Landscape map generation method and system based on big data, terminal and medium

Abstract

The invention is suitable for the technical field of data processing, and provides a landscape map generation method and system based on big data, a terminal and a medium, and the method comprises the steps: firstly obtaining remote sensing image information of a target landscape and multiple pieces of local real-shot image information, and then carrying out the division processing of the remote sensing image information based on a preset landscape type set, according to the method, multiple pieces of landscape basic unit information are quickly generated, then image optimization processing is performed on the landscape basic unit information based on local real-shot image information, optimization unit information is effectively generated, and finally, landscape map information is accurately generated according to multiple pieces of optimization unit information. According to the method, a big data technology and a remote sensing image technology can be combined, the landscape map with high display accuracy is generated, the accuracy of image recognition is remarkably improved, and powerful technical support and data basis are provided for deeply researching the overall change process of a target landscape.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and in particular to a method, system, terminal and medium for generating a landscape map based on big data. Background Art

[0002] Maps can help analysts better understand things through images, and landscape maps are a tool that uses graphic thinking methods to study landscape patterns and processes. They are an important means of understanding and expressing natural landscapes and their dynamic changes.

[0003] At present, there is a lack of a landscape map that combines big data technology and remote sensing image technology, which is not conducive to improving the display accuracy of landscape maps. There is a problem of low display accuracy, which needs further improvement. Summary of the invention

[0004] Based on this, the embodiments of the present application provide a landscape map generation method, system, terminal and medium based on big data to solve the problem of low display accuracy in the prior art.

[0005] In a first aspect, an embodiment of the present application provides a method for generating a landscape map based on big data, the method comprising:

[0006] Acquire remote sensing image information and multiple local real-shot image information of the target landscape;

[0007] Based on a preset landscape type set, the remote sensing image information is divided and processed to generate a plurality of landscape basic unit information, wherein the landscape type set includes a plurality of different landscape type information;

[0008] Based on the local real-shot image information, image optimization processing is performed on each of the basic unit information of the landscape to generate optimized unit information;

[0009] Landscape map information is generated based on the plurality of optimization unit information.

[0010] Compared with the prior art, the beneficial effect is as follows: the landscape map generation method based on big data provided in the embodiment of the present application, the terminal device can first obtain the remote sensing image information and multiple local real-shot image information of the target landscape, and then divide and process the remote sensing image information based on a preset landscape type set, quickly generate multiple landscape basic unit information, and then optimize the image of each landscape basic unit information based on the local real-shot image information, effectively generate optimized unit information, and finally accurately generate landscape map information based on multiple optimized unit information, thereby realizing the combination of big data technology and remote sensing image technology to generate a landscape map with high display accuracy, which to a certain extent solves the current problem of low display accuracy.

[0011] In a second aspect, an embodiment of the present application provides a landscape map generation system based on big data, the system comprising:

[0012] Remote sensing image information acquisition module: used to obtain remote sensing image information of the target landscape and multiple local real-shot image information;

[0013] A landscape basic unit information generation module is used to divide and process the remote sensing image information based on a preset landscape type set to generate a plurality of landscape basic unit information, wherein the landscape type set includes a plurality of different landscape type information;

[0014] An optimized unit information generation module is used to perform image optimization processing on each of the basic unit information of the landscape based on the local real-shot image information to generate optimized unit information;

[0015] Landscape map information generation module: used to generate landscape map information based on the plurality of optimization unit information.

[0016] In a third aspect, an embodiment of the present application provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method of the first aspect described above when executing the computer program.

[0017] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method of the first aspect described above are implemented.

[0018] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below.

[0020] Figure 1 It is a flow chart of a landscape map generation method provided in an embodiment of the present application;

[0021] Figure 2 It is a flowchart of step S300 in the landscape atlas generation method provided in one embodiment of the present application;

[0022] Figure 3 It is a schematic diagram of the process after step S400 in the landscape atlas generation method provided in one embodiment of the present application;

[0023] Figure 4It is a module block diagram of a landscape atlas generation system provided by an embodiment of the present application;

[0024] Figure 5 It is a schematic diagram of a terminal device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0025] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0026] In the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0027] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0028] In order to illustrate the technical solution described in this application, a specific embodiment is provided below for illustration.

[0029] See also Figure 1 , Figure 1 : is a flow chart of a landscape map generation method based on big data provided in an embodiment of the present application. In this embodiment, the execution subject of the landscape map generation method is a terminal device. It is understandable that the types of terminal devices include but are not limited to mobile phones, tablet computers, laptop computers, ultra-mobile personal computers (Ultra-Mobile Personal Computer, UMPC), netbooks, personal digital assistants (Personal Digital Assistant, PDA), etc., and the embodiments of the present application do not impose any restrictions on the specific types of terminal devices.

[0030] See also Figure 1The landscape map generation method provided in the embodiment of the present application includes but is not limited to the following steps:

[0031] In S100, remote sensing image information and multiple local real-shot image information of the target landscape are obtained.

[0032] Specifically, the terminal device can obtain remote sensing image information and multiple local real-shot image information of the target landscape, thereby obtaining the macro features and local detail features of the target landscape, wherein the remote sensing image information is used to describe the image data obtained for the target landscape through remote sensing technology; the local real-shot image information is used to describe the image obtained by shooting a part of the target landscape.

[0033] In S200, based on a preset landscape type set, the remote sensing image information is divided and processed to generate a plurality of landscape basic unit information.

[0034] Specifically, after the terminal device acquires remote sensing image information and local real-shot image information, the terminal device can divide and process the remote sensing image information based on a preset landscape type set, and generate multiple landscape basic unit information, thereby realizing regional division of the remote sensing image information, wherein the landscape type set includes multiple different landscape type information. For example, the landscape type information may be lakes, grasslands, flowers or trees.

[0035] In S300, based on the local real-shot image information, image optimization processing is performed on each landscape basic unit information to generate optimized unit information.

[0036] Specifically, after the terminal device generates a plurality of landscape basic unit information, the terminal device may perform image optimization processing on each landscape basic unit information based on the local real-shot image information, and effectively generate optimized unit information.

[0037] In some possible implementations, in order to optimize the image processing of each landscape basic unit information, please refer to Figure 2 Step S300 includes but is not limited to the following steps:

[0038] In S310 , for each piece of local real-shot image information: based on a preset image recognition algorithm, image recognition processing is performed on the local real-shot image information to generate first object type information.

[0039] Specifically, the terminal device can perform this processing on each local real-shot image information: based on a preset image recognition algorithm, perform image recognition processing on the local real-shot image information to quickly generate first object type information, wherein the image recognition algorithm can be an image recognition algorithm based on a convolutional neural network; the first object type information is used to describe the type corresponding to the object in the local real-shot image information.

[0040] In S320, for each piece of landscape basic unit information: based on an image recognition algorithm, image recognition processing is performed on the landscape basic unit information to generate second object type information.

[0041] Specifically, after the terminal device generates the first object type information, the terminal device can perform this processing on each landscape basic unit information: based on the above-mentioned image recognition algorithm, perform image recognition processing on the landscape basic unit information to quickly generate second object type information, wherein the second object type information is used to describe the type corresponding to the object in the landscape basic unit information.

[0042] In S330, based on the first object type information, image optimization processing is performed on the second object type information of the same type to generate optimized unit information.

[0043] Specifically, after the terminal device generates the second object type information, the terminal device can perform image optimization processing on the second object type information of the same type based on the first object type information, effectively generate optimized unit information, and thus integrate local detail features into macro features.

[0044] In S400, landscape atlas information is generated based on a plurality of optimized unit information.

[0045] Specifically, after the terminal device generates the optimization unit information, the terminal device can effectively generate landscape map information based on multiple optimization unit information, thereby realizing the combination of big data technology and remote sensing image technology to generate a landscape map with high display accuracy and including a time dimension.

[0046] In some possible implementations, in order to better understand and study the changes in the target landscape, please refer to Figure 3 After step S400, the method further includes but is not limited to the following steps:

[0047] In S410, standard change rate information and real-time change rate information corresponding to each optimization unit information are obtained.

[0048] Specifically, the terminal device may first obtain the standard change rate information and real-time change rate information corresponding to each optimization unit information, wherein the standard change rate information is used to describe the standard change rate determined under preset standard environmental conditions. For example, for the optimization unit information of plant types, the standard change rate information may be the standard change speed corresponding to the plant density within a preset time span, and the standard change rate information may also be the average value of the standard change speed corresponding to the plant density, height, and coverage area within the preset time span. The real-time change rate information is used to describe the real-time change rate determined under actual environmental conditions. For example, for the optimization unit information of plant types, the real-time change rate information may be the real-time change speed of the plant density within a preset time span, and the real-time change rate information may also be the average value of the real-time change speed of the plant density, height, and coverage area within the preset time span.

[0049] In S420, first overall change index information of the optimized unit information is determined according to the standard change rate information, the real-time change rate information and a preset first change index calculation formula.

[0050] Specifically, after the terminal device obtains the standard change rate information and the real-time change rate information, the terminal device can accurately determine the first overall change index information of the optimization unit information based on the standard change rate information, the real-time change rate information and the preset first change index calculation formula.

[0051] In some possible implementations, in order to determine the first overall change index information, step S420 includes but is not limited to the following steps:

[0052] In S421, the standard change rate information and the real-time change rate information are input into a preset first change index calculation formula to determine first overall change index information of the optimization unit information.

[0053] Specifically, the terminal device can input the standard change rate information and the real-time change rate information into a preset first change index calculation formula to accurately determine the first overall change index information of the optimization unit information, thereby determining the overall change index of the same type of optimization unit information.

[0054] In some possible implementations, the first change index calculation formula may be:

[0055]

[0056] In the formula, C represents the first overall change index information, N represents the number of optimization unit information of the same type, i represents the type order of the optimization unit information, and B irepresents the real-time change rate information corresponding to the optimization unit information of the i-th type, A represents the standard change rate information, and X i It represents the compensation coefficient corresponding to the optimization unit information of the i-th type. The specific value of the compensation coefficient can be appropriately adjusted according to the actual environmental conditions. The specific value of the compensation coefficient can be 1.

[0057] In S430, second overall change index information of the target landscape is determined according to the first overall change index information and a preset second change index calculation formula.

[0058] In some possible implementations, in order to determine the second overall change index information of the target landscape, step S430 includes but is not limited to the following steps:

[0059] In S431, the first overall change index information is input into a preset second change index calculation formula to determine the second overall change index information of the target landscape.

[0060] Specifically, the terminal device may input the first overall change index information into a preset second change index calculation formula to accurately determine the second overall change index information of the target landscape, thereby achieving determination of the overall change index of the target landscape.

[0061] In some possible implementations, the second change index calculation formula may be:

[0062]

[0063] In the formula, D represents the second overall change index information, M represents the total number of types of optimized unit information, and C i Indicates the first overall change index information corresponding to the i-th type of optimization unit information, Y i It represents the weight correction factor corresponding to the i-th type of optimization unit information. The weight correction factor is used to highlight the importance of the change process of a certain type of optimization unit information according to actual needs.

[0064] The implementation principle of the landscape map generation method based on big data in the embodiment of the present application is as follows: the terminal device can first obtain remote sensing image information and multiple local real-shot image information of the target landscape, and then divide and process the remote sensing image information based on a preset landscape type set, quickly generate multiple landscape basic unit information, and then optimize the image of each landscape basic unit information based on the local real-shot image information, effectively generate optimized unit information, and finally accurately generate landscape map information based on multiple optimized unit information, thereby realizing the combination of big data technology and remote sensing image technology to generate a landscape map with high display accuracy, improve the accuracy of image recognition, and facilitate a more in-depth study of the overall change process of the target landscape.

[0065] It should be noted that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0066] The embodiment of the present application also provides a landscape map generation system based on big data. For the convenience of explanation, only the parts related to the present application are shown, such as Figure 4 As shown, the system 40 includes:

[0067] Remote sensing image information acquisition module 41: used to acquire remote sensing image information of a target landscape and multiple local real-shot image information;

[0068] The landscape basic unit information generating module 42 is used to divide and process the remote sensing image information based on a preset landscape type set to generate a plurality of landscape basic unit information, wherein the landscape type set includes a plurality of different landscape type information;

[0069] The optimized unit information generating module 43 is used to perform image optimization processing on each landscape basic unit information based on the local real-shot image information to generate optimized unit information;

[0070] The landscape map information generating module 44 is used to generate landscape map information according to a plurality of optimized unit information.

[0071] Optionally, the above-mentioned optimization unit information generation module 43 includes:

[0072] A first object type information generating submodule: for each local real shot image information: based on a preset image recognition algorithm, performing image recognition processing on the local real shot image information to generate first object type information, wherein the first object type information is used to describe the type corresponding to the object in the local real shot image information;

[0073] The second object type information generating submodule is used for: for each landscape basic unit information: based on the image recognition algorithm, performing image recognition processing on the landscape basic unit information to generate second object type information, wherein the second object type information is used to describe the type corresponding to the object in the landscape basic unit information;

[0074] The optimized unit information generating submodule is used to perform image optimization processing on the second object type information of the same type based on the first object type information to generate optimized unit information.

[0075] Optionally, the system 40 further includes:

[0076] Change rate information acquisition module: used to obtain standard change rate information and real-time change rate information corresponding to each optimization unit information;

[0077] A first overall change index information determination module: used to determine the first overall change index information of the optimization unit information according to the standard change rate information, the real-time change rate information and the preset first change index calculation formula;

[0078] The second overall change index information determination module is used to determine the second overall change index information of the target landscape according to the first overall change index information and a preset second change index calculation formula.

[0079] Optionally, the first overall change index information determination module includes:

[0080] The first overall change index information determination submodule is used to input the standard change rate information and the real-time change rate information into a preset first change index calculation formula to determine the first overall change index information of the optimization unit information, wherein the first change index calculation formula is:

[0081]

[0082] In the formula, C is the first overall change index information, N is the number of optimization unit information of the same type, i is the type order of the optimization unit information, B i is the real-time change rate information corresponding to the optimization unit information of the i-th type, A is the standard change rate information, X i is the compensation coefficient corresponding to the optimized unit information of the i-th type.

[0083] Optionally, the second overall change index information determination module includes:

[0084] The second overall change index information determination submodule is used to input the first overall change index information into a preset second change index calculation formula to determine the second overall change index information of the target landscape, wherein the second change index calculation formula is:

[0085]

[0086] Where D is the second overall change index information, M is the total number of types of optimized unit information, C i is the first overall change index information corresponding to the optimized unit information of the i-th type, Y i is the weight correction factor corresponding to the optimized unit information of the i-th type.

[0087] It should be noted that the information interaction, execution process and other contents between the above-mentioned modules are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0088] The present application also provides a terminal device, such as Figure 5 As shown, the terminal device 50 of this embodiment includes: a processor 51, a memory 52, and a computer program 53 stored in the memory 52 and executable on the processor 51. When the processor 51 executes the computer program 53, the steps in the above-mentioned landscape atlas generation method embodiment are implemented, for example Figure 1 Steps S100 to S400 shown; or, when the processor 51 executes the computer program 53, the functions of each module in the above device are realized, for example Figure 4 The functions of the modules 41 to 44 are shown.

[0089] The terminal device 50 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server, and the terminal device 50 includes but is not limited to a processor 51 and a memory 52. ​​Those skilled in the art will appreciate that Figure 5 It is only an example of the terminal device 50 and does not constitute a limitation of the terminal device 50. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device 50 may also include input and output devices, network access devices, buses, etc.

[0090] Among them, the processor 51 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.; the general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0091] The memory 52 may be an internal storage unit of the terminal device 50, such as a hard disk or memory of the terminal device 50, or the memory 52 may be an external storage device of the terminal device 50, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the terminal device 50; further, the memory 52 may also include both an internal storage unit and an external storage device of the terminal device 50, and the memory 52 may also store a computer program 53 and other programs and data required by the terminal device 50, and the memory 52 may also be used to temporarily store data that has been output or is to be output.

[0092] One embodiment of the present application also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the steps of each of the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc.; the computer-readable medium may include: any entity or device that can carry computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0093] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, all equivalent changes made according to the methods, principles, and structures of the present application should be included in the protection scope of the present application.

Claims

1. A landscape map generation method based on big data, characterized in that: The method comprises: Acquire remote sensing image information and multiple local real-shot image information of the target landscape; Based on a preset landscape type set, the remote sensing image information is divided and processed to generate a plurality of landscape basic unit information, wherein the landscape type set includes a plurality of different landscape type information; Based on the local real-shot image information, image optimization processing is performed on each of the basic unit information of the landscape to generate optimized unit information; Landscape map information is generated based on the plurality of optimization unit information.

2. The method according to claim 1, characterized in that The step of performing image optimization processing on each of the basic unit information of the landscape based on the local real-shot image information to generate optimized unit information includes: For each of the local real-shot image information: based on a preset image recognition algorithm, perform image recognition processing on the local real-shot image information to generate first object type information, wherein the first object type information is used to describe the type corresponding to the object in the local real-shot image information; For each of the landscape basic unit information: based on the image recognition algorithm, perform image recognition processing on the landscape basic unit information to generate second object type information, wherein the second object type information is used to describe the type corresponding to the object in the landscape basic unit information; Based on the first object type information, image optimization processing is performed on the second object type information of the same type to generate optimized unit information.

3. The method according to claim 2, characterized in that After generating landscape atlas information according to the plurality of optimization unit information, the method further includes: Obtaining standard change rate information and real-time change rate information corresponding to each of the optimization unit information; Determine the first overall change index information of the optimization unit information according to the standard change rate information, the real-time change rate information and a preset first change index calculation formula; The second overall change index information of the target landscape is determined according to the first overall change index information and a preset second change index calculation formula.

4. The method according to claim 3, characterized in that The determining the first overall change index information of the optimization unit information according to the standard change rate information, the real-time change rate information and a preset first change index calculation formula includes: The standard change rate information and the real-time change rate information are input into a preset first change index calculation formula to determine the first overall change index information of the optimization unit information, wherein the first change index calculation formula is: Wherein, C is the first overall change index information, N is the number of the same type of the optimization unit information, i is the type order of the optimization unit information, B i is the real-time change rate information corresponding to the optimization unit information of the i-th type, A is the standard change rate information, X i is the compensation coefficient corresponding to the optimization unit information of the ith type.

5. The method according to claim 4, characterized in that The determining the second overall change index information of the target landscape according to the first overall change index information and a preset second change index calculation formula includes: The first overall change index information is input into a preset second change index calculation formula to determine the second overall change index information of the target landscape, wherein the second change index calculation formula is: Where D is the second overall change index information, M is the total number of types of the optimization unit information, C i is the first overall change index information corresponding to the optimization unit information of the i-th type, Y i is the weight correction factor corresponding to the optimization unit information of the i-th type.

6. A landscape map generation system based on big data, characterized in that: The system comprises: Remote sensing image information acquisition module: used to obtain remote sensing image information of the target landscape and multiple local real-shot image information; A landscape basic unit information generation module is used to divide and process the remote sensing image information based on a preset landscape type set to generate a plurality of landscape basic unit information, wherein the landscape type set includes a plurality of different landscape type information; An optimized unit information generation module is used to perform image optimization processing on each of the basic unit information of the landscape based on the local real-shot image information to generate optimized unit information; Landscape map information generation module: used to generate landscape map information based on the plurality of optimization unit information.

7. The system according to claim 6, characterized in that The optimization unit information generation module includes: A first object type information generating submodule is configured to: for each of the local real shot image information, perform image recognition processing on the local real shot image information based on a preset image recognition algorithm to generate first object type information, wherein the first object type information is used to describe the type corresponding to the object in the local real shot image information; A second object type information generating submodule is used for: for each of the landscape basic unit information: based on the image recognition algorithm, performing image recognition processing on the landscape basic unit information to generate second object type information, wherein the second object type information is used to describe the type corresponding to the object in the landscape basic unit information; Optimized unit information generation submodule: used to perform image optimization processing on the second object type information of the same type based on the first object type information to generate optimized unit information.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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