Satellite image data set generation method and device based on visual language

Abstract

The embodiment of the invention provides a satellite image data set generation method and device based on a visual language model, and is applied to the technical field of image processing, and the method comprises the steps: extracting the related information of a specific event; acquiring a corresponding satellite image based on the specific event related information; identifying a specific event area in the satellite image and automatically labeling to generate a corresponding label; a dataset is generated based on the satellite images and the corresponding tags. In this way, manufacturing of the satellite image data set can be automatically and efficiently achieved, and the technical problems that time and labor are consumed in the data set manufacturing process, and consistency and quality of the data set are difficult to guarantee are solved.

Description

Technical Field

[0001] The present disclosure relates to the field of image processing technology, and in particular to a method and device for generating a satellite image data set based on visual language. Background Art

[0002] The traditional dataset production process usually relies on manual analysis and manual drawing, which has obvious shortcomings. First, traditional satellite data processing methods usually rely on manual or semi-automatic processing procedures, including satellite image acquisition, preprocessing, analysis and result output. This method has the problems of low processing efficiency and limited analysis accuracy. Secondly, the current dataset production process usually relies on manual or semi-automatic methods, which require manual intervention for data cropping, labeling and formatting. This method is not only time-consuming and labor-intensive, but also difficult to ensure the consistency and quality of the dataset.

[0003] In recent years, with the development of satellite technology and the improvement of computing power, satellite images have played an increasingly important role. However, the production of satellite image datasets still has the above problems, which limits the pace of moving towards automation and efficiency. Summary of the invention

[0004] The present invention provides a method and device for generating a satellite image data set based on visual language, which solves the technical problems that the data set production process is time-consuming and labor-intensive, and it is difficult to ensure the consistency and quality of the data set.

[0005] According to a first aspect of the present disclosure, a method for generating a satellite image dataset based on visual language is provided. The method comprises:

[0006] Extract relevant information about a specific event;

[0007] Based on the information related to the specific event, obtain corresponding satellite images;

[0008] Identify specific event areas in the satellite image and automatically annotate and generate corresponding labels;

[0009] A data set is generated based on the satellite images and the corresponding labels.

[0010] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein extracting information related to a specific event includes:

[0011] Use visual language models to analyze text and images in news reports and social platforms, and automatically extract the time nodes and geographic location information of specific events.

[0012] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the method further includes:

[0013] Use the visual language model to query and download satellite images of a specified geographic area within a specific time period determined by the time node and geographic location information of a specific event.

[0014] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the method further includes:

[0015] The corresponding satellite images obtained are preprocessed with geometric correction and atmospheric correction, and satellite images of different bands are merged into a comprehensive image.

[0016] According to the above aspects and any possible implementation, an implementation is further provided, wherein the identifying a specific event area in a satellite image and automatically marking and generating a corresponding label comprises:

[0017] Remote sensing inversion technology is used to identify specific event areas in satellite images and automatically annotate and generate corresponding specific event labels.

[0018] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the method further includes:

[0019] Use the divided data sets to train and optimize the specific event recognition model.

[0020] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the method further includes:

[0021] Continuously acquire new satellite image datasets of specific events and dynamically update datasets of specific events.

[0022] According to a second aspect of the present disclosure, a device for generating a satellite image data set based on visual language is provided. The device comprises:

[0023] Extraction module, used to extract information related to specific events;

[0024] A download module, used to obtain corresponding satellite images based on the information related to the specific event;

[0025] A processing module is used to identify specific event areas in satellite images and automatically annotate and generate corresponding labels;

[0026] A generating module is used to generate a data set based on the satellite image and the corresponding label.

[0027] According to a third aspect of the present disclosure, an electronic device is provided, which includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the program, the method described above is implemented.

[0028] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method according to the first aspect and / or the second aspect of the present disclosure is implemented.

[0029] In the present disclosure, a satellite image dataset is produced based on a visual language model. First, the visual language model is used to analyze the text and images in news reports and social platforms, and the time nodes and geographic location information of specific events are automatically extracted. Then, according to the time nodes and geographic location information of specific events, satellite images of a specified geographic area within a specific time period are queried and downloaded, and the preprocessing operation of the satellite images is automatically performed. Then, remote sensing inversion technology is used to identify specific event areas in satellite images and automatically annotate and generate corresponding specific event labels, thereby constructing a high-quality dataset. The above method can realize the automated production of datasets and provide continuous, efficient and accurate dataset technical support for various fields.

[0030] It should be understood that the contents described in the summary of the invention are not intended to limit 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 easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0032] Figure 1 A flowchart of a method for generating a satellite image dataset based on a visual language model according to an embodiment of the present disclosure is shown;

[0033] Figure 2 A government reported fire information graph according to an embodiment of the present disclosure is shown;

[0034] Figure 3 A fire scene diagram according to an embodiment of the present disclosure is shown;

[0035] Figure 4 A normalized combustion index difference diagram before and after a fire according to an embodiment of the present disclosure is shown;

[0036] Figure 5 A fire area marking diagram according to an embodiment of the present disclosure is shown;

[0037] Figure 6 A fire image and a corresponding label map according to an embodiment of the present disclosure are shown;

[0038] Figure 7A block diagram of a satellite image data set generation device based on a visual language model according to an embodiment of the present disclosure is shown;

[0039] Figure 8 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0041] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0042] In this disclosure, in order to solve the problem that traditional data set production is time-consuming and labor-intensive, and it is difficult to ensure the consistency and quality of data sets, a method for generating satellite image data sets based on a visual language model is proposed. The method includes: using a visual language model to extract key information of specific events from news texts and social platforms, downloading corresponding satellite images based on the extracted key information, and performing preprocessing operations on the satellite images; then identifying specific event areas in the satellite images and automatically annotating and generating corresponding labels, thereby constructing a satellite image data set. In this way, the entire process of satellite image data set production is intelligent and automated, and the efficiency and quality of data set production are improved.

[0043] Figure 1 A flowchart of a method 100 for generating a satellite image dataset based on a visual language model according to an embodiment of the present disclosure is shown. Figure 1 As shown in FIG. 1 , the method for generating a satellite image dataset based on a visual language model includes:

[0044] S101, extracting information related to a specific event.

[0045] In some embodiments, based on the search terms corresponding to preset specific events, network information such as news reports and social platforms are searched to obtain relevant network information of the corresponding specific events; and natural language processing technology is used to extract descriptions, time nodes and geographic location information of the specific events.

[0046] In some embodiments, since satellite image processing requires a certain amount of time, in order to ensure that the corresponding satellite image has been acquired, the existing network information is retrieved, or the incremental network information of a preset time period is retrieved.

[0047] In some embodiments, taking the specific event as a fire event as an example, multiple channels such as news reports and social platforms are searched using fire-related search terms to obtain relevant network information about the fire, such as government reports on fire information. Figure 2 As shown; natural language processing technology is used to extract the description, time node and geographic location information of the fire incident.

[0048] In some embodiments, in addition to fire, specific events can also be represented as floods, forest pests, environmental pollution, etc., all of which can use natural language processing technology to process satellite image data to extract corresponding relevant information.

[0049] In some embodiments, the Visual Language Model (VLM) uses natural language processing technology to extract the following core information from various network information:

[0050] Event Overview: A forest fire occurred in Qixing Town, Bijie City, Guizhou Province;

[0051] Time node: 15:00 on February 18, 2024;

[0052] Geographical location: Yachi Town, Qixingguan District, Bijie City, Guizhou Province.

[0053] In some embodiments, in order to further determine the scene and scale of the fire, the VLM uses image recognition technology to analyze the accompanying fire scene image, such as the fire scene image. Figure 3 As shown, significant features such as flames and smoke in the image are detected to further verify and supplement the information extracted from the text.

[0054] In some embodiments, in order to further verify the accuracy of the fire information extracted from the text information, the VLM verifies the acquired fire information by processing the metadata of the image, that is, encoding the image shooting time, location and other information into a numerical vector and inputting it together with the image's color, shape and other visual feature vectors into the VLM model for processing.

[0055] S102: Acquire corresponding satellite images based on the information related to the specific event.

[0056] In some embodiments, based on the acquired information on the time and location of the fire, VLM integrates these key parameters - namely, "February 18, 2024" as the time of the fire, and "Qixing Town, Bijie City, Guizhou Province" as the location of the fire - into the API of the Copernicus Open Access Hub to query and obtain satellite image data of a specified geographic area within a specific event period before and after the fire. Through this operation, VLM successfully downloaded the Sentinel-2L2A image before the fire, with the file name "S2A_MSIL2A_20240217T032821_N0510_R018_T48RXQ_20240217T064849.SAFE", and the Sentinel-2L2A image after the fire, with the file name "S2B_MSIL2A_20240323T032519_N0510_R018_T48RXQ_20240323T064807.SAFE".

[0057] S103, identifying a specific event area in the satellite image and automatically marking and generating a corresponding label.

[0058] In some embodiments, VLM will perform geometric correction on the image to correct the spatial deformation caused by factors such as the sensor's viewing angle and the rotation of the earth. Subsequently, atmospheric correction is performed to eliminate the impact of factors such as water vapor and aerosols in the atmosphere on the image quality and improve the reliability of the data. Next, VLM uses image fusion technology to merge satellite images of different bands into a comprehensive image to provide richer surface information.

[0059] In some embodiments, VLM first calls the preprocessing code of Sentinel-2 images to process the image data before and after the fire. During the processing, VLM specifically extracts five key bands: B2 (blue), B3 (green), B4 (red), B8 (near infrared) and B12 (shortwave infrared). To ensure the consistency and comparability of the data, VLM also resamples the B12 band to 10-meter resolution for synthesis with other bands.

[0060] In some embodiments, after completing the preprocessing operation on the satellite image, VLM merges multiple image data into a unified GeoTiff format file to facilitate subsequent data processing and analysis. The GeoTiff format not only supports the storage of multi-band images, but also retains geographic coordinates and projection information, making the data easier to understand and apply.

[0061] In some embodiments, remote sensing inversion technology is used to identify and mark the burned area of ​​the fire. Among them, the Normalized Burn Ratio (NBR) effectively identifies the impact of the fire on the surface by analyzing the changes in the reflectivity of the short-wave infrared band and the near-infrared band before and after the fire, and the Normalized Difference Vegetation Index (NDVI) is used to evaluate the changes in vegetation coverage before and after the fire, and assist in determining the scope and extent of the burned area.

[0062] In some embodiments, VLM calls Python scripts and uses the remote sensing inversion method NBR to conduct a detailed analysis of the area before and after the fire. By calculating the difference between the NBR value before the disaster and the NBR value after the disaster, the NBR difference before and after the fire is shown in the figure. Figure 4 As shown in Figure 2, VLM accurately identified the burned areas and used these areas as the final labeled areas for subsequent analysis and evaluation. Figure 5 shown.

[0063] S104: Generate a data set based on the satellite image and the corresponding label.

[0064] In some embodiments, in order to meet the training requirements of the fire monitoring model, the VLM first performs pixel-level labeling operations on the post-fire images of the marked fire area to obtain labeled post-fire images. Subsequently, the post-fire images are cropped with a window size of 512*512 pixels according to an 80% overlap rate to generate a series of training samples. The cropped images not only include the core area where the fire occurred, but also cover the surrounding areas that have not been affected by the fire, thereby increasing the diversity and richness of the data set.

[0065] In some embodiments, in order to enhance the generalization ability of the model, VLM further performs data augmentation operations on the cropped samples, including randomly rotating the image to simulate different observation angles, scaling the image to simulate observation effects at different distances, and translating the image to simulate different geographical locations. These transformations effectively simulate various situations that may occur in the real world.

[0066] In some embodiments, the data set division should not only consider the ratio of fire and non-fire areas, but also the temporal and spatial distribution of the data. By stratifying different types of fire data, the data set sample size is ensured to be sufficient and diverse. VLM divides these enhanced samples into training sets, validation sets, and test sets in a ratio of 6:2:2 to ensure that the model can be fully utilized and evaluated in the training, validation, and testing stages. Fire satellite images and corresponding labels are as follows: Figure 6 shown.

[0067] In some embodiments, the divided data set is used to train and optimize the specific event recognition model.

[0068] In some embodiments, VLM expands the newly obtained training data set into the original training data set, and uses them together for the training of semantic segmentation and change detection small models. During the training process, VLM executes the evaluation code and generates a detailed model performance report to monitor and evaluate the performance of semantic segmentation and change detection small models in real time. Based on these evaluation results, VLM will optimize and adjust the training model accordingly. For example, hyperparameters such as learning rate, batch size, and optimizer are adjusted to ensure the stability and consistency of training. At the same time, in order to avoid overfitting and find the best model performance, an early stopping strategy is adopted. This strategy terminates training early when the performance of the validation set begins to decline, thereby retaining the weight of the model when it performs best on the validation set.

[0069] In some embodiments, in order to evaluate the performance of the model, accuracy, recall and F1 score are selected as evaluation indicators. After the training is completed, VLM selects the model weights that perform best in these indicators as the latest weights of the semantic segmentation and change detection small models, thereby completing the iterative update and performance optimization of the semantic segmentation and change detection small models.

[0070] In some embodiments, the VLM continuously monitors and deeply analyzes the newly collected fire data according to the aforementioned steps, and automatically incorporates the new data into the training data set to achieve dynamic updating of the training data set.

[0071] According to the embodiments of the present disclosure, efficient and automatic generation of data sets is achieved, which solves the problems of time-consuming and labor-intensive traditional data set production process and difficulty in ensuring the consistency and quality of the data sets.

[0072] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0073] The above is an introduction to the method embodiment. The following is a further explanation of the scheme disclosed in the present invention through an apparatus embodiment.

[0074] Figure 7 FIG. 7 is a block diagram of a satellite image dataset generation device 700 based on a visual language model according to an embodiment of the present disclosure. Figure 7 As shown, the apparatus 700 includes:

[0075] Extraction module 701, used to extract information related to a specific event;

[0076] A download module 702 is used to obtain corresponding satellite images based on the information related to the specific event;

[0077] Processing module 703, used to identify specific event areas in satellite images and automatically mark and generate corresponding labels;

[0078] The generating module 704 is used to generate a data set based on the satellite image and the corresponding label.

[0079] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0080] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0081] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0082] Figure 8 A schematic block diagram of an electronic device 800 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, 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 processing, 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.

[0083] The electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a ROM 802 or a computer program loaded from a storage unit 808 into a RAM 803. In the RAM 803, various programs and data required for the operation of the electronic device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An I / O interface 805 is also connected to the bus 804.

[0084] Multiple components in the electronic device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the electronic device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0085] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 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 running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the method 100 in any other appropriate manner (e.g., by means of firmware).

[0086] Various implementations of the systems and techniques described above herein 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), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including 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.

[0087] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0088] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may 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 may 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.

[0089] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and a 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).

[0090] The systems and techniques described herein may 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 with 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 may 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.

[0091] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0092] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0093] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for generating a satellite image dataset based on a visual language model, comprising: Extract relevant information about a specific event; Based on the information related to the specific event, obtain corresponding satellite images; Identify specific event areas in the satellite image and automatically annotate and generate corresponding labels; A data set is generated based on the satellite images and the corresponding labels.

2. The method according to claim 1, characterized in that The extracting of relevant information of a specific event includes: Use visual language models to analyze text and images in news reports and social platforms, and automatically extract the time nodes and geographic location information of specific events.

3. The method according to claim 2, characterized in that The method further comprises: Use the visual language model to query and download satellite images of a specified geographic area within a specific time period determined by the time node and geographic location information of a specific event.

4. The method according to claim 1, characterized in that: The method further comprises: The corresponding satellite images obtained are preprocessed with geometric correction and atmospheric correction, and satellite images of different bands are merged into a comprehensive image.

5. The method according to claim 1, characterized in that The identifying of specific event areas in satellite images and automatically marking and generating corresponding labels includes: Remote sensing inversion technology is used to identify specific event areas in satellite images and automatically annotate and generate corresponding specific event labels.

6. The method according to claim 1, characterized in that The method further comprises: Use the divided data sets to train and optimize the specific event recognition model.

7. The method according to claim 1, characterized in that The method further comprises: Continuously acquire new satellite image datasets of specific events and dynamically update datasets of specific events.

8. A device for generating a satellite image data set based on a visual language model, comprising: Extraction module, used to extract information related to specific events; A download module, used to obtain corresponding satellite images based on the information related to the specific event; A processing module is used to identify specific event areas in satellite images and automatically annotate and generate corresponding labels; A generating module is used to generate a data set based on the satellite image and the corresponding label.

9. An electronic device, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, 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 of any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

Images