CN111121797B - Road screening method, device, server and storage medium - Google Patents
Road screening method, device, server and storage medium Download PDFInfo
- Publication number
- CN111121797B CN111121797B CN201811296480.5A CN201811296480A CN111121797B CN 111121797 B CN111121797 B CN 111121797B CN 201811296480 A CN201811296480 A CN 201811296480A CN 111121797 B CN111121797 B CN 111121797B
- Authority
- CN
- China
- Prior art keywords
- road
- track
- satellite image
- candidate
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
Abstract
The embodiment of the invention discloses a road screening method, a road screening device, a server and a storage medium. The method comprises the following steps: determining candidate roads included in the target satellite image; screening the driving track from the track data of each device; and matching each candidate road included in the target satellite image with the driving track, and determining the road included in each candidate road according to the matching result. The satellite image and the equipment track data are combined to identify the road, so that the road identification efficiency is improved, the actual road can be effectively screened, the accuracy of road screening and the timeliness of new road extraction are improved, and the road identification cost is reduced.
Description
Technical Field
The embodiment of the invention relates to the field of road identification, in particular to a road screening method, a road screening device, a server and a storage medium.
Background
Nowadays, more and more functions are integrated in terminals, and these functions can be implemented by various applications. A user can make life more convenient by applying various functions. The road recognition function and the location function are widely used by users due to their real-time and convenience. In the road identification service, a server stores a large amount of road information including road names, road length and width information, position information, satellite images, real-time road conditions and the like, and can provide road information for users through a user side so as to facilitate the users to go out.
The method commonly used in road identification and extraction at present is a semi-automatic road extraction algorithm, the algorithm firstly selects a certain road seed point manually, then uses a certain search strategy to detect the road edge by combining the influence characteristic information of the road and the like, and finally uses a certain road edge tracking algorithm to connect road sections to extract the road. In addition, after the new road is laid, the road information cannot be updated in time due to time delay of road information acquisition by surveying and mapping personnel or interference of regional conditions. Another common road extraction algorithm is automatic extraction based on machine learning, but green belts, buildings, mountains and hills and the like interfere with road extraction, the current situations of rivers and the like are not similar to the road shape in remote sensing satellite images, and the automatic extraction algorithm based on machine learning is difficult to distinguish to eliminate interference, so the accuracy of road extraction is low.
Disclosure of Invention
The embodiment of the invention provides a road screening method, a road screening device, a server and a storage medium, which are used for improving the accuracy of road screening and the timeliness of new road extraction and reducing the road identification cost.
In a first aspect, an embodiment of the present invention provides a road screening method, where the method includes:
determining candidate roads included in the target satellite image;
screening the driving track from the track data of each device;
and matching each candidate road included in the target satellite image with the driving track, and determining the road included in each candidate road according to the matching result.
In a second aspect, an embodiment of the present invention further provides a road screening apparatus, where the apparatus includes:
the road determining module is used for determining each candidate road included in the target satellite image;
the track obtaining module is used for screening the driving track from the track data of each device;
and the matching module is used for matching each candidate road included in the target satellite image with the driving track and determining the road included in each candidate road according to the matching result.
In a third aspect, an embodiment of the present invention further provides a server, where the server includes:
one or more processors;
a memory for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement any of the road screening methods in embodiments of the present invention.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement any one of the road screening methods in the embodiments of the present invention.
According to the embodiment of the invention, the candidate roads contained in the target satellite image are determined, the driving track is obtained by screening from the track data of each device, and the roads contained in the candidate roads are determined according to the matching result of the candidate roads and the driving track, so that the accuracy of road screening is improved, the efficiency of road identification is improved, and the cost of road identification is reduced.
Drawings
FIG. 1 is a flow chart of a road screening method according to a first embodiment of the present invention;
FIG. 2 is a flow chart of a road screening method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a road screening apparatus according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a server in the fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
The road screening method provided by the embodiment is applicable to the situation of screening the actual road from the candidate roads, and the method can be executed by a road screening device, which can be implemented by software and/or hardware, and can be integrated in a server. Referring to fig. 1, the method of the present embodiment specifically includes the following steps:
and S110, determining each candidate road included in the target satellite image.
The target satellite image is image data obtained by shooting or scanning a ground object through a camera, a television camera, a multispectral scanner and other equipment in the running process of the artificial earth satellite, and comprises pictures or videos. The candidate roads may include road names, road length and width information, position information, satellite images, real-time road conditions, and other information.
Candidate roads are extracted through the target satellite image, and the target satellite image is preprocessed to improve the quality of the image, illustratively, the preprocessing includes histogram equalization, filtering processing and the like. And then low-level processing is carried out, the original image is converted into an image convenient for extracting road characteristics, and the processing in the aspects of binarization, gray scale, edge, grammar, vertex, direction and the like is mainly carried out. And then, carrying out middle-level processing, analyzing the processed image, and extracting road characteristics including geometric characteristics, radiation characteristics, topological characteristics, context characteristics and the like. The road features on the image after the middle-level processing are organized according to a certain rule by means of a computer and the like, and the road is understood and identified by the structure and the relation of elements, a road model and the rule and the knowledge related to the road.
Since green belts, buildings, mountains and hills and the like interfere with road extraction, and lines such as rivers are similar to the road shape in the remote sensing satellite image, erroneous judgment may be generated in the process of extracting the road through the target satellite image, so that other images similar to the road shape are determined as the road.
Optionally, a high-resolution target satellite image is obtained, and the candidate roads are extracted through the high-resolution target satellite image to serve as the candidate roads, so that the accuracy of extracting the candidate roads is improved.
And S120, screening the driving track from the track data of each device.
The device can be mobile equipment, wearable equipment, vehicle-mounted equipment and the like, wherein a satellite tracking positioner and a positioning software development kit are deployed, positioning data of the device can be acquired, the positioning data is stored in a database, and the positioning data is sent to a server.
The server receives the positioning data, obtains the track data of the corresponding equipment through each group of positioning data, exemplarily analyzes the time and place information of the positioning data, and connects positioning points in a series of continuous positioning data of time and place to obtain the track data of the corresponding equipment.
The driving track is obtained by screening the track data of each device, illustratively, the continuous movement speed of the positioning points in the track data of each device is obtained by analyzing the positioning data, the continuous movement speeds of the positioning points in the tracks of various types are different, so that the device track classification can be obtained by the continuous movement speed of the positioning points, wherein the track classification comprises a walking track, a driving track, a riding track, a train track and the like, and the driving track is obtained by screening.
S130, matching each candidate road included in the target satellite image with the driving track, and determining the road included in each candidate road according to the matching result.
Specifically, road extraction is performed only according to the satellite image, and the road extraction is interfered by lines such as green belts, buildings, mountains and hills, rivers and the like, so that the accuracy of road extraction is reduced. The candidate roads in the target satellite image are matched with the driving track, the roads in the candidate roads are determined according to the matching result, the practical application condition of the roads is fully utilized, the walking and riding routes are probably non-common roads, and the train track is a fixed train track which cannot be used as a common driving road, so the driving track is selected from the walking track, the driving track, the riding track and the train track as the determination condition to be matched with the candidate roads, namely the driving track can be embodied as a road, the interference of green belts, rivers and the like can be eliminated, and the accuracy of road extraction is improved.
Specifically, matching each candidate road included in the target satellite image with the driving track, and determining the road included in each candidate road according to the matching result includes: and if the driving track number of each candidate road included in the target satellite image is greater than a track threshold value, determining that each candidate road is the target road. For example, in order to further determine that the road is a normal traffic road, a target road is determined according to the number of driving tracks on the candidate road, each candidate road included in the target satellite image is matched with the driving track by using a hidden markov algorithm to obtain a matching result, and if the number of driving tracks on each candidate road included in the target satellite image is greater than a track threshold, each candidate road is determined to be the target road, wherein the track threshold may be set by a technician according to an actual situation.
According to the technical scheme, the candidate roads in the target satellite image are determined, the candidate roads are preliminarily judged to be the roads, the driving track is obtained by screening the track data of each device, the candidate roads are matched with the driving track to determine the roads in the candidate roads, the actual application of the roads is considered, the accuracy of road extraction and screening is improved, the actual detection of surveying and mapping personnel is not needed, the efficiency of road identification is improved, and the cost of road identification is reduced.
Example two
The present embodiment is optimized based on the above embodiments, and details not described in detail in the present embodiment are described in the above embodiments. Referring to fig. 2, the road screening method provided in this embodiment includes:
s210, taking the target satellite image as the input of the road identification model, and extracting each candidate road included in the target satellite image.
Wherein the road identification model is generated by: matching the basic road data with the sample satellite image to determine a road included in the sample satellite image; and training the convolution network model according to the road included in the sample satellite image to generate the road identification model. Specifically, the determined existing road data is collected as basic road data, a sample satellite image is collected, the basic road data is matched with the sample satellite image, a training sample and a test sample are constructed, and optionally tens of millions of training samples and test samples are constructed, so that the accuracy of the model is improved. And performing self-help learning by adopting a convolutional neural network, and constructing a learning model as a road identification model.
And taking the updated target satellite image as the input of the road identification model, and extracting each candidate road included in the latest target satellite image.
And S220, taking the track data of each device as the input of a track classification model to obtain the driving track included in the track data of each device.
Wherein the trajectory classification model is generated by: determining a traffic type of the sample trajectory data and trajectory characteristics of the sample trajectory data, wherein the trajectory characteristics include at least one of an average speed, an acceleration, a speed variance, a maximum speed, a curvature, and a direction; and training a classification model according to the traffic type of the sample track data and the track characteristics of the sample track data to generate the track classification model. Specifically, the traffic type of the sample track data is obtained, the track characteristics of the sample track data corresponding to the same type, including at least one of average speed, acceleration, speed variance, maximum speed, curvature and direction, are obtained to obtain training samples and test samples, and optionally, tens of millions of groups of training samples and test samples are constructed to improve the accuracy of the model. And training the classification model through the training samples to generate a track classification model.
And taking the currently received equipment track data as the input of a track classification model to obtain the driving track included in the currently received equipment track data.
S230, matching each candidate road included in the target satellite image with the driving track, and determining the road included in each candidate road according to the matching result.
Optionally, before matching each candidate road included in the target satellite image with the driving trajectory, the method further includes: and filtering the existing roads from the candidate roads included in the target satellite image according to the roads included in the basic road data. Specifically, the road identification model is used for obtaining each candidate road in the current target satellite image, and the existing road in each candidate road is filtered according to the road included in the basic road data, so that the newly laid road is screened out, and the extraction of the new road is realized.
According to the technical scheme, the road identification model and the track classification model are built, the target satellite image is used as the input of the road identification model, the candidate roads in the target satellite image are extracted, the equipment track data is used as the input of the track classification model, the driving track in the equipment track data is obtained, the accuracy of extracting the candidate roads and the driving track is improved, and the existing roads are filtered from the candidate roads, so that the new roads are extracted in time.
EXAMPLE III
The present embodiment provides a road screening device, refer to fig. 3, and the device specifically includes:
a road determination module 310, configured to determine candidate roads included in the target satellite image;
a track obtaining module 320, configured to filter driving tracks from the track data of each device;
the matching module 330 is configured to match each candidate road included in the target satellite image with the driving trajectory, and determine a road included in each candidate road according to a matching result.
Optionally, the road determining module 310 is specifically configured to:
taking a target satellite image as the input of a road identification model, and extracting each candidate road included in the target satellite image;
wherein the road identification model is generated by:
matching the basic road data with the sample satellite image to determine a road included in the sample satellite image;
and training the convolution network model according to the road included in the sample satellite image to generate the road identification model.
Optionally, the trajectory obtaining module 320 is specifically configured to:
taking the track data of each device as the input of a track classification model to obtain the driving track included in the track data of each device;
wherein the trajectory classification model is generated by:
determining a traffic type of the sample trajectory data and trajectory characteristics of the sample trajectory data, wherein the trajectory characteristics include at least one of an average speed, an acceleration, a speed variance, a maximum speed, a curvature, and a direction;
and training a classification model according to the traffic type of the sample track data and the track characteristics of the sample track data to generate the track classification model.
Optionally, the method further includes: and the filtering module is used for filtering the existing road from the candidate roads included in the target satellite image according to the road included in the basic road data.
Optionally, the matching module 330 is specifically configured to determine that each candidate road is the target road if the number of driving tracks on each candidate road included in the target satellite image is greater than a track threshold.
According to the technical scheme, the candidate roads in the target satellite image are determined through the road determining module, the track obtaining module screens the driving track from the track data of each device, the matching module matches the candidate roads in the target satellite image with the driving track, the roads in the candidate roads are determined according to the matching result, the actual application of the roads is fully considered, the accuracy of road extraction and screening is improved, the actual detection of surveying and mapping personnel is not needed, the road identification efficiency is improved, and the road identification cost is reduced.
Example four
Fig. 4 is a schematic structural diagram of a server according to a fourth embodiment of the present invention. FIG. 4 illustrates a block diagram of an exemplary server 412 suitable for use in implementing embodiments of the present invention. The server 412 shown in fig. 4 is only an example and should not bring any limitations to the function and scope of use of the embodiments of the present invention.
As shown in FIG. 4, the server 412 is in the form of a general purpose computing device. Components of server 412 may include, but are not limited to: one or more processors or processing units 416, a system memory 428, and a bus 418 that couples the various system components including the system memory 428 and the processing unit 416.
The system memory 428 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)430 and/or cache memory 432. The server 412 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 434 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, commonly referred to as a "hard drive"). Although not shown in FIG. 4, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 418 by one or more data media interfaces. Memory 428 can include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 440 having a set (at least one) of program modules 442 may be stored, for instance, in memory 428, such program modules 442 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. The program modules 442 generally perform the functions and/or methodologies of the described embodiments of the invention.
The server 412 may also communicate with one or more external devices 414 (e.g., keyboard, pointing device, display 424, etc.), with one or more devices that enable a user to interact with the server 412, and/or with any devices (e.g., network card, modem, etc.) that enable the server 412 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 422. Also, server 412 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet) through network adapter 420. As shown, network adapter 420 communicates with the other modules of server 412 over bus 418. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the server 412, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 416 executes various functional applications and data processing, such as implementing a road screening method provided by embodiments of the present invention, by executing at least one of the other programs stored in the system memory 428.
EXAMPLE five
Fifth, an embodiment of the present invention further provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a road screening method.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (9)
1. A method of screening a road, comprising:
determining candidate roads included in the target satellite image;
screening the driving track from the track data of each device;
filtering existing roads from candidate roads included in the target satellite image according to roads included in basic road data;
and matching each candidate road included in the target satellite image with the driving track, and determining the road included in each candidate road according to the matching result.
2. The method of claim 1, wherein determining candidate roads included in the target satellite image comprises:
taking a target satellite image as the input of a road identification model, and extracting each candidate road included in the target satellite image;
wherein the road identification model is generated by:
matching the basic road data with the sample satellite image to determine a road included in the sample satellite image;
and training the convolution network model according to the road included in the sample satellite image to generate the road identification model.
3. The method of claim 1, wherein screening the driving trajectory from the device trajectory data comprises:
taking the track data of each device as the input of a track classification model to obtain the driving track included in the track data of each device;
wherein the trajectory classification model is generated by:
determining a traffic type of the sample trajectory data and trajectory characteristics of the sample trajectory data, wherein the trajectory characteristics include at least one of an average speed, an acceleration, a speed variance, a maximum speed, a curvature, and a direction;
and training a classification model according to the traffic type of the sample track data and the track characteristics of the sample track data to generate the track classification model.
4. The method according to claim 1, wherein matching each candidate road included in the target satellite image with the driving trajectory and determining a road included in each candidate road according to a matching result includes:
and if the driving track number of each candidate road included in the target satellite image is greater than a track threshold value, determining that each candidate road is the target road.
5. A road screening device, comprising:
the road determining module is used for determining each candidate road included in the target satellite image;
the track obtaining module is used for screening the driving track from the track data of each device;
the filtering module is used for filtering the existing road from the candidate roads included in the target satellite image according to the road included in the basic road data;
and the matching module is used for matching each candidate road included in the target satellite image with the driving track and determining the road included in each candidate road according to the matching result.
6. The apparatus of claim 5, wherein the road determination module is specifically configured to:
taking a target satellite image as the input of a road identification model, and extracting each candidate road included in the target satellite image;
wherein the road identification model is generated by:
matching the basic road data with the sample satellite image to determine a road included in the sample satellite image;
and training the convolution network model according to the road included in the sample satellite image to generate the road identification model.
7. The apparatus of claim 5, wherein the trajectory acquisition module is specifically configured to:
taking the track data of each device as the input of a track classification model to obtain the driving track included in the track data of each device;
wherein the trajectory classification model is generated by:
determining a traffic type of the sample trajectory data and trajectory characteristics of the sample trajectory data, wherein the trajectory characteristics include at least one of an average speed, an acceleration, a speed variance, a maximum speed, a curvature, and a direction;
and training a classification model according to the traffic type of the sample track data and the track characteristics of the sample track data to generate the track classification model.
8. A server, characterized in that the server comprises:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a road screening method as recited in any one of claims 1-4.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a method of road screening as claimed in any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811296480.5A CN111121797B (en) | 2018-11-01 | 2018-11-01 | Road screening method, device, server and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811296480.5A CN111121797B (en) | 2018-11-01 | 2018-11-01 | Road screening method, device, server and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111121797A CN111121797A (en) | 2020-05-08 |
| CN111121797B true CN111121797B (en) | 2021-11-12 |
Family
ID=70494915
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811296480.5A Active CN111121797B (en) | 2018-11-01 | 2018-11-01 | Road screening method, device, server and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111121797B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113703015B (en) * | 2020-05-22 | 2024-05-03 | 阿里巴巴集团控股有限公司 | Data processing method, device, equipment and medium |
| CN111811525B (en) * | 2020-06-09 | 2022-05-10 | 广东国地规划科技股份有限公司 | Road network generation method and system based on remote sensing image and floating car track |
| CN112131233B (en) * | 2020-08-28 | 2022-11-15 | 北京百度网讯科技有限公司 | Method, apparatus, device and computer storage medium for identifying updated road |
| CN114383600B (en) * | 2022-03-23 | 2022-07-19 | 北京百度网讯科技有限公司 | Processing method and device for map, electronic equipment and storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1607907A1 (en) * | 2004-06-16 | 2005-12-21 | Mitsubishi Electric Information Technology Centre Europe B.V. | Algorithm for line tracking using a circular sector search window |
| CN105183893A (en) * | 2015-09-29 | 2015-12-23 | 百度在线网络技术（北京）有限公司 | Positioning track classification method and device |
| CN105371857A (en) * | 2015-10-14 | 2016-03-02 | 山东大学 | Device and method for constructing road network topology based on bus GNSS space-time tracking data |
| CN105509753A (en) * | 2015-12-28 | 2016-04-20 | 北京握奇智能科技有限公司 | Map matching method and system based on floating car satellite positioning data |
| CN107025440A (en) * | 2017-03-27 | 2017-08-08 | 北京航空航天大学 | A kind of remote sensing images method for extracting roads based on new convolutional neural networks |
| CN107203761A (en) * | 2017-06-15 | 2017-09-26 | 厦门大学 | Road width method of estimation based on high-resolution satellite image |
| CN108229270A (en) * | 2017-01-12 | 2018-06-29 | 北京市商汤科技开发有限公司 | For identifying the method, apparatus and electronic equipment of road from remote sensing images |
| CN108229274A (en) * | 2017-02-28 | 2018-06-29 | 北京市商汤科技开发有限公司 | Multilayer neural network model training, the method and apparatus of roadway characteristic identification |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8908914B2 (en) * | 2012-01-17 | 2014-12-09 | Maxlinear, Inc. | Method and system for map generation for location and navigation with user sharing/social networking |
-
2018
- 2018-11-01 CN CN201811296480.5A patent/CN111121797B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1607907A1 (en) * | 2004-06-16 | 2005-12-21 | Mitsubishi Electric Information Technology Centre Europe B.V. | Algorithm for line tracking using a circular sector search window |
| CN105183893A (en) * | 2015-09-29 | 2015-12-23 | 百度在线网络技术（北京）有限公司 | Positioning track classification method and device |
| CN105371857A (en) * | 2015-10-14 | 2016-03-02 | 山东大学 | Device and method for constructing road network topology based on bus GNSS space-time tracking data |
| CN105509753A (en) * | 2015-12-28 | 2016-04-20 | 北京握奇智能科技有限公司 | Map matching method and system based on floating car satellite positioning data |
| CN108229270A (en) * | 2017-01-12 | 2018-06-29 | 北京市商汤科技开发有限公司 | For identifying the method, apparatus and electronic equipment of road from remote sensing images |
| CN108229274A (en) * | 2017-02-28 | 2018-06-29 | 北京市商汤科技开发有限公司 | Multilayer neural network model training, the method and apparatus of roadway characteristic identification |
| CN107025440A (en) * | 2017-03-27 | 2017-08-08 | 北京航空航天大学 | A kind of remote sensing images method for extracting roads based on new convolutional neural networks |
| CN107203761A (en) * | 2017-06-15 | 2017-09-26 | 厦门大学 | Road width method of estimation based on high-resolution satellite image |
Non-Patent Citations (2)
| Title |
|---|
| "Combining Float Car Data and Multispectral Satellite Images to Extract Road Features and Networks";Chun Liu 等;《Lecture Notes in Geoinformation and Cartography》;20130103;第29-43页 * |
| Combining Float Car Data and Multispectral Satellite Images to Extract Road Features and Networks;Chun Liu 等;《Lecture Notes in Geoinformation and Cartography》;20130103;第29-43页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111121797A (en) | 2020-05-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111121797B (en) | Road screening method, device, server and storage medium | |
| JP7221089B2 (en) | Stable simultaneous execution of location estimation and map generation by removing dynamic traffic participants | |
| CN110705405B (en) | Target labeling method and device | |
| US10628890B2 (en) | Visual analytics based vehicle insurance anti-fraud detection | |
| CN108229364B (en) | Building contour generation method and device, computer equipment and storage medium | |
| CN110647886A (en) | Interest point marking method and device, computer equipment and storage medium | |
| CN110956137A (en) | Point cloud data target detection method, system and medium | |
| CN109916415B (en) | Road type determination method, device, equipment and storage medium | |
| CN110378966A (en) | Camera extrinsic scaling method, device, computer equipment and storage medium | |
| CN112528859B (en) | Lane line detection method, device, equipment and storage medium | |
| CN112364843A (en) | Plug-in aerial image target positioning detection method, system and equipment | |
| CN113361643A (en) | Deep learning-based universal mark identification method, system, equipment and storage medium | |
| CN113091757A (en) | Map generation method and device | |
| CN110609879B (en) | Interest point duplicate determination method and device, computer equipment and storage medium | |
| CN114565780A (en) | Target identification method and device, electronic equipment and storage medium | |
| CN111488866B (en) | Invading object identification method and device based on deep learning and electronic equipment | |
| CN114202689A (en) | Point location marking method and device, electronic equipment and storage medium | |
| CN111126336B (en) | Sample collection method, device and equipment | |
| CN111488771B (en) | OCR hooking method, device and equipment | |
| CN112560700A (en) | Information association method and device based on motion analysis and electronic equipment | |
| CN110427911A (en) | A kind of Approach for road detection, device, equipment and storage medium | |
| CN115184744B (en) | GIS ultrahigh frequency discharge signal detection device and method based on fast-RCNN | |
| CN109815307B (en) | Position determination method, apparatus, device, and medium | |
| CN110909681A (en) | Vehicle and pedestrian video counting method and device based on neural network | |
| CN117789100A (en) | Method, device, equipment and storage medium for identifying optical fiber vibration event |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |