CN112733781A - City functional area identification method combining POI data, storage medium and electronic equipment - Google Patents
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Abstract
The invention provides a method for identifying a city functional area by combining POI data, a storage medium and electronic equipment, wherein the method for identifying the city functional area comprises the following steps: acquiring a high-resolution remote sensing image of a target area according to the acquired target area information; acquiring urban road network information of a target area according to the target area information, and acquiring block information of the target area according to the urban road network information; dividing the high-resolution remote sensing image according to the street information to obtain a street remote sensing image; dividing the block remote sensing image by using a selective searching algorithm to obtain a small block scene image; identifying the small scene images by using the scene identification model to obtain scene identification results of the small scene images; and fusing the scene recognition result and POI data of each block, and performing topic modeling by using a hidden Dirichlet distribution model to mine the potential semantic features of the blocks to obtain the urban function recognition result of each block. The method can improve the accuracy rate of identifying the urban functional area.
Description
Technical Field
The invention relates to the field of image recognition, in particular to a method for recognizing a city functional area by combining POI data, a storage medium and electronic equipment.
Background
The city functional area is a region space which can realize the aggregation of related social resource spaces and effectively exert certain specific city functions, intensively reflects the characteristics of the city, and is a form of modern city development. In the existing research, scholars mainly divide the urban functional space into residential areas, business areas, scientific and educational areas, industrial areas, public service areas, leisure areas, transportation areas and the like. The urban functional area is accurately identified, so that the development of an urban planning theory can be promoted, an optimization suggestion can be provided for the spatial distribution pattern of the city, and a more scientific and reasonable urban spatial structure system is formed.
At present, urban functional area identification is mainly carried out through a single method, but the method is low in efficiency, accuracy cannot be guaranteed, and the requirement for quickly and accurately identifying urban functional areas under high-speed urban development cannot be met.
Disclosure of Invention
The invention aims to solve the problems of low efficiency and low accuracy of a mode for identifying urban functional areas by a single method in the prior art.
In order to solve the above problems, the present invention provides a method for identifying a functional area of a city by using POI data, comprising:
(a) acquiring target area information, and acquiring a high-resolution remote sensing image of the target area according to the target area information;
(b) acquiring urban road network information of the target area according to the target area information, and acquiring block information of the target area according to the urban road network information;
(c) dividing the high-resolution remote sensing image according to the block information to obtain a block remote sensing image of each block;
(d) dividing the block remote sensing images by using a selective searching algorithm to obtain small scene images of each block remote sensing image;
(e) identifying the small scene images by using a scene identification model to obtain a scene identification result of each small scene image;
(f) POI data of each block are obtained according to the block information;
(g) and fusing the scene recognition result and the POI data of each block, and performing topic modeling by using a hidden Dirichlet distribution model to mine the potential semantic features of the blocks to obtain the urban function recognition result of each block.
Optionally, the method for identifying a functional area of a city based on POI data further includes, between step (a) and step (c):
carrying out data processing on the high-resolution remote sensing image to obtain a processed remote sensing image after data processing;
in the step (c), dividing the processed remote sensing image according to the block information to obtain the block remote sensing image of each block.
Optionally, in the method for identifying a functional area of a city according to POI data, step (d) specifically includes:
acquiring a remote sensing image data set required by scene recognition, and training a convolutional neural network model by using the data set to obtain a remote sensing image scene recognition model;
and identifying the small scene images by using the scene identification model to obtain a scene identification result of each small scene image.
Optionally, the method for identifying a functional area of a city by using POI data further includes:
(h) acquiring GPS data of a taxi;
(i) and verifying the urban functional area division result by using the GPS data.
Based on the same inventive concept, the present invention also provides a computer-readable storage medium:
the storage medium stores program instructions, and the computer reads the program instructions and then executes the method for identifying the urban functional area by combining the POI data.
Based on the same inventive concept, the present invention also provides an electronic device, comprising:
at least one processor and at least one memory;
at least one memory stores program instructions, and at least one processor reads the program instructions and executes the method for identifying the urban functional area by combining the POI data.
According to the urban functional area identification method combining POI data, the storage medium and the electronic equipment, the urban functional area identification method divides the block remote sensing image by selecting a searching algorithm, and then identifies the small scene image by using the scene identification model so as to facilitate the identification of the small scene image. And finally, fusing the identification result with POI data to obtain a block to obtain a city functional area identification model, wherein the POI data is fused, so that the identification result is more accurate.
Drawings
Fig. 1 is a flowchart of a method for identifying a functional area of a city according to POI data in embodiment 1 of the present invention;
fig. 2 is a flowchart of a method for identifying a functional area of a city according to POI data in embodiment 2 of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to embodiment 4 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
Example 1
The embodiment provides a method for identifying a city functional area by combining POI data, which is applied to electronic equipment, and as shown in fig. 1, the method includes the following steps:
s101, obtaining target area information, and obtaining a remote sensing image of the target area according to the target area information.
The target area information can be manually input, or complete information can be acquired from the Internet according to the manually input partial information. The target area information includes a target area name, a target area coordinate, and the like, where the target area name is an administrative district name, such as beijing city and hebei province, the target area coordinate is a position of the target area in a geographic coordinate system, the geographic coordinate system may be one of a WGS-84 coordinate system, beijing 54 and sienna 80, different coordinate systems may be mutually converted, and the WGS-84 coordinate system is taken as an example in this embodiment.
The remote sensing image of the target area can be obtained locally or directly from the internet according to the target area information, and the remote sensing image preferably is a high-resolution remote sensing image. And locally acquiring a database capable of establishing the remote sensing image locally, wherein the database comprises the remote sensing image and an image coordinate corresponding to the remote sensing image. During operation, matching the image coordinates of the remote sensing images in the database with the target area coordinates, if at least partial overlap exists between the image coordinates and the target area coordinates, obtaining the remote sensing images corresponding to the image coordinates, splicing at least one obtained remote sensing image according to the coordinates, and cutting according to target area information to obtain the remote sensing images of the target area.
The process of obtaining the remote sensing images from the internet is similar to the process of obtaining local data, namely matching is carried out according to the coordinates of a target area and the image coordinates of the remote sensing images on the internet, if the image coordinates are at least partially overlapped with the coordinates of the target area, the remote sensing images corresponding to the image coordinates are obtained, at least one obtained remote sensing image is spliced according to the coordinates to obtain the remote sensing images of the target area, and preprocessing such as atmospheric correction and geometric correction is carried out.
S102, obtaining road network information of the target area according to the target area information, and obtaining street information of the target area according to the road network information.
The road network information of the target area comprises road network data of the target area, and the road network information of the target area can be acquired from an Open Street Map (OSM) according to the target area information. The road grade in the OSM comprises an expressway, a main road, a first-level road, a second-level road, a third-level road, an auxiliary road, a sidewalk, a community road, a street, a living street, a path, a walking road, a stair road, a track and the like, and part of the roads can be selected according to needs in operation. In the embodiment, high-speed roads, main roads, primary roads, secondary roads and tertiary roads in a target area are selected as examples, and road network data of the levels are subjected to topology and connection processing to remove redundant miscellaneous roads, suspension roads and broken roads; then, a buffer area is established according to the processed road, and the buffer width of the buffer area can be set according to needs, for example, 20m, 10m and 5m can be set according to the road grade; and finally, obtaining block information according to the road and the buffer area thereof. The block information of the target area comprises the shape, the size and the block coordinates of a block surrounded by the road and the buffer area thereof in the target area.
S103, dividing the remote sensing image according to the block information to obtain the block remote sensing image of each block.
Namely, the street block coordinates in step S102 and the image coordinates in step S101 are converted into the same coordinate system, the street block and the remote sensing image are overlapped in the same coordinate system, and the remote sensing image obtained in step S101 is divided by using the shape and size of the street block obtained in step S102, so as to obtain the remote sensing image of each street block.
And S104, dividing the block remote sensing images by using a selective searching algorithm to obtain small scene images of each block remote sensing image.
And dividing the block scene images of the block remote sensing image by using a selective searching algorithm, so that each block scene image only comprises simple scenes.
And S105, identifying the small scene images by using a scene identification model to obtain a scene identification result of each small scene image.
Firstly, a remote sensing image scene recognition model is trained by using a remote sensing image data set to obtain various model parameters of an optimal training result, and then small scene images of each block are recognized by using the trained model to obtain scene categories and corresponding quantity contained in each block.
And S106, acquiring POI data of each block according to the block information.
The method comprises the steps of acquiring POI data of a target area by using technologies such as a web crawler, carrying out preprocessing such as cleaning and duplicate removal on the data, then carrying out re-classification, converting the block coordinates and the POI data coordinates in the step S102 into the same coordinate system, putting blocks into the coordinate system of the POI according to the block coordinates, putting the POI data into the coordinate system according to the POI data coordinates, dividing the POI data by using the shape and size of the blocks obtained in the step S102 to obtain the POI data of each block, and carrying out POI data category and quantity statistics.
S107, combining the scene recognition result and the POI data category quantity of each block, and utilizing a hidden Dirichlet distribution model to carry out topic modeling and mining on the potential semantic features of the blocks to obtain the city function recognition result of each block.
In the process of urban functional area recognition research, each area can be taken as a document, different types of POI and scene recognition results in the area are taken as words, the type of the functional area is a theme, and therefore mapping is established between data of an urban category and key elements of a theme model, and urban functional area division can be achieved by utilizing a hidden Dirichlet allocation model.
In the embodiment, the block remote sensing image is divided by selecting a searching algorithm, and then the small-block scene image is identified by using the scene identification model, so that the small-block scene image is identified conveniently. And finally, fusing the identification result with POI data to obtain a block to obtain a city functional area identification model, wherein the POI data is fused, so that the identification result is more accurate.
Example 2
The embodiment provides a method for identifying a city functional area by combining POI data, which is applied to electronic equipment, and as shown in fig. 2, the method includes the following steps:
s201, obtaining target area information, obtaining road network information of the target area according to the target area information, and obtaining street information of the target area according to the road network information.
The target area information can be manually input, or complete information can be acquired from the Internet according to the manually input partial information. The target area information includes an area name, coordinates thereof, and the like, where the area name is an administrative area name, such as beijing city, hebeibei province, and the like, the target area coordinates are positions of the target area in geographic coordinates, the geographic coordinate system may be a WGS-84 coordinate system, beijing 54, sienna 80, and the like, and different coordinate systems may be mutually converted, and in this embodiment, the WGS-84 coordinate system is taken as an example.
The road network information of the target area comprises road network data of the target area, and the road network information of the target area can be acquired from an Open Street Map (OSM) according to the target area information. The road grade in the OSM comprises an expressway, a main road, a first-level road, a second-level road, a third-level road, an auxiliary road, a sidewalk, a community road, a street, a living street, a path, a walking road, a stair road, a track and the like, and part of the roads can be selected according to needs in operation. In the embodiment, high-speed roads, main roads, primary roads, secondary roads and tertiary roads in a target area are selected as examples, and road network data of the levels are subjected to topology and connection processing to remove redundant miscellaneous roads, suspension roads, broken roads and the like; then, a buffer area is established according to the processed road, and the buffer width of the buffer area can be set according to needs, for example, 20m, 10m and 5m can be set according to the road grade; and finally, obtaining block information according to the road and the buffer area thereof. The block information of the target area comprises the shape, the size and the block coordinates of a block surrounded by the road and the buffer area thereof in the target area.
And S202, acquiring a remote sensing image of the target area according to the target area information.
The remote sensing image of the target area can be obtained locally or directly from the internet according to the target area information, and the remote sensing image preferably is a high-resolution remote sensing image. And locally acquiring a database capable of establishing the remote sensing image locally, wherein the database comprises the remote sensing image and an image coordinate corresponding to the remote sensing image. During operation, matching the image coordinates of the remote sensing images in the database with the target area coordinates, if at least partial overlap exists between the image coordinates and the target area coordinates, obtaining the remote sensing images corresponding to the image coordinates, splicing at least one obtained remote sensing image according to the coordinates, and cutting according to target area information to obtain the remote sensing images of the target area.
The process of obtaining the remote sensing image from the internet is similar to the process of obtaining the local data, namely, the target area coordinates are matched with the image coordinates of the remote sensing images on the internet, if at least part of the image coordinates are overlapped with the coordinates of the target area, the remote sensing images corresponding to the image coordinates are obtained, and at least one obtained remote sensing image is spliced according to the coordinates to obtain the remote sensing image of the target area.
And S203, carrying out data processing on the remote sensing image to obtain a processed remote sensing image after data processing.
In the step, the steps of radiometric calibration, atmospheric correction, geometric correction, orthometric correction and the like are mainly carried out on the remote sensing image. Radiometric calibration means that pixel brightness values of recorded original remote sensing images are converted into radiometric values, and radiometric calibration formulas of different sensors are different. The atmospheric correction converts the radiance or the surface reflectivity into the actual surface reflectivity, and specifically, a wmorran model, a wLOWTRAN model, a wtcor model, a w6S model and the like can be adopted. The geometric correction process is to carry out geographic coordinate positioning on the remote sensing shadow to obtain real coordinate information. The orthorectification is a process of generating a planar orthoimage by eliminating the influence of the terrain or the deformation caused by the orientation of the camera. Radiometric calibration, atmospheric correction, geometric correction and ortho correction in this step are general processes for remote sensing data processing, and are not described herein again.
And S204, dividing the processed remote sensing image according to the block information to obtain the block remote sensing image of each block.
Converting the street block coordinates in the step S201 and the image coordinates in the step S203 into the same coordinate system, putting the street blocks into the coordinate system according to the street block coordinates, putting the remote sensing image into the coordinate system according to the image coordinates, and dividing the remote sensing image obtained in the step S203 by using the shape and size of the street blocks obtained in the step S201 to obtain the remote sensing image of each street block.
S205, dividing the block remote sensing images by using a selective searching algorithm to obtain small scene images of each block remote sensing image.
And dividing the block scene images of the block remote sensing image by using a selective searching algorithm, so that each block scene image only comprises simple scenes.
S206, obtaining a scene image training sample, and training a convolutional neural network model by using the training sample to obtain a scene recognition model.
Firstly, a remote sensing image scene recognition model is trained by using a remote sensing image data set to obtain various model parameters of an optimal training result.
And S207, identifying the small scene images by using a scene identification model to obtain a scene identification result of each small scene image.
And identifying the small scene images of each block by using the trained model to obtain the scene categories and the corresponding quantity contained in each block.
And S208, acquiring POI data of each block according to the block information.
The POI information can be obtained from a local POI database or the Internet. The POI data includes a name, coordinates, an address, and the like, and thus the neighborhood coordinates in the neighborhood information are matched with the coordinates of the POI data to obtain neighborhood POI data of each neighborhood. The POI data is then pre-processed, including data cleansing and data deduplication.
S209, fusing the scene recognition result and the POI data of each block, and utilizing a hidden Dirichlet distribution model to carry out topic modeling and mining on the potential semantic features of the blocks to obtain the city function recognition result of each block.
In the process of urban functional area recognition research, each area can be taken as a document, different types of POI and scene recognition results in the area are taken as words, the type of the functional area is a theme, and therefore mapping is established between data of an urban category and key elements of a theme model, and urban functional area division can be achieved by utilizing a hidden Dirichlet allocation model.
And S210, acquiring GPS data of the taxi.
And acquiring taxi GPS data by visiting a company official website, sorting the data, and removing the data outside the target area and the repeated data.
And S211, verifying the urban functional area division result by using the GPS data.
By analyzing taxi GPS data working days, non-working days and peak time periods in days in the blocks, taxi GPS information statistical data of each block are obtained, the taxi GPS information statistical data are classified and compared with the city functional areas to which the blocks belong, and the city functional area identification accuracy is judged.
According to the embodiment, the urban function identification result is verified through the GPS data of the taxi, so that the result is more accurate.
Example 3
The present embodiment provides a computer-readable storage medium, where program instructions are stored in the storage medium, and a computer reads the program instructions to execute the method for identifying a functional area of a city in combination with POI data according to embodiment 1 or 2.
Example 4
The present embodiment provides an electronic device, as shown in fig. 3, which includes at least one processor 301; and a memory 302 communicatively coupled to the at least one processor 301; the memory 302 stores instructions executable by the processor 301, and the instructions are executed by the at least one processor 301, so that the at least one processor 301 can execute the method for identifying a functional area of a city in combination with POI data according to any one of the embodiments 1 and 2, and has corresponding advantages for executing the method. For technical details which are not described in detail in this example, reference is made to the method provided in example 1 or 2 of the present application.
In fig. 3, taking one processor 301 as an example, the electronic device may further include: an input device 303 and an output device 304. The processor 301, the memory 302, the input device 303 and the output device 304 may be connected by a bus or other means, and fig. 3 illustrates the connection by a bus as an example.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.
Claims (6)
1. A method for identifying a city functional area by combining POI data is characterized by comprising the following steps:
(a) acquiring target area information, and acquiring a high-resolution remote sensing image of the target area according to the target area information;
(b) acquiring urban road network information of the target area according to the target area information, and acquiring block information of the target area according to the urban road network information;
(c) dividing the high-resolution remote sensing image according to the block information to obtain a block remote sensing image of each block;
(d) dividing the block remote sensing images by using a selective searching algorithm to obtain small scene images of each block remote sensing image;
(e) identifying the small scene images by using a scene identification model to obtain a scene identification result of each small scene image;
(f) POI data of each block are obtained according to the block information;
(g) and fusing the scene recognition result and the POI data of each block, and performing topic modeling by using a hidden Dirichlet distribution model to mine the potential semantic features of the blocks to obtain the urban function recognition result of each block.
2. The method for identifying urban functional areas based on POI data as claimed in claim 1, wherein between step (a) and step (c), further comprising:
carrying out data processing on the high-resolution remote sensing image to obtain a processed remote sensing image after data processing;
in the step (c), dividing the processed remote sensing image according to the block information to obtain the block remote sensing image of each block.
3. The method for identifying urban functional areas in combination with POI data as claimed in claim 1 or 2, wherein the step (d) comprises:
acquiring a remote sensing image data set required by scene recognition, and training a convolutional neural network model by using the data set to obtain a remote sensing image scene recognition model;
and identifying the small scene images by using the scene identification model to obtain a scene identification result of each small scene image.
4. The method for identifying urban functional areas based on POI data as recited in claim 3, further comprising:
(h) acquiring GPS data of a taxi;
(i) and verifying the urban functional area division result by using the GPS data.
5. A computer-readable storage medium characterized by:
the storage medium stores program instructions, and the computer executes the method for identifying the functional area of the city by combining the POI data according to claims 1 to 4 after reading the program instructions.
6. An electronic device, comprising:
at least one processor and at least one memory;
at least one of the memories stores program instructions, and at least one of the processors executes the method for identifying a functional area of a city according to any one of claims 1 to 4 in conjunction with POI data after reading the program instructions.
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| CN115272854A (en) * | 2022-07-27 | 2022-11-01 | 清华大学 | Palm area identification method and product based on multi-source information analysis |
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| CN113468982A (en) * | 2021-06-11 | 2021-10-01 | 北京建筑大学 | Method, device and storage medium for classifying urban functional areas |
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| CN114819852B (en) * | 2022-05-24 | 2022-11-22 | 深圳市神州龙智慧城市科技有限公司 | Logistics planning system and method for smart city |
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