Point of interest state determination method, model training method and device

Abstract

The present disclosure provides a method for determining the status of a point of interest, a model training method, and an apparatus, relating to the fields of computer technology, particularly electronic maps, intelligent transportation, and artificial intelligence. The implementation scheme comprises: obtaining a satellite image of a target area, wherein the target area is the area where a target point of interest is located; determining, based on the satellite image, whether there is a mismatch between the target area and the target point of interest, wherein the mismatch indicates that the target area is unsuitable for accommodating the target point of interest; and determining the status of the target point of interest based on the determination result.

Description

Technical Field

[0001] The present disclosure relates to the fields of computer technology, in particular to the fields of electronic maps, intelligent transportation, and artificial intelligence, and specifically to a method and device for determining the status of a point of interest, a method and device for training a point of interest status determination model, an electronic device, a computer-readable storage medium, and a computer program product. Background Art

[0002] Point of Interest (POI) is any geographical object that can be abstracted as a point in a geographic information system, especially some geographical entities closely related to people's lives, such as schools, banks, restaurants, gas stations, hospitals, supermarkets, etc.

[0003] The points of interest can be displayed on an electronic map according to their locations. Accordingly, users can view the points of interest on the electronic map.

[0004] The approaches described in this section are not necessarily approaches that have been previously conceived or employed. Unless otherwise indicated, it should not be assumed that any approach described in this section is prior art simply by virtue of its inclusion in this section. Similarly, unless otherwise indicated, the issues raised in this section should not be considered as having been recognized in any prior art. Summary of the Invention

[0005] The present disclosure provides a method and apparatus for determining the state of a point of interest, a method and apparatus for training a point of interest state determination model, an electronic device, a computer-readable storage medium, and a computer program product.

[0006] According to one aspect of the present disclosure, a method for determining the status of a point of interest is provided, comprising: acquiring a satellite image of a target area, the target area being an area where a target point of interest is located; determining, based on the satellite image, whether there is a mismatch between the target area and the target point of interest, the mismatch indicating that the target area is unsuitable for accommodating the target point of interest; and determining the status of the target point of interest based on a result of the determination.

[0007] According to one aspect of the present disclosure, a method for training a region state determination model is provided, comprising: obtaining a first satellite image of a first region and a first mismatch label of the first region, wherein the first region is an region where a first point of interest is located, and the first mismatch label is used to indicate whether the first region is mismatched with the first point of interest; obtaining a second satellite image of a second region, wherein the second region is an region where a second point of interest is located; determining a similarity between the first satellite image and the second satellite image; in response to the similarity being greater than a similarity threshold, determining the second mismatch label of the second region as the first mismatch label; and training the region state determination model using the satellite images and mismatch labels of the regions as training samples, wherein the regions include the first region and the second region, the satellite images include the first satellite image and the second satellite image, and the mismatch labels include the first mismatch label and the second mismatch label.

[0008] According to one aspect of the present disclosure, a device for determining the status of a point of interest is provided, comprising: an acquisition module configured to acquire a satellite image of a target area, the target area being the area where a target point of interest is located; a first determination module configured to determine, based on the satellite image, whether the target area and the target point of interest are mismatched, the mismatch indicating that the target area is unsuitable for accommodating the target point of interest; and a second determination module configured to determine the status of the target point of interest based on a result of the determination.

[0009] According to one aspect of the present disclosure, a training device for a region state determination model is provided, comprising: a first acquisition module configured to acquire a first satellite image of a first region, where a first point of interest is located, and a first mismatch label of the first region, the first region being the region where a first point of interest is located, the first mismatch label being used to indicate whether the first region is mismatched with the first point of interest; a second acquisition module configured to acquire a second satellite image of a second region, where a second point of interest is located; a first determination module configured to determine a similarity between the first satellite image and the second satellite image; a second determination module configured to determine, in response to the similarity being greater than a similarity threshold, the second mismatch label of the second region as the first mismatch label; and a training module configured to train the region state determination model using the satellite images and mismatch labels of the region as training samples, wherein the region includes the first region and the second region, the satellite images include the first satellite image and the second satellite image, and the mismatch labels include the first mismatch label and the second mismatch label.

[0010] According to one aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor, the memory storing instructions executable by the at least one processor, the instructions being executed by the at least one processor so that the at least one processor can execute any of the methods of the above aspects.

[0011] According to one aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, where the computer instructions are used to cause a computer to execute any of the above methods.

[0012] According to one aspect of the present disclosure, a computer program product is provided, comprising a computer program, which implements the method of any one of the above aspects when executed by a processor.

[0013] According to one or more embodiments of the present disclosure, the efficiency and timeliness of determining the status of a point of interest can be improved.

[0014] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings illustrate exemplary embodiments and constitute a part of the specification. Together with the description of the specification, they serve to explain exemplary implementation of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, the same reference numerals designate similar, but not necessarily identical, elements.

[0016] Figure 1 A schematic diagram illustrating an exemplary system in which the various methods described herein may be implemented according to an embodiment of the present disclosure;

[0017] Figure 2 A flowchart of a method for determining a point of interest state according to an embodiment of the present disclosure is shown;

[0018] Figure 3 A comparison diagram of a point of interest before and after failure according to an embodiment of the present disclosure is shown;

[0019] Figure 4 A flowchart of a method for training a region state determination model according to an embodiment of the present disclosure is shown;

[0020] Figure 5 FIG2 shows a structural block diagram of a device for determining a state of a point of interest according to an embodiment of the present disclosure;

[0021] Figure 6A structural block diagram of a training device for a region state determination model according to an embodiment of the present disclosure is shown; and

[0022] Figure 7 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0023] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be appreciated by those skilled in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0024] In this disclosure, unless otherwise specified, the use of terms such as "first" and "second" to describe various elements is not intended to limit the positional relationship, temporal relationship, or importance relationship of these elements. Such terms are only used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of the element, while in some cases, based on the context of the description, they may also refer to different instances.

[0025] The terms used in the descriptions of the various examples described in this disclosure are for the purpose of describing specific examples only and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element may be one or more. In addition, the term "and / or" used in this disclosure encompasses any one and all possible combinations of the listed items.

[0026] In this disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0027] The accuracy of points of interest (POIs) in electronic maps directly impacts the quality of the maps and the user experience. Therefore, it's crucial to promptly identify obsolete POIs in the real world (e.g., those that have relocated or ceased operations) and remove them from the electronic map (i.e., prevent them from being displayed to the user). If obsolete POIs are not removed from the electronic map promptly, users may be led to the wrong, obsolete POIs, wasting their time and negatively impacting the user experience.

[0028] In the related art, the status of points of interest in the real world is usually determined by user reporting or on-site collection by collection vehicles. This processing method is labor-intensive, time-consuming, and inefficient, and cannot timely detect invalid points of interest in the real world.

[0029] In response to the above problems, embodiments of the present disclosure provide a method for determining the status of a point of interest, which can improve the efficiency and timeliness of determining the status of a point of interest.

[0030] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0031] Figure 1 FIG2 is a schematic diagram of an exemplary system 100 in which the various methods and apparatuses described herein may be implemented according to an embodiment of the present disclosure. Figure 1 , the system 100 includes one or more client devices 101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120. The client devices 101, 102, 103, 104, 105, and 106 can be configured to execute one or more applications.

[0032] In an embodiment of the present disclosure, the server 120 may run one or more services or software applications that enable execution of the point of interest state determination method.

[0033] In some embodiments, server 120 may also provide other services or software applications, which may include non-virtualized environments and virtualized environments. In some embodiments, these services may be provided as web-based services or cloud services, such as provided to users of client devices 101, 102, 103, 104, 105, and / or 106 under a software as a service (SaaS) model.

[0034] exist Figure 1 In the configuration shown, the server 120 may include one or more components that implement the functions performed by the server 120. These components may include software components, hardware components, or a combination thereof that can be executed by one or more processors. Users operating client devices 101, 102, 103, 104, 105, and / or 106 may, in turn, utilize one or more client applications to interact with the server 120 to utilize the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from the system 100. Therefore, Figure 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

[0035] The user can use client devices 101, 102, 103, 104, 105 and / or 106 to navigate. The client device can provide an interface that enables the user of the client device to interact with the client device. The client device can also output information to the user via the interface. Figure 1 Only six client devices are depicted, but one skilled in the art will appreciate that the present disclosure can support any number of client devices.

[0036] Client devices 101, 102, 103, 104, 105, and / or 106 may include various types of computer devices, such as portable handheld devices, general-purpose computers (such as personal computers and laptops), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, gaming systems, thin clients, various messaging devices, sensors or other sensing devices, etc. These computer devices may run various types and versions of software applications and operating systems, such as Microsoft Windows, Apple iOS, UNIX-like operating systems, Linux, or Linux-like operating systems; or include various mobile operating systems, such as Microsoft Windows Mobile OS, iOS, Windows Phone, and Android. Portable handheld devices may include cellular phones, smartphones, tablet computers, personal digital assistants (PDAs), etc. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. Gaming systems may include various handheld gaming devices, internet-enabled gaming devices, etc. Client devices are capable of executing a variety of different applications, such as various internet-related applications, communication applications (such as email applications), and short message service (SMS) applications, and may use various communication protocols.

[0037] The network 110 may be any type of network known to those skilled in the art that can support data communications using any of a variety of available protocols, including but not limited to TCP / IP, SNA, IPX, etc. By way of example only, the one or more networks 110 may be a local area network (LAN), an Ethernet-based network, a token ring, a wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a blockchain network, a public switched telephone network (PSTN), an infrared network, a wireless network (e.g., Bluetooth, Wi-Fi), and / or any combination of these and / or other networks.

[0038] Server 120 may include one or more general-purpose computers, specialized server computers (e.g., PC (personal computer) servers, UNIX servers, mid-range servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running virtual operating systems, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain a server's virtual storage device). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0039] The computing units in the server 120 may run one or more operating systems including any of the operating systems described above as well as any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and / or middle-tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, and the like.

[0040] In some implementations, server 120 may include one or more applications to analyze and consolidate data feeds and / or event updates received from users of client devices 101, 102, 103, 104, 105, and 106. Server 120 may also include one or more applications to display the data feeds and / or real-time events via one or more display devices of client devices 101, 102, 103, 104, 105, and 106.

[0041] In some embodiments, server 120 may be a distributed system server or a server integrated with blockchain. Server 120 may also be a cloud server, or an intelligent cloud computing server or intelligent cloud host equipped with artificial intelligence technology. A cloud server is a host product within the cloud computing service system that addresses the management difficulties and poor scalability of traditional physical hosts and virtual private servers (VPS) services.

[0042] The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 130 may be used to store information such as music files. The databases 130 may reside in a variety of locations. For example, the database used by the server 120 may be local to the server 120, or may be remote from the server 120 and communicate with the server 120 via a network-based or dedicated connection. The databases 130 may be of different types. In some embodiments, the databases used by the server 120 may be, for example, relational databases. One or more of these databases may store, update, and retrieve data to and from the databases in response to commands.

[0043] In some embodiments, one or more of the databases 130 may also be used by applications to store application data. The databases used by the applications may be different types of databases, such as a key-value store, an object store, or a conventional store backed by a file system.

[0044] Figure 1 The system 100 may be configured and operated in various ways to enable application of the various methods and apparatuses described in accordance with the present disclosure.

[0045] According to some embodiments, the client devices 101-106 may include an electronic map application, which may provide various services based on the electronic map. Accordingly, the server 120 may be a server corresponding to the electronic map application. The server 120 may, for example, determine the status of the point of interest by executing the method for determining the status of the point of interest of the embodiment of the present disclosure. Based on the determined status of the point of interest, the electronic map may be updated. Specifically, the points of interest determined to be valid in the electronic map are retained, and the points of interest determined to be invalid in the electronic map are hidden. This may improve the accuracy of the points of interest in the electronic map. Users may access the electronic map application in the client devices 101-106 to implement map functions related to points of interest, such as point of interest search and point of interest navigation.

[0046] Figure 2 FIG. 2 is a flow chart of a method 200 for determining a point of interest state according to an embodiment of the present disclosure. The method 200 is generally performed on a server (e.g. Figure 1 In some embodiments, the method 200 may also be performed on a client device (e.g., Figure 1 The execution of each step of the method 200 may be performed at the client devices 101, 102, 103, 104, 105 and 106 shown in FIG. Figure 1 The server 120 shown in FIG. 1 may also be Figure 1Client devices 101, 102, 103, 104, 105 and 106 are shown in FIG.

[0047] like Figure 2 As shown, the method 200 includes steps S210 - S230 .

[0048] In step S210, a satellite image of a target area is acquired, where the target area is the area where the target point of interest is located.

[0049] In step S220 , based on the satellite image, it is determined whether the target area and the target point of interest are mismatched, where the mismatch indicates that the target area is not suitable for accommodating the target point of interest.

[0050] In step S230 , based on the result determined in step S220 , the state of the target point of interest is determined.

[0051] According to the embodiments of the present disclosure, the status of the target area (whether it is mismatched with the target point of interest) is determined through satellite images of the target area, and the status of the target point of interest is determined based on the status of the target area, thereby achieving automatic and timely recognition of the status of the point of interest and improving the efficiency and timeliness of determining the status of the point of interest.

[0052] The following describes each step of method 200 in detail.

[0053] In step S210, a satellite image of a target area is acquired, where the target area is the area where the target point of interest is located.

[0054] According to some embodiments, the target point of interest can be any point of interest in an electronic map. Specifically, each point of interest in the electronic map can be used as a target point of interest, and its status can be determined based on the method 200 of the embodiment of the present disclosure. This allows the points of interest in the electronic map to be traversed and updated, thereby improving the accuracy of the electronic map.

[0055] In embodiments of the present disclosure, the target area is the area where the target point of interest is located. That is, the location of the target area is determined based on the location coordinates of the target point of interest. According to some embodiments, the target area may be an area centered on the target point of interest. This allows for comprehensive and uniform sampling of the area surrounding the target point of interest, thereby improving the accuracy of determining the state of the point of interest.

[0056] For example, the target area may be a circular area with a fixed radius (eg, 100 meters) with the target point of interest as the center. For another example, the target area may also be a rectangular area with the target point of interest as the center.

[0057] Satellite imagery, also known as remote sensing imagery, is obtained by detecting the reflection and emission of electromagnetic waves from objects on Earth's surface through satellites in space, and then converting and identifying this information. Because satellite imagery provides comprehensive observation of the Earth's surface, the latest satellite imagery of any point of interest (POI) on an electronic map can be obtained. Determining the status of POIs based on satellite imagery enables comprehensive coverage of POIs on electronic maps, effectively discovering POIs that are currently hidden from other mining techniques and have long been ineffective, thereby improving the accuracy of POIs on electronic maps.

[0058] In the embodiment of the present disclosure, the target point of interest being invalid means that the target point of interest is not suitable for being visited by the user, for example, the target point of interest has been moved out of the original location, has ceased operations, etc.

[0059] According to some embodiments, a satellite image of a target area may be acquired through a satellite observation system and compressed to a preset size for storage and calculation. The preset size may be set as needed.

[0060] It should be noted that the satellite image of the target area may include other areas except the target area. For example, when the target area is a circular area, the satellite image of the target area may be a rectangular image including the circular area.

[0061] After obtaining the satellite image of the target area, it can be determined whether the target area and the target point of interest are mismatched based on the satellite image.

[0062] The mismatch between the target area and the target point of interest indicates that the target area is not suitable for accommodating the target point of interest. For example, if the target area is currently under construction or demolition, the target area is not suitable for accommodating the target point of interest, that is, the target area and the target point of interest are mismatched. For another example, if the target area is a body of water or a green space, and the target point of interest is a shop, the target area is not suitable for accommodating the target point of interest, that is, the target area and the target point of interest are mismatched. For another example, if the target area is a commercial street and the target point of interest is a shop, the target area is suitable for accommodating the target point of interest, that is, the target area and the target point of interest do not mismatch, or in other words, the target area and the target point of interest are adapted.

[0063] According to some embodiments, in step S220, a trained artificial intelligence model may be used to determine whether the target area and the target point of interest are mismatched. Specifically, step S220 may include the following steps S222-S226.

[0064] In step S222 , image features of the satellite image are extracted.

[0065] In step S224 , the image features are input into the trained region state determination model to obtain the mismatch probability output by the region state determination model.

[0066] In step S226 , in response to the mismatch probability being greater than the probability threshold, it is determined that the target area and the target point of interest are mismatched.

[0067] According to some embodiments, for step S222, any image embedding algorithm may be used to extract image features of the satellite image. The image features may be, for example, one-dimensional vectors of a preset dimension (eg, 256 dimensions).

[0068] The region state determination model may be, for example, a neural network model, a decision tree model, or the like.

[0069] According to some embodiments, in order to improve the prediction effect of the model so that the model can still obtain accurate prediction results when the overall tone of the satellite image is dark, the regional state determination model can use a gradient boosting decision tree (GBDT), which includes multiple regression trees.

[0070] Specifically, the image features can be fed into multiple trained regression trees to obtain prediction values ​​output by each regression tree. The prediction values ​​output by each regression tree are summed to obtain a mismatch probability between the target region and the target point of interest. If the mismatch probability is greater than a probability threshold, it can be determined that the target region and the target point of interest do not match.

[0071] The regional state model can be obtained by training, for example, through the regional state model training method 400 of the embodiment below.

[0072] The probability threshold can be set as needed, for example, it can be set to 0.5.

[0073] It is understood that if the target area and the target point of interest do not match, the current target area is no longer suitable for accommodating the target point of interest, and accordingly, the target point of interest is likely to have failed. Therefore, by determining whether the target area and the target point of interest do not match, the state of the target point of interest can be determined and the failed target point of interest can be identified.

[0074] According to some embodiments, in response to determining that the target area and the target point of interest do not match, the target point of interest is determined to be invalid. After the target point of interest is determined to be invalid, the target point of interest can be removed from the electronic map and no longer presented to the user. There are various ways to remove a target point of interest. For example, the data for the target point of interest can be deleted from the electronic map; or the data for the target point of interest can be retained in the electronic map, but the display status of the target point of interest can be set to "invisible"; etc.

[0075] Considering that the determination result obtained in step S220 may contain errors, according to some embodiments, in response to determining that the target area and the target point of interest do not match, the target point of interest is verified to be invalid based on preset rules. According to this embodiment, after determining that the target area and the target point of interest do not match in step S220, the target point of interest is considered to be suspected of being invalid, and further verification is performed based on preset rules to determine whether the target point of interest is actually invalid, thereby improving the accuracy of the determination of the point of interest status.

[0076] Preset rules can be configured as needed. For example, a preset rule may include determining whether the target POI has been reported as valid by a user or a collection vehicle in the recent past (e.g., within three days or a week). If so, the target POI is considered to be still valid. Another example of a preset rule may include determining whether the target POI is connected to a WiFi network. If so, the target POI is considered to be still valid.

[0077] In other embodiments, in response to determining that the target area and the target POI do not match, a verification task for the target POI can be generated and sent to a relevant staff member. The staff member executes the verification task, verifies the status of the target POI, and returns a verification result indicating whether the target POI is valid or invalid. This can also improve the accuracy of determining the POI status.

[0078] According to some embodiments, in response to determining that the target area does not mismatch (ie, fits) with the target point of interest, the target point of interest is determined to be valid.

[0079] Figure 3 A comparison diagram of a satellite image 310 before and after the failure of a target point of interest 312 according to an embodiment of the present disclosure is shown.

[0080] Based on satellite image 310 at a certain historical moment, it can be determined that the target area (ie, the surface area shown in satellite image 310 ) matches target point of interest 312 , and therefore target point of interest 312 is valid. Target point of interest 312 is located on one side of road 314 .

[0081] Based on the current satellite image 320 , it can be determined that the target area (ie, the surface area shown in the satellite image 320 ) does not match the target point of interest 312 . The target point of interest 312 has fallen into the demolition construction area 324 , and therefore the target point of interest 312 is invalid.

[0082] According to an embodiment of the present disclosure, a method for training a region status determination model is also provided. The region status determination model is used to determine whether a region and a point of interest are mismatched.

[0083] Figure 4FIG. 4 is a flow chart showing a method 400 for training a regional state determination model according to an embodiment of the present disclosure. The method 400 is typically performed on a server (e.g. Figure 1 In some embodiments, the method 400 may also be performed on a client device (e.g., Figure 1 The execution of each step of the method 400 may be performed at the client devices 101, 102, 103, 104, 105 and 106 shown in FIG. Figure 1 The server 120 shown in FIG. 1 may also be Figure 1 Client devices 101, 102, 103, 104, 105 and 106 are shown in FIG.

[0084] like Figure 4 As shown, the method 400 includes steps S410 - S450 .

[0085] In step S410, a first satellite image of a first area and a first mismatch tag of the first area are obtained, where the first area is where the first point of interest is located, and the first mismatch tag is used to indicate whether the first area is mismatched with the first point of interest.

[0086] In step S420, a second satellite image of a second area is acquired, where the second area is where the second point of interest is located.

[0087] In step S430 , the similarity between the first satellite image and the second satellite image is determined.

[0088] In step S440 , in response to the similarity being greater than a similarity threshold, the second mismatch label of the second region is determined as the first mismatch label.

[0089] In step S450 , the regional state determination model is trained using the satellite images and mismatch labels of the region as training samples.

[0090] According to the embodiments of the present disclosure, only a small number of satellite images (i.e., first satellite images, for example, 100) are manually annotated, and other satellite images (i.e., second satellite images, for example, 10,000) are automatically annotated based on the annotated satellite images. This can automatically generate a large number of samples, thereby improving the training efficiency and prediction effect of the model.

[0091] According to some embodiments, in step S410 , a first mismatch flag of 1 indicates that the first region and the first interest point are mismatched, and a first mismatch flag of 0 indicates that the first region and the first interest point are not mismatched (ie, are matched).

[0092] According to some embodiments, in step S430, any image embedding algorithm may be used to extract image features of the first satellite image and the second satellite image respectively, and the cosine similarity of the two image features is used as the similarity between the first satellite image and the second satellite image.

[0093] The similarity threshold can be set as needed. Generally, the larger the similarity threshold is set, the higher the accuracy of the second mismatch label is. The similarity threshold can be set to 0.9, for example.

[0094] The region state determination model may be, for example, a neural network model, a decision tree model, or the like.

[0095] According to some embodiments, in order to improve the prediction effect of the model so that the model can still obtain accurate prediction results when the overall tone of the satellite image is dark, the regional state determination model can use a gradient boosting decision tree (GBDT), which includes multiple regression trees.

[0096] Corresponding to the GBDT model, step S450 may include: extracting image features of the satellite image; and sequentially generating each of a plurality of regression trees based on the image features and the mismatch labels, wherein, in two adjacent regression trees, the latter regression tree is determined based on the loss of the previous regression tree, and the loss is determined based on the predicted value and the mismatch labels obtained by inputting the image features into the previous regression tree.

[0097] Based on the trained GBDT model, a satellite image of the area where the target point of interest to be identified is located (i.e., the target area) can be obtained, image features of the satellite image can be extracted, and the image features can be input into multiple trained regression trees respectively to obtain the predicted values ​​output by each regression tree. The predicted values ​​output by each regression tree are added together to obtain the probability of mismatch between the target area and the target point of interest (i.e., mismatch probability). In response to the mismatch probability being greater than a probability threshold (e.g., 0.5), it can be determined that the target area and the target point of interest are mismatched.

[0098] According to an embodiment of the present disclosure, a device for determining the state of a point of interest is also provided. Figure 5 FIG. 5 shows a structural block diagram of an apparatus 500 for determining a point of interest state according to an embodiment of the present disclosure. Figure 5 As shown, the apparatus 500 includes:

[0099] An acquisition module 510 is configured to acquire a satellite image of a target area, where the target area is an area where a target point of interest is located;

[0100] A first determining module 520 is configured to determine, based on the satellite image, whether the target area and the target point of interest are mismatched, wherein the mismatch indicates that the target area is not suitable for accommodating the target point of interest; and

[0101] The second determining module 530 is configured to determine the state of the target point of interest based on the determination result.

[0102] According to the embodiments of the present disclosure, the status of the target area (whether it is mismatched with the target point of interest) is determined through satellite images of the target area, and the status of the target point of interest is determined based on the status of the target area, thereby achieving automatic and timely recognition of the status of the point of interest and improving the efficiency and timeliness of determining the status of the point of interest.

[0103] According to some embodiments, the second determining module 530 is further configured to: in response to determining that the target area does not match the target point of interest, determine that the target point of interest is invalid.

[0104] According to some embodiments, the second determining module 530 is further configured to: in response to determining that the target area does not match the target point of interest, verify whether the target point of interest is invalid based on a preset rule.

[0105] According to some embodiments, the first determination module 520 includes: an extraction unit configured to extract image features of the satellite image; a prediction unit configured to input the image features into a trained region state determination model to obtain a mismatch probability output by the region state determination model, wherein the region state determination model includes multiple regression trees; and a determination unit configured to determine that the target region and the target point of interest are mismatched in response to the mismatch probability being greater than a probability threshold.

[0106] According to some embodiments, the target point of interest is any point of interest in an electronic map, and the target area is an area centered on the target point of interest.

[0107] According to an embodiment of the present disclosure, a training device for a region status determination model is also provided. Figure 6 FIG. 6 shows a structural block diagram of a training device 600 for determining a regional state model according to an embodiment of the present disclosure. Figure 6 As shown, the apparatus 600 includes:

[0108] A first acquisition module 610 is configured to acquire a first satellite image of a first area and a first mismatch tag of the first area, where the first area is where a first point of interest is located, and the first mismatch tag is used to indicate whether the first area is mismatched with the first point of interest;

[0109] A second acquisition module 620 is configured to acquire a second satellite image of a second area, where the second area is where the second point of interest is located;

[0110] A first determination module 630 is configured to determine a similarity between the first satellite image and the second satellite image;

[0111] A second determining module 640 is configured to determine, in response to the similarity being greater than a similarity threshold, the second mismatch label of the second area as the first mismatch label; and

[0112] The training module 650 is configured to train the regional state determination model using satellite images and mismatch labels of a region as training samples, where the region includes the first region and the second region, the satellite images include the first satellite image and the second satellite image, and the mismatch labels include the first mismatch label and the second mismatch label.

[0113] According to the embodiments of the present disclosure, only a small number of satellite images (first satellite images) are manually annotated, and other satellite images (second satellite images) are automatically annotated based on the annotated satellite images, which can automatically generate a large number of samples and improve the training efficiency and prediction effect of the model.

[0114] According to some embodiments, the regional status determination model includes multiple regression trees, and the training module 650 includes: an extraction unit configured to extract image features of the satellite image; a generation unit configured to sequentially generate each of the multiple regression trees based on the image features and the mismatch labels, wherein, in two adjacent regression trees, the latter regression tree is determined based on the loss of the previous regression tree, and the loss is determined based on the predicted value obtained by inputting the image features into the previous regression tree and the mismatch labels.

[0115] It should be understood that Figure 5 The modules or units of the apparatus 500 shown in FIG. 5 can be used in conjunction with the reference Figure 2 The steps in the method 200 described above correspond to the following. Figure 6 The modules or units of the apparatus 600 shown in FIG. 6 can be used in conjunction with the reference Figure 4 The steps in the method 400 described above correspond to each other. Thus, the operations, features, and advantages described above for the method 200 are also applicable to the apparatus 500 and the modules and units included therein, and the operations, features, and advantages described above for the method 400 are also applicable to the apparatus 600 and the modules and units included therein. For the sake of brevity, some operations, features, and advantages are not described in detail here.

[0116] Although specific functions are discussed above with reference to specific modules, it should be noted that the functions of the various modules discussed herein may be separated into multiple modules, and / or at least some functions of multiple modules may be combined into a single module. For example, the first determination module 520 and the second determination module 530 described above may be combined into a single module in some embodiments.

[0117] It should also be understood that various techniques may be described herein in the general context of software hardware elements or program modules. Figure 5 、 Figure 6 The various modules described can be implemented in hardware or in hardware in combination with software and / or firmware. For example, these modules can be implemented as computer program code / instructions, which are configured to be executed in one or more processors and stored in a computer-readable storage medium. Alternatively, these modules can be implemented as hardware logic / circuits. For example, in some embodiments, one or more of modules 510-650 can be implemented together in a system on chip (SoC). SoC can include an integrated circuit chip (which includes a processor (e.g., a central processing unit (CPU), a microcontroller, a microprocessor, a digital signal processor (DSP), etc.), a memory, one or more communication interfaces, and / or one or more components in other circuits), and can optionally execute the received program code and / or include embedded firmware to perform functions.

[0118] According to an embodiment of the present disclosure, an electronic device is also provided, including: at least one processor; and a memory communicatively connected to the at least one processor, the memory storing instructions executable by the at least one processor, the instructions being executed by the at least one processor so that the at least one processor can execute the point of interest status determination method and / or area status determination model training method.

[0119] According to an embodiment of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is also provided, wherein the computer instructions are used to enable a computer to execute the above-mentioned method for determining the state of an interest point and / or the training method for the area state determination model.

[0120] According to an embodiment of the present disclosure, a computer program product is further provided, including a computer program, which, when executed by a processor, implements the above-mentioned method for determining the state of a point of interest and / or the method for training a region state determination model.

[0121] refer to Figure 7, a block diagram of an electronic device 700 that can serve as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0122] like Figure 7 As shown, electronic device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. Various programs and data required for the operation of device 700 can also be stored in RAM 703. Computing unit 701, ROM 702, and RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to bus 704.

[0123] Multiple components in the electronic device 700 are connected to the I / O interface 705, including: an input unit 706, an output unit 707, a storage unit 708, and a communication unit 709. The input unit 706 can be any type of device that can input information to the device 700. The input unit 706 can receive input digital or character information, and generate key signal input related to user settings and / or function control of the electronic device, and can include but is not limited to a mouse, a keyboard, a touch screen, a trackpad, a trackball, a joystick, a microphone and / or a remote control. The output unit 707 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator and / or a printer. The storage unit 708 can include but is not limited to a magnetic disk, an optical disk. The communication unit 709 allows the device 700 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver and / or a chipset, such as Bluetooth TM devices, 802.11 devices, Wi-Fi devices, WiMAX devices, cellular communication devices, and / or the like.

[0124] The computing unit 701 may be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above, such as method 200 and / or method 400. For example, in some embodiments, method 200 may be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by the computing unit 701, one or more steps of methods 200 and 400 described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to execute the method 200 and / or the method 400 in any other appropriate manner (eg, by means of firmware).

[0125] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0126] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0127] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0128] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0129] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0130] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0131] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not limited herein.

[0132] Although the embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above-mentioned methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but is only limited by the claims after authorization and their equivalents. Various elements in the embodiments or examples may be omitted or replaced by their equivalents. In addition, the steps may be performed in an order different from that described in this disclosure. Further, the various elements in the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced by equivalent elements that appear after this disclosure.

Claims

1. A method for determining the state of a point of interest, comprising: Acquire a satellite image of a target area, wherein the target area is an area where a target point of interest is located; Determining, based on the satellite image, whether the target area and the target point of interest are mismatched, wherein the mismatch indicates that the target area is not suitable for accommodating the target point of interest, and determining, based on the satellite image, whether the target area and the target point of interest are mismatched includes: extracting image features of the satellite image; Inputting the image features into a trained region state determination model to obtain a mismatch probability output by the region state determination model; and In response to the mismatch probability being greater than a probability threshold, determining that the target area and the target point of interest are mismatched; and Based on a result of the determination, a state of the target point of interest is determined.

2. The method according to claim 1, wherein The determining the state of the target point of interest based on the determination result includes: In response to determining that the target area does not match the target point of interest, the target point of interest is determined to be invalid.

3. The method according to claim 1, wherein The determining the state of the target point of interest based on the determination result includes: In response to determining that the target area does not match the target point of interest, verifying whether the target point of interest is invalid based on a preset rule.

4. The method according to any one of claims 1 to 3, wherein The target point of interest is any point of interest in an electronic map, and the target area is an area centered on the target point of interest.

5. A method for training a regional state determination model, comprising: Acquire a first satellite image of a first area and a first mismatch tag of the first area, where the first area is an area where a first point of interest is located, and the first mismatch tag is used to indicate whether the first area is mismatched with the first point of interest; Acquire a second satellite image of a second area, where the second area is where the second point of interest is located; determining a similarity between the first satellite image and the second satellite image; In response to the similarity being greater than a similarity threshold, determining the second mismatch label of the second area as the first mismatch label; as well as The regional state determination model is trained using satellite images and mismatch labels of a region as training samples, wherein the region includes the first region and the second region, the satellite images include the first satellite image and the second satellite image, and the mismatch labels include the first mismatch label and the second mismatch label.

6. The method according to claim 5, wherein: The regional state determination model includes a plurality of regression trees, and wherein the satellite images and mismatch labels of the region are used as training samples, and training the regional state determination model includes: extracting image features of the satellite image; Based on the image features and the mismatch labels, each regression tree in the multiple regression trees is generated in sequence, wherein, in two adjacent regression trees, the latter regression tree is determined based on the loss of the previous regression tree, and the loss is determined based on the predicted value obtained by inputting the image features into the previous regression tree and the mismatch labels.

7. A device for determining a state of a point of interest, comprising: an acquisition module configured to acquire a satellite image of a target area, wherein the target area is an area where a target point of interest is located; A first determining module is configured to determine, based on the satellite image, whether the target area and the target point of interest are mismatched, wherein the mismatch indicates that the target area is not suitable for accommodating the target point of interest, the first determining module comprising: an extraction unit configured to extract image features of the satellite image; a prediction unit configured to input the image features into a trained region state determination model to obtain a mismatch probability output by the region state determination model, wherein the region state determination model includes a plurality of regression trees; and a determining unit configured to, in response to the mismatch probability being greater than a probability threshold, determine that the target area and the target point of interest are mismatched; and The second determining module is configured to determine the state of the target point of interest based on the determination result.

8. The device according to claim 7, wherein The second determining module is further configured to: In response to determining that the target area does not match the target point of interest, the target point of interest is determined to be invalid.

9. The device according to claim 7, wherein The second determining module is further configured to: In response to determining that the target area does not match the target point of interest, verifying whether the target point of interest is invalid based on a preset rule.

10. The device according to any one of claims 7 to 9, wherein: The target point of interest is any point of interest in an electronic map, and the target area is an area centered on the target point of interest.

11. A training device for a region state determination model, comprising: a first acquisition module configured to acquire a first satellite image of a first area and a first mismatch tag of the first area, wherein the first area is an area where a first point of interest is located, and the first mismatch tag is used to indicate whether the first area is mismatched with the first point of interest; A second acquisition module is configured to acquire a second satellite image of a second area, where the second area is an area where the second point of interest is located; a first determining module, configured to determine a similarity between the first satellite image and the second satellite image; a second determining module, configured to determine, in response to the similarity being greater than a similarity threshold, a second mismatch label of the second area as the first mismatch label; as well as The training module is configured to train the regional state determination model using satellite images and mismatch labels of a region as training samples, wherein the region includes the first region and the second region, the satellite images include the first satellite image and the second satellite image, and the mismatch labels include the first mismatch label and the second mismatch label.

12. The device according to claim 11, wherein The region state determination model includes a plurality of regression trees, and wherein the training module includes: an extraction unit configured to extract image features of the satellite image; A generation unit is configured to sequentially generate each of the multiple regression trees based on the image features and the mismatch labels, wherein, in two adjacent regression trees, the latter regression tree is determined based on the loss of the previous regression tree, and the loss is determined based on the predicted value obtained by inputting the image features into the previous regression tree and the mismatch labels.

13. An electronic device comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; in The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

14. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 6.

15. A computer program product comprising a computer program, wherein When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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