CN110647883A - Method and device for mining point of interest (POI) data - Google Patents
Method and device for mining point of interest (POI) data Download PDFInfo
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- CN110647883A CN110647883A CN201811635508.3A CN201811635508A CN110647883A CN 110647883 A CN110647883 A CN 110647883A CN 201811635508 A CN201811635508 A CN 201811635508A CN 110647883 A CN110647883 A CN 110647883A
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/24—Aligning, centring, orientation detection or correction of the image
- G06V10/243—Aligning, centring, orientation detection or correction of the image by compensating for image skew or non-uniform image deformations
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Abstract
The invention discloses a method and a device for mining POI data. The method comprises the following steps: carrying out image recognition on the map to obtain name text data and icon data; determining the name of the POI according to the name text data, and determining the type of the POI according to the icon data; and generating one or more pieces of POI data according to the determined POI names and POI types. The method provided by the technical scheme can identify two important parameters, namely the type and the name of the POI data from the map, has high identification accuracy and good effect, can quickly acquire the update of the POI along with the update of the map, and the obtained POI data can be used for cross-verifying the validity and the timeliness of the existing POI data, thereby realizing the accuracy improvement and the shortening of the update period of the existing POI data and having high practicability.
Description
Technical Field
The invention relates to the technical field of electronic maps, in particular to a method and a device for mining POI data.
Background
A POI is usually an abbreviation of point of interest, and may also be referred to as a point of information. Points of interest and information points may be identified in the map as banks, sights, companies, hospitals, government agencies, restaurants, malls, and the like. The "point of interest" and "POI" in the present invention both refer to the above meanings.
At present, the source of POI data does not rely on manual collection, which is a low-efficiency method, and can obtain the relevant information of the POI through an interface of an electronic map provider in the internet. However, due to industry competition, electronic map providers are often provided with protection mechanisms, so that a certain proportion of dirty data, such as names and positions, are inaccurate in the acquired POI data.
Disclosure of Invention
In view of the above, the present invention has been made to provide a method and apparatus for mining point of interest POI data that overcome the above problems or at least partially solve the above problems.
According to an aspect of the present invention, there is provided a method for mining point of interest POI data, including:
carrying out image recognition on the map to obtain name text data and icon data;
determining the name of the POI according to the name text data, and determining the type of the POI according to the icon data;
and generating one or more pieces of POI data according to the determined POI names and POI types.
Optionally, the method further comprises:
obtaining a map tile map, and storing the obtained map tile map according to a standard coding rule;
the image recognition of the map comprises: carrying out image recognition on each map tile map;
the generating one or more pieces of POI data according to the determined POI name and POI type includes: and determining the geographical position information in the POI data according to the standard coding rule.
Optionally, the obtaining the map tile map includes:
a map tile map is obtained from the Internet at a specified display level.
Optionally, the specified display level is a display level with the largest scale provided by an electronic map provider.
Optionally, the image recognizing the map to obtain the name text data and the icon data includes:
the text data is identified according to a text identification model, and the icon data is identified according to an icon identification model.
Optionally, the method further comprises:
generating a text sample image according to preset text information;
and training to obtain a text recognition model by taking the character information and the character sample image as training samples.
Optionally, the method further comprises:
obtaining a map legend;
and taking the map legend as a training sample, and training to obtain an icon identification model.
Optionally, the method further comprises:
and comparing and verifying POI data in a POI database by using the generated POI data, and updating the POI database according to a verification result.
According to another aspect of the present invention, there is provided a device for mining POI data, including:
the map unit is suitable for carrying out image recognition on a map to obtain name text data and icon data;
a POI data generating unit adapted to determine a POI name from said name text data and a POI type from said icon data; and generating one or more pieces of POI data according to the determined POI names and POI types.
Optionally, the map unit is further adapted to obtain a map tile map, and store the obtained map tile map according to a standard encoding rule; and is adapted to perform image recognition on each map tile map;
the POI data generating unit is suitable for determining the geographical position information in the POI data according to the standard coding rule.
Optionally, the map unit is adapted to obtain a map tile map of a specified display level from the internet.
Optionally, the specified display level is a display level with the largest scale provided by an electronic map provider.
Optionally, the map unit is adapted to identify the text data according to a text recognition model and identify the icon data according to an icon recognition model.
Optionally, the map unit is adapted to generate a text sample image according to preset text information; and training to obtain a text recognition model by taking the character information and the character sample image as training samples.
Optionally, the map unit is adapted to obtain a map legend, and train the map legend as a training sample to obtain an icon identification model.
Optionally, the apparatus further comprises:
and the POI data verification unit is suitable for comparing and verifying the POI data in the POI database by using the generated POI data and updating the POI database according to a verification result.
According to still another aspect of the present invention, there is provided an intelligent terminal including: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to perform a method as any one of the above.
According to a further aspect of the invention, there is provided a computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement a method as any one of the above.
As can be seen from the above, according to the technical scheme of the present invention, by identifying the map, the type and name of the POI are respectively determined according to the obtained icon data and text data, and accordingly, the corresponding POI data can be generated. The method provided by the technical scheme can identify two important parameters, namely the type and the name of the POI data from the map, has high identification accuracy and good effect, can quickly acquire the update of the POI along with the update of the map, and the obtained POI data can be used for cross-verifying the validity and the timeliness of the existing POI data, thereby realizing the accuracy improvement and the shortening of the update period of the existing POI data and having high practicability.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a schematic flowchart illustrating a method for mining POI data according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram illustrating a point of interest POI data mining apparatus according to an embodiment of the present invention;
FIG. 3 shows a schematic structural diagram of an intelligent terminal according to one embodiment of the invention;
FIG. 4 shows a schematic structural diagram of a computer-readable storage medium according to one embodiment of the invention;
fig. 5 shows a part of a map.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Fig. 1 is a flowchart illustrating a method for mining POI data according to an embodiment of the present invention. As shown in fig. 1, the method includes:
step S110, carrying out image recognition on the map to obtain name text data and icon data.
Fig. 5 shows a part of a map. From fig. 5, a plurality of POIs such as creative culture squares, blessing temple bridges, middle village E world, and intimate spring can be seen. Wherein, in spring, the subway station can be known according to the legend, the mall E world can be known as the market according to the legend, and the blessing temple bridge can be known as the bridge. Therefore, the information in the map is very rich, and a plurality of POI can be identified on the basis of the map, and corresponding name text data and icon data are obtained.
Step S120, the POI name is determined from the name text data, and the POI type is determined from the icon data.
As shown in the mingchun in fig. 5, the POI name is "mingchun" and the POI type is "subway station".
And step S130, generating one or more pieces of POI data according to the determined POI names and POI types.
As can be seen, in the method shown in fig. 1, by identifying the map, the type and name of the POI are determined according to the obtained icon data and text data, and accordingly, corresponding POI data can be generated. The method provided by the technical scheme can identify two important parameters, namely the type and the name of the POI data from the map, has high identification accuracy and good effect, can quickly acquire the update of the POI along with the update of the map, and the obtained POI data can be used for cross-verifying the validity and the timeliness of the existing POI data, thereby realizing the accuracy improvement and the shortening of the update period of the existing POI data and having high practicability.
In an embodiment of the present invention, the method further includes: obtaining a map tile map, and storing the obtained map tile map according to a standard coding rule; the image recognition of the map comprises: carrying out image recognition on each map tile map; generating one or more pieces of POI data according to the determined POI name and POI type includes: geographic location information in the POI data is determined according to standard encoding rules.
In this embodiment, the map may be an electronic map, and as known from the electronic map, the map is composed of a plurality of map tiles, and therefore, the obtained object may be a map tile, and may be stored according to a standard encoding rule. Therefore, when the POI related information is identified, the corresponding geographic position information can be actually determined, and the obtained POI data is basically complete.
In an embodiment of the present invention, the method for obtaining a map tile map includes: a map tile map is obtained from the Internet at a specified display level.
For example, a map tile map of a specified display level is acquired by a service of an electronic map provider in the internet such as a Baidu map, a Google map, or the like. At different display levels the scale is different, whereas at larger scales the number of POIs displayed is generally greater, but the number of map tiles that need to be identified is also greater, and vice versa. Therefore, the appropriate display level can be selected according to actual needs. In an embodiment of the present invention, in the method, the display level is specified as a display level at which a scale provided by an electronic map provider is maximized. That is, in this embodiment, it is desirable to identify more POIs. Accordingly, the number of map tile maps that need to be identified is also greater.
In an embodiment of the present invention, in the method, the image recognition of the map to obtain the name text data and the icon data includes: the text data is identified according to a text identification model, and the icon data is identified according to an icon identification model.
In the embodiment, the map is subjected to image recognition through a pre-trained text recognition model and an icon recognition model. In particular, the model may be obtained by machine learning training. The specific machine learning network may be selected by referring to various neural networks in the prior art, which is not limited herein.
Examples of model training are given below with two embodiments:
in an embodiment of the present invention, the method further includes: generating a text sample image according to preset text information; and taking the character information and the character sample image as training samples, and training to obtain a text recognition model.
In the selection process of the training samples, the text sample images can be correspondingly set according to the text attributes in the map to be recognized. For example, one or more of the following factors may be considered: character size, character color, character interval, character font, and character typesetting.
In an embodiment of the present invention, the method further includes: obtaining a map legend; and taking the map legend as a training sample, and training to obtain the icon identification model.
Map legends are important aids for users to get useful information from maps. Generally, the map legend may show bus stops, road bridges, mall hospitals, and the like.
Besides the manner of obtaining the map legend, the training samples may be provided in other manners, for example, images corresponding to the specified POI types are selected from the map as the training samples according to a manual labeling manner.
In one specific example, a large green area in the map is labeled as a golf course, and then is used as a training sample, so that other green areas in the map can be identified as golf courses.
In an embodiment of the present invention, the method further includes: and comparing and verifying the POI data in the POI database by using the generated POI data, and updating the POI database according to a verification result.
For example, if the generated POI data and the original POI data in the POI database are determined to correspond to the same POI according to the similarity during the comparison verification, the corresponding item in the original POI data may be updated according to the type, name or geographical location in the generated POI data. In addition, for POI lacking the matching item in the POI database, the generated POI data can also be directly added into the POI database.
Fig. 2 is a schematic structural diagram of a device for mining point of interest POI data according to an embodiment of the present invention. As shown in fig. 2, the device 200 for mining POI data includes:
the map unit 210 is adapted to perform image recognition on the map, and obtain name text data and icon data.
Fig. 5 shows a part of a map. From fig. 5, a plurality of POIs such as creative culture squares, blessing temple bridges, middle village E world, and intimate spring can be seen. Wherein, in spring, the subway station can be known according to the legend, the mall E world can be known as the market according to the legend, and the blessing temple bridge can be known as the bridge. Therefore, the information in the map is very rich, and a plurality of POI can be identified on the basis of the map, and corresponding name text data and icon data are obtained.
A POI data generating unit 220 adapted to determine a POI name from the name text data and a POI type from the icon data; and generating one or more pieces of POI data according to the determined POI names and POI types.
As shown in the mingchun in fig. 5, the POI name is "mingchun" and the POI type is "subway station".
As can be seen, the apparatus shown in fig. 2 identifies the map, and determines the type and name of the POI according to the obtained icon data and text data, so as to generate corresponding POI data. The method provided by the technical scheme can identify two important parameters, namely the type and the name of the POI data from the map, has high identification accuracy and good effect, can quickly acquire the update of the POI along with the update of the map, and the obtained POI data can be used for cross-verifying the validity and the timeliness of the existing POI data, thereby realizing the accuracy improvement and the shortening of the update period of the existing POI data and having high practicability.
In an embodiment of the present invention, in the above apparatus, the map unit 210 is further adapted to obtain a map tile map, and store the obtained map tile map according to a standard encoding rule; and is adapted to perform image recognition on each map tile map; the POI data generating unit 220 is adapted to determine the geographical location information in the POI data according to standard coding rules.
In this embodiment, the map may be an electronic map, and as known from the electronic map, the map is composed of a plurality of map tiles, and therefore, the obtained object may be a map tile, and may be stored according to a standard encoding rule. Therefore, when the POI related information is identified, the corresponding geographic position information can be actually determined, and the obtained POI data is basically complete.
In an embodiment of the present invention, in the above apparatus, the map unit 210 is adapted to obtain the map tile map with the specified display level from the internet.
For example, a map tile map of a specified display level is acquired by a service of an electronic map provider in the internet such as a Baidu map, a Google map, or the like. At different display levels the scale is different, whereas at larger scales the number of POIs displayed is generally greater, but the number of map tiles that need to be identified is also greater, and vice versa. Therefore, the appropriate display level can be selected according to actual needs. In one embodiment of the present invention, in the above apparatus, the display level is specified as a display level at which a scale provided by an electronic map provider is maximized. That is, in this embodiment, it is desirable to identify more POIs. Accordingly, the number of map tile maps that need to be identified is also greater.
In an embodiment of the present invention, in the above apparatus, the map unit 210 is adapted to recognize the text data according to a text recognition model and recognize the icon data according to an icon recognition model.
In the embodiment, the map is subjected to image recognition through a pre-trained text recognition model and an icon recognition model. In particular, the model may be obtained by machine learning training. The specific machine learning network may be selected by referring to various neural networks in the prior art, which is not limited herein.
Examples of model training are given below with two embodiments:
in an embodiment of the present invention, in the above apparatus, the map unit 210 is adapted to generate a text sample image according to preset text information; and taking the character information and the character sample image as training samples, and training to obtain a text recognition model.
In the selection process of the training samples, the text sample images can be correspondingly set according to the text attributes in the map to be recognized. For example, one or more of the following factors may be considered: character size, character color, character interval, character font, and character typesetting.
In an embodiment of the present invention, in the above apparatus, the map unit 210 is adapted to obtain a map legend, and train the map legend as a training sample to obtain the icon recognition model.
Map legends are important aids for users to get useful information from maps. Generally, the map legend may show bus stops, road bridges, mall hospitals, and the like.
Besides the manner of obtaining the map legend, the training samples may be provided in other manners, for example, images corresponding to the specified POI types are selected from the map as the training samples according to a manual labeling manner.
In one specific example, a large green area in the map is labeled as a golf course, and then is used as a training sample, so that other green areas in the map can be identified as golf courses.
In an embodiment of the present invention, the above apparatus further comprises: and the POI data verification unit (not shown in the figure 2) is suitable for performing comparison verification on the POI data in the POI database by using the generated POI data, and updating the POI database according to the verification result.
For example, if the generated POI data and the original POI data in the POI database are determined to correspond to the same POI according to the similarity during the comparison verification, the corresponding item in the original POI data may be updated according to the type, name or geographical location in the generated POI data. In addition, for POI lacking the matching item in the POI database, the generated POI data can also be directly added into the POI database.
In summary, according to the technical scheme of the present invention, by identifying the map, the type and name of the POI are respectively determined according to the obtained icon data and text data, and accordingly, the corresponding POI data can be generated. The method provided by the technical scheme can identify two important parameters, namely the type and the name of the POI data from the map, has high identification accuracy and good effect, can quickly acquire the update of the POI along with the update of the map, and the obtained POI data can be used for cross-verifying the validity and the timeliness of the existing POI data, thereby realizing the accuracy improvement and the shortening of the update period of the existing POI data and having high practicability.
It should be noted that:
the algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose devices may be used with the teachings herein. The required structure for constructing such a device will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It will be appreciated by those skilled in the art that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components of the point of interest POI data mining apparatus according to embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
For example, fig. 3 shows a schematic structural diagram of an intelligent terminal according to an embodiment of the present invention. The intelligent terminal comprises a processor 310 and a memory 320 arranged to store computer executable instructions (computer readable program code). The memory 320 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. The memory 320 has a storage space 330 storing computer readable program code 331 for performing any of the method steps described above. For example, the storage space 330 for storing the computer readable program code may comprise respective computer readable program codes 331 for respectively implementing various steps in the above method. The computer readable program code 331 may be read from or written to one or more computer program products. These computer program products comprise a program code carrier such as a hard disk, a Compact Disc (CD), a memory card or a floppy disk. Such a computer program product is typically a computer readable storage medium such as described in fig. 4. Fig. 4 shows a schematic structural diagram of a computer-readable storage medium according to an embodiment of the present invention. The computer readable storage medium 400 stores computer readable program code 331 for performing the steps of the method according to the present invention, which is readable by the processor 310 of the smart terminal 300 and when the computer readable program code 331 is executed by the smart terminal 300, causes the smart terminal 300 to perform the steps of the method described above, and in particular, the computer readable program code 331 stored by the computer readable storage medium may perform the method shown in any of the embodiments described above. The computer readable program code 331 may be compressed in a suitable form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (10)
1. A method for mining point of interest (POI) data comprises the following steps:
carrying out image recognition on the map to obtain name text data and icon data;
determining the name of the POI according to the name text data, and determining the type of the POI according to the icon data;
and generating one or more pieces of POI data according to the determined POI names and POI types.
2. The method of claim 1, wherein the method further comprises:
obtaining a map tile map, and storing the obtained map tile map according to a standard coding rule;
the image recognition of the map comprises: carrying out image recognition on each map tile map;
the generating one or more pieces of POI data according to the determined POI name and POI type includes: and determining the geographical position information in the POI data according to the standard coding rule.
3. The method of any of claims 1-2, wherein the obtaining a map tile map comprises:
a map tile map is obtained from the Internet at a specified display level.
4. The method of any one of claims 1-3, wherein the specified display level is a display level at which a scale provided by an electronic map provider is maximized.
5. An apparatus for mining POI data, comprising:
the map unit is suitable for carrying out image recognition on a map to obtain name text data and icon data;
a POI data generating unit adapted to determine a POI name from said name text data and a POI type from said icon data; and generating one or more pieces of POI data according to the determined POI names and POI types.
6. The apparatus of claim, wherein,
the map unit is also suitable for acquiring a map tile map and storing the acquired map tile map according to a standard coding rule; and is adapted to perform image recognition on each map tile map;
the POI data generating unit is suitable for determining the geographical position information in the POI data according to the standard coding rule.
7. The apparatus of any one of claims 5-6,
the map unit is suitable for acquiring the map tile map with the appointed display level from the Internet.
8. The apparatus of any one of claims 5-7, wherein the specified display level is a display level at which a scale provided by an electronic map provider is maximized.
9. An intelligent terminal, wherein, this intelligent terminal includes: a processor; and a memory arranged to store computer-executable instructions that, when executed, cause the processor to perform the method of any one of claims 1-4.
10. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of claims 1-4.
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