by inputting the extracted precipitation image as training data for deep learning; estimating, by an estimator, the amount of precipitation of the transmitted data based on the artificial neural network model; and transmitting, by the transmission unit, the analysis result data of the precipitation amount estimation to the service platform.

The service platform tags the analysis result data together on the SNS platform when the data collection unit acquires the image through the SNS platform, and overlaps the analysis result data on the image when the image is acquired through a general shooting application If the image is acquired through a camera inside the vehicle, such as a CCTV or a black box, a service that provides analysis result data along with risk information and disaster information is provided to the user.

The service platform provides the analysis result data together with geotag information included in the precipitation image extracted from the precipitation analysis server to the weather-related service provider.

According to the present invention,

It improves the accuracy of weather observation by using it as additional information for spatial precipitation distribution calculations by the Korea Meteorological Administration and the Ministry of Environment, and enables precipitation observation without additional equipment installation from autonomous vehicle cameras and other applicable camera equipment, and social media activities of citizens. And through voluntary participation, it is possible to build a spatio-temporal ultra-high-resolution precipitation observation network, which enables accurate observation and prediction of flash floods and localized heavy rains, thereby ultimately preventing meteorological disasters.

Effects of the present invention are not limited to those mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a block diagram showing an image-based precipitation information providing system according to an embodiment of the present invention;
2 is a flowchart illustrating an image-based precipitation information providing method according to an embodiment of the present invention;
3 is an exemplary diagram of the process of estimating precipitation in ripples;
4 is an exemplary diagram of an experiment for extracting ripple information through an image processing algorithm;
5 is an exemplary view showing an effect expected by the implementation of the present invention.

The above objects, other objects, features and advantages of the present invention will be easily understood through the following preferred embodiments in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments described herein and may be embodied in other forms. Rather, the embodiments introduced herein are provided so that the disclosed content may be thorough and complete, and the spirit of the present invention may be sufficiently conveyed to those skilled in the art.

In this specification, when terms such as first, second, etc. are used to describe components, these components should not be limited by these terms. These terms are only used to distinguish one component from another. The embodiments described and illustrated herein also include complementary embodiments thereof.

Further, when it is stated that any element, component, device, or system includes a component consisting of a program or software, even if not explicitly stated, that element, component, device, or system means that the program or software is executed. Alternatively, it should be understood to include hardware (eg, memory, CPU, etc.) necessary for operation or other programs or software (eg, drivers necessary for operating an operating system or hardware, etc.).

Also, the terms used herein are for the purpose of describing the embodiments and are not intended to limit the present invention. As used herein, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, the terms 'comprises' and/or 'comprising' do not exclude the presence or addition of one or more other components.

In addition, the '... Wealth', '... Terms such as 'group' and 'module' mean a unit that processes at least one function or operation, which may be implemented as hardware or software or a combination of hardware and software. In addition, articles such as 'a', 'an' and 'the' in the context of describing the present invention are meant to include both the singular and the plural unless otherwise indicated herein or clearly contradicted by the context in the context of describing the present invention. can be used

In describing the specific embodiments below, various specific contents have been prepared to more specifically describe the invention and help understanding. However, a reader having enough knowledge in this field to understand the present invention may recognize that it can be used without these various specific details.

In some cases, it is mentioned in advance that in describing the invention, parts that are commonly known and not largely related to the invention are not described in order to avoid confusion for no reason in explaining the invention.

Hereinafter, specific technical contents to be practiced in the present invention will be described in detail with reference to the accompanying drawings.

The present invention provides a method of estimating and servicing precipitation information such as rainfall intensity and rainfall through the corresponding image data by photographing the precipitation environment using a photographing module such as a smartphone camera that anyone has without the need to purchase separate observation equipment. will be.

1 is a block diagram illustrating an image-based precipitation information providing system according to an embodiment of the present invention, and FIG. 2 is a flowchart illustrating an image-based precipitation information providing method according to an embodiment of the present invention.

Referring to FIG. 1 , an image-based weather information providing system according to an embodiment of the present invention may include a data collection unit 100 , a precipitation analysis server 200 , and a service platform 300 . In addition, the data collection unit 100 may include a photographing module 110 in which the user shoots an outdoor image, etc., and a determination unit 120 that primarily determines the presence or absence of precipitation based on the image photographed by the photographing module 110. can

Referring to FIG. 2 , the image-based weather information providing method according to an embodiment of the present invention may include a data collection step ( S100 ), a precipitation analysis step ( S200 ), and a step ( S300 ) of providing a service to a user.

In the data collection step (S100), the image generated by the user through the photographing module 110 is acquired through the data collection unit 100 and transmitted to the precipitation analysis server. Specifically, the user takes an image through the photographing module 110 (S110), and it is transmitted to the precipitation analysis server only when it is determined that there is precipitation by determining the presence or absence of precipitation based on the image collected from the photographing module 110. It may include a precipitation determination step (S120).

The photographing module 110 may be a camera unit of a smartphone, a digital camera, etc. that anyone generally has, and may also include a black box camera of a vehicle, a recognition unit of an autonomous vehicle, or a CCTV.

The photographing module 110 is capable of real-time, regular precipitation monitoring.

Since the image generated through the photographing module 110 may include an outdoor photographed image required for precipitation observation, the image may include a photo or a video.

In the present invention, in that precipitation can be observed through the existing photographing module 110 without additional cost or installation space, the cost of the existing observation technology, installation site, maintenance, transferability of data, spatiotemporal intermittency, environmental destruction, etc. can solve the problem

In the precipitation determination step, the determination unit 120 determines the collection path of the collected images, and then determines the presence or absence of precipitation differently based on the image generated by the photographing module 110 according to the collection path.

The determination unit 120 first determines whether the collected image is directly collected from the user terminal itself, such as a smart phone including the photographing module 110 terminal or the photographing module 110, or whether the user in the user terminal performs a social network (SNS) Service), it is possible to first determine the collection path, such as whether it was uploaded to the platform and collected through the corresponding SNS platform.

As a result of the determination of the collection path, when the collected image is collected from the user terminal itself, that is, transmitted from a smartphone, digital camera, CCTV, black box, autonomous vehicle, etc., the ripple generated by raindrops in the image periodically (Water ripples), raindrops themselves, traces of raindrops, raindrops or raindrops on a windowpane, or an open umbrella, etc., determine whether there is precipitation.

As a result of the determination of the collection path, when the collected image is collected through the SNS platform, that is, when the user uploads a newly created image to the SNS platform or stored in the user terminal, the image is periodically generated by raindrops from the image. Precipitation is determined based on criteria that can be judged to be precipitation such as water ripples, raindrops themselves, traces of raindrops, raindrops or raindrops on a windowpane, or an open umbrella, or a hashtag ( hashtag), comments, etc. can be analyzed to determine whether it is precipitation.

If information such as ripples, raindrops, traces of raindrops, raindrops or raindrops formed on a windowpane, an open umbrella, etc. generated by raindrops is detected in the image, it can be determined that precipitation exists.

In addition, if precipitation-related keywords such as precipitation, rain, rain, umbrella, and heavy rain are included in hashtags and comments included in SNS posts, it may be determined that precipitation exists.

However, if the collection path of the collected data is collected through the SNS platform as a result of determination by the determination unit 120 of the data collection unit 100, the copyright or portrait right of the corresponding video image is encrypted and protected and then sent to the precipitation analysis server. is sent

The collected image data is transmitted to the precipitation analysis server 200 only when it is determined that precipitation exists as a result of the primary precipitation determination of the determining unit 120 .

The determination unit 120 may send a rain stamp to the precipitation analysis server 200 to indicate that precipitation is present in the image in which the precipitation is detected. Here, the precipitation analysis server 200 may be expressed as a precipitation post office, a precipitation monitoring dedicated server, a Rain-Post Office, and the like.

In the precipitation analysis step ( S200 ), the precipitation information is estimated by deep learning based on the data transmitted from the data collection unit 100 , and the resultant data is transmitted to the service platform 300 . Specifically, it may include extracting a precipitation image (S211), generating an artificial neural network model (S212), and estimating the amount of precipitation (S213).

In the precipitation analysis step ( S200 ), the precipitation analysis server 200 may include a control unit 210 for estimating precipitation information through deep learning, and a memory 220 for storing precipitation images as learning data.

The control unit 210 includes an image extraction unit 211 for extracting a precipitation image from the collected image data, a modeling unit 212 for generating an artificial neural network model for deep learning, and a weight for estimating the amount of precipitation based on the artificial neural network model. The government 213 may include a transmission unit 214 that transmits the result data to the service platform 300 .

In the step of extracting the precipitation image ( S211 ), the image extraction unit 211 may extract a precipitation image in a stationary state in which precipitation is detected from the image transmitted from the data collection unit 100 . Since the image consists of a photo or video (movie), the capture of the photo or video including the scene in which such precipitation is detected is extracted. However, it is also possible to extract a part of a video that is not in a still state.

In the step (S212) of generating the artificial neural network model, the modeling unit 212 inputs the precipitation image extracted from the image extraction unit 211 as learning data for deep learning. The training data may be stored in the memory 220 in the precipitation analysis server 200 .

A large amount of precipitation images may be collected to normalize the size of the precipitation image, and preprocessing may be performed to extract features from the images and store them in a DB. In the DB, copyright information (copyright holder, copyright protection period, creation date, copyright extinction information, etc.) of the collected river images may be stored together.

The modeling unit 212 applies artificial intelligence, and performs deep learning (deep-learning) on the precipitation information of the collected precipitation images. A deep learning algorithm can be used to infer precipitation information from a precipitation image.

Precipitation information can be learned by precisely analyzing the precipitation image based on the geomark included in the precipitation image and data from the Korea Meteorological Administration and the Ministry of Environment.

The amount of precipitation and the density of precipitation may be inferred from information on the number, size, and thickness of detected ripples per unit area or unit time.

FIG. 3 is an experimental diagram illustrating the process of estimating precipitation in ripples. In the precipitation image including ripples in FIG. 3-(a), shadow regions of the image are recognized as shown in FIGS. 3-(b) and 3-(c). By interpolating, the number, size, and thickness of ripples can be recognized to be measurable.

4 is an exemplary diagram of an experiment for extracting ripple information through an image processing algorithm. Referring to FIG. 3, ripples are detected (FIG. 4-(c)) through the image processing process (FIG. 4-(b)) in the original image (FIG. 4-(a)) taken directly with the iPhone, which is one of the smart phones. )can do.

In the step of estimating the amount of precipitation ( S213 ), the estimator 213 estimates precipitation information such as the amount of precipitation, density of precipitation, and intensity of precipitation of the transmitted data, that is, precipitation images, based on the artificial neural network model generated through deep learning. .

Thereafter, the transmitter 214 transmits the result data generated as a result of the estimation of the precipitation information of the estimator 213 to the service platform 300 .

The service platform 300 that has received the result data in the step S300 of providing the service to the user performs a service of providing information on the result of estimating the precipitation information such as the amount of precipitation and the intensity of precipitation in the precipitation analysis server 200 . do. When the video data collection path is through SNS, the precipitation information can be transmitted to the corresponding SNS platform and the precipitation information can be tagged together.

In addition, when the data is collected through the camera application of the smart terminal, the precipitation information may be displayed on the screen on which the corresponding image or photo is displayed. In this case, the precipitation information can be expressed overlaid on the image. In addition, precipitation information can be provided by an overlay method that allows an image or a photo to be played over the image.

When images are collected through a recording module inside a vehicle, such as a CCTV, a black box, or a recognition unit of an autonomous vehicle, risk information and disaster information can be provided along with precipitation information.

The service platform 300 may directly provide a precipitation information service to a user who has created a video image through the photographing module 110 as described above, or may provide a precipitation information service to a weather-related service provider.

The service platform 300 may include means for acquiring and analyzing information related to a meteorological service, such as precipitation information, and pushing the information to the service user terminal device.

In providing precipitation information to a meteorological service provider such as the Korea Meteorological Administration, geotag information included in a precipitation image and precipitation information estimated through deep learning may be provided together. Through this, information on precipitation, disaster, and safety can be provided to the user or the user's terminal, the user's vehicle, and the area where the CCTV is installed (S310).

5 is an exemplary view showing an effect expected by the implementation of the present invention.

Referring to FIG. 5 , by using the system according to an embodiment of the present invention for monitoring a city, a small radar for observing a city, a radio precipitation meter for monitoring ROI, a vehicle sensor (VISS) for monitoring a road network, and a city center for monitoring the weather for the entire city It is possible to create a smart city with information, road networks, and other spatiotemporal observations. Through this, effective disaster monitoring such as preemptive prevention of water disasters is possible.

In FIG. 5, Rain Stamper refers to the present system including the step of stamping a stamp indicating that precipitation exists according to an embodiment of the present invention.

In the above, even though all the components constituting the embodiment of the present invention are described as being combined or operating in combination, the present invention is not necessarily limited to this embodiment. That is, within the scope of the object of the present invention, all the components may operate by selectively combining one or more. In addition, although all of the components may be implemented as one independent hardware, some or all of the components are selectively combined to perform some or all functions of the combined components in one or a plurality of hardware program modules It may be implemented as a computer program having Codes and code segments constituting the computer program can be easily deduced by those skilled in the art of the present invention. Such a computer program is stored in a computer readable storage medium (Computer Readable Media), read and executed by the computer, thereby implementing the embodiment of the present invention.

100: data collection unit
110: shooting module
120: determination unit
200: precipitation analysis server
210: control unit
211: image extraction unit
212: modeling unit
213: estimator
214: transmission unit
220: memory
300: service platform

Claims (18)

In the precipitation information providing system using deep learning,
a data collection unit that acquires an image generated by a user through a method such as shooting and transmits it to a precipitation analysis server;
a precipitation analysis server that transmits analysis result data including precipitation information including precipitation intensity and precipitation amount to a service platform by utilizing deep learning based on the transmitted data; and
a service platform that provides a service related to the analysis result data;
Image-based precipitation information providing system comprising a. According to claim 1,
The data collection unit,
a photographing module for a user to generate a daily image including precipitation information; and
a determination unit that determines whether precipitation is present based on the image collected from the photographing module and transmits the collected image to a precipitation analysis server when it is determined that there is precipitation;
Image-based precipitation information providing system comprising a. 3. The method of claim 2,
The photographing module is
Video based, characterized in that it is any one of the outdoor shooting modules that generate a video image by constantly shooting a certain area, such as a camera, digital camera, CCTV, black box, and the recognition unit of an autonomous vehicle built into the user's smart terminal. of the precipitation information provision system. 3. The method of claim 2,
The determining unit,
When the collected images are directly collected from a user terminal including a photographing module or a photographing module,
An image-based precipitation information providing system, characterized in that it is determined whether precipitation exists by periodically detecting ripples and raindrops formed on the glass window from the collected images. 3. The method of claim 2,
The determining unit,
When the collected image is uploaded to the SNS platform by the user in the user terminal including the photographing module and collected through the corresponding SNS platform,
An image-based precipitation information providing system, characterized in that it determines the presence or absence of precipitation by analyzing hashtags and comments included in the post, and detecting ripples and raindrops on the glass window from the collected images. According to claim 1,
The precipitation analysis server,
a controller for estimating precipitation information through deep learning by detecting a precipitation image including precipitation information with respect to the transmitted data; and
a memory for storing the detected precipitation image as learning data;
Image-based precipitation information providing system comprising a. 7. The method of claim 6,
The control unit is
an image extraction unit for extracting a precipitation image including precipitation information from the input image when receiving one or more of the transmitted data;
a modeling unit for generating an artificial neural network model according to the degree of precipitation by inputting the extracted precipitation image as learning data for deep learning;
an estimator 213 for estimating the amount of precipitation of the transmitted data based on the artificial neural network model; and
a transmission unit for transmitting the analysis result data of the precipitation estimation to the service platform;
Image-based precipitation information providing system comprising a. According to claim 1,
The service platform is
When the image is collected through the SNS platform by the data collection unit, the analysis result data is tagged together on the SNS platform,
If the image is collected through a general shooting application, the analysis result data is included in the image overlapped,
An image-based precipitation information providing system, characterized in that it provides a service that provides analysis result data along with risk information and disaster information when an image is collected through an in-vehicle camera such as a CCTV or a black box. According to claim 1,
The service platform is
The image-based precipitation information providing system, characterized in that the analysis result data along with geotag information included in the precipitation image extracted from the precipitation analysis server is provided to a weather-related service provider. In the method of providing precipitation information using deep learning,
A data collection step of acquiring the image generated by the user through a method such as shooting by the data collection unit and transmitting it to the precipitation analysis server;
A precipitation analysis step of transmitting, in the precipitation analysis server, analysis result data including precipitation information including precipitation intensity and precipitation amount, etc. to a service platform by using deep learning based on the transmitted data; and
The service platform provides a service related to the analysis result data;
Image-based precipitation information providing method comprising a. 11. The method of claim 10,
The data collection step is
a step of a user taking a daily image including precipitation information by using a photographing module; and
a precipitation determination step of determining the presence or absence of precipitation based on the image collected from the photographing module and transmitting the collected image to a precipitation analysis server when it is determined that there is precipitation;
Image-based precipitation information providing method comprising a. 12. The method of claim 11,
The photographing module is
Video based, characterized in that it is any one of the outdoor shooting modules that generate a video image by constantly shooting a certain area, such as a camera, digital camera, CCTV, black box, and the recognition unit of an autonomous vehicle built into the user's smart terminal. of precipitation information. 12. The method of claim 11,
The precipitation determination step is
When the collected images are directly collected from a user terminal including a photographing module or a photographing module,
An image-based precipitation information providing method, characterized in that it is determined whether there is precipitation by periodically detecting ripples and raindrops formed on a glass window from the collected images. 12. The method of claim 11,
The precipitation determination step is
When the collected image is uploaded to the SNS platform by the user in the user terminal including the photographing module and collected through the corresponding SNS platform,
An image-based precipitation information providing method, characterized in that it is determined whether there is precipitation by analyzing the hashtags and comments included in the post, and detecting ripples and raindrops on the glass window from the collected images. 11. The method of claim 10,
The precipitation analysis step is,
An image-based precipitation information providing method, characterized in that the precipitation analysis server detects a precipitation image with respect to the transmitted data, stores the detected precipitation image as learning data in a memory, and estimates precipitation information through deep learning. 11. The method of claim 10,
The precipitation analysis step is,
When the image extraction unit receives one or more of the transmitted data, extracting a precipitation image including precipitation information from the input image;
The modeling unit generates an artificial neural network model according to the degree of precipitation by inputting the extracted precipitation image as training data for deep learning;
estimating, by an estimator, the amount of precipitation of the transmitted data based on the artificial neural network model; and
Transmitting, by the transmission unit, the analysis result data of the precipitation estimation to the service platform;
Image-based precipitation information providing method comprising a. 11. The method of claim 10,
The service platform is
When the data collection unit acquires the image through the SNS platform, it tags the analysis result data on the SNS platform together,
In the case of acquiring an image through a general shooting application, the analysis result data is included in the image overlapped,
An image-based precipitation information providing method, characterized in that when an image is acquired through an internal camera such as a CCTV or a black box, a service for providing analysis result data along with risk information and disaster information is provided to the user. 11. The method of claim 10,
The service platform is
An image-based precipitation information providing method, characterized in that the analysis result data is provided to a weather-related service provider together with geotag information included in the precipitation image extracted from the precipitation analysis server.

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