Geographic information surveying and mapping system

Abstract

The invention relates to the field of surveying and mapping, in particular to a geographic information surveying and mapping system. The technical problems are that when the unmanned aerial vehicle is used for geographic information surveying and mapping, for discrete data collected by the unmanned aerial vehicle, an existing surveying and mapping system adopts a method of firstly modeling and then importing the data into a model, data merging and association are achieved, the workload of modeling and importing the model is large, and therefore the requirement for surveying and mapping equipment is high; according to the technical scheme, the geographic information surveying and mapping system comprises a data acquisition module, a data processing module, a data judgment module, a data transmission module and a data encryption module, compared with the prior art that the requirements for surveying and mapping equipment and workers are high, the method has the advantages that the datum point is set, data collection and data fusion are carried out with the datum point as the datum, modeling is not needed, data fusion can be achieved through a simple algorithm, operation is easy, and the requirements for the surveying and mapping equipment and the workers are low.

Description

Technical Field

[0001] The present invention relates to the field of surveying and mapping, and in particular to a geographic information surveying and mapping system. Background Art

[0002] A geographic information system (GIS) is a technology and tool used to collect, store, manage, analyze, and display geographic data. By using GIS, we can obtain and process various data about the Earth's surface and geographical phenomena, such as topography, land use, population, and transportation. The working principle of a geographic information system is to combine geographic data with its corresponding location information. This data can come from a variety of different sources, such as satellite remote sensing, GPS measurements, maps, and surveys. In GIS, maps are an important component, providing a visual way to display and analyze data. GIS has a variety of application areas, including municipal planning, environmental management, agriculture, energy, and aerospace. In urban planning, GIS can help decision makers assess the suitability of land, design new buildings and infrastructure, and analyze factors such as traffic flow and population density. In environmental management, GIS can be used to monitor and protect natural resources, predict natural disasters, and assess the environment. Risk; In the agricultural field, GIS can be used for soil analysis, crop management and water resource planning; At the same time, GIS can also play a role in the energy and aerospace fields, and be used for resource exploration, wind farm site selection, etc.; In short, the geographic information mapping system is a powerful tool. By integrating and analyzing geographic data, it can provide in-depth insights into the earth's surface and geographical phenomena, and provide support for decision makers and planners in all walks of life; When using drones for geographic information mapping, for the discrete data collected by drones, the existing mapping system adopts the method of modeling first and then importing the data into the model to achieve data merging and association. The workload of modeling and importing the model is large, so the requirements for mapping equipment are relatively high. Summary of the Invention

[0003] In order to overcome the problem of discrete data collected by drones when using drones for geographic information surveying and mapping, the existing surveying and mapping systems adopt the method of first modeling and then importing the data into the model to achieve data merging and association. The workload of modeling and importing the model is large, and therefore the requirements for surveying and mapping equipment are high.

[0004] The technical solution of the present invention is: a geographic information surveying and mapping system, comprising: Data acquisition module, used to collect geographic information using remote sensing satellites, drones, manual surveying and mapping, and other means; The data processing module is used to process the data collected by the data collection module to obtain orderly and analyzable geographic data; The data judgment module is used to judge the correctness of the ordered and analyzable geographic data obtained after being processed by the data processing module; Data transmission module, used to realize data transmission between data acquisition module, data processing module and data judgment module; The data encryption module is used to encrypt data to prevent data loss when it is transmitted between the data acquisition module, the data processing module and the data judgment module.

[0005] Preferably, the data acquisition module collects the data required for surveying and mapping, the data processing module processes the data, the data judgment judges the correctness of the data, the data transmission module realizes the data transmission between the data acquisition module, the data processing module and the data judgment module, and the data encryption module ensures that the data will not be lost.

[0006] Preferably, the data collection module includes the following methods when collecting geographic information: A11: For surface geographic information, remote sensing satellites are used to collect surface image data as surface geographic data. A12: For geographic information on water bodies such as rivers, lakes, and streams, we use lidar and sonar to measure bottom data of rivers, lakes, and streams, and we use various sensors such as flow sensors to measure geographic information related to water flow. A13: Use a combination of drones and data acquisition equipment to collect geographic data of the surveying area.

[0007] Preferably, when using remote sensing satellites to collect image data of the earth's surface as geographic data of the earth's surface, the data is collected by the following steps: S11: using a remote sensing satellite to obtain a plurality of remote sensing images of the surveyed area, wherein the plurality of remote sensing images are remote sensing images obtained by the remote sensing satellite at multiple time points; S12: Use the CNN-based image recognition model to identify the acquired remote sensing images, determine whether there is cloud or fog obstruction in the remote sensing images, and remove the remote sensing images with cloud or fog obstruction areas greater than a certain percentage; S13: dividing the remaining remote sensing images into a plurality of regions of the same size; S14: Determine the clarity of each area, remove remote sensing images that do not meet the requirements, and use the remaining remote sensing images as the final surface geographic data.

[0008] Preferably, when using a method combining drones and data acquisition equipment to collect geographic data of a surveying area, the following steps are included: S21: Establish image positioning points within the surveying and mapping area. Multiple image positioning points are set, and the number of image positioning points per square kilometer of the surveying and mapping area is 3-8. The image positioning points are marked with eye-catching and durable signs, such as reflectors, triangular towers, or brightly colored signs, to indicate their locations and correspond to the image coordinate system. When the drone is collecting images, each image collected contains an image of at least one image positioning point. S22: Select one of the multiple sets of images collected by the drone as a reference image. The specific steps are as follows: (a) Eliminate images that are obscured by clouds or fog, have excessive lighting, have large angle differences, or are blurred. Eliminate images that have obvious human interference or motion blur. (b) Perform feature extraction on the selected image groups, extract SIFT / SURF feature points in the images, and determine the similarity between the images; (c) Using the extracted features, we compare and match different images, find the overlapping areas, and use the Ransac algorithm to filter the matching points and remove the noise matching points; (d) Select benchmark images based on coverage, image quality, and capture time; Coverage: Select an image group that covers the target area completely and has a high overlap rate as the benchmark; Image quality: select images with clear images, rich details and less noise as the benchmark; Shooting time: select the set of images with the most recent shooting time as the benchmark; S23: Using the selected reference image as the original image, other images collected by the UAV are merged into the reference image to obtain a complete mapping image. Image merging includes feature extraction, feature matching, image registration, and image fusion.

[0009] Preferably, when using drones for data collection, due to the effective flying altitude of drones, it is difficult for drones to collect all images and other data in the surveying area at the same location. Therefore, drones need to fly to different locations for data collection. Therefore, it is necessary to merge the data collected by the drones. By setting image positioning points, the image positioning points can be used as a reference to determine the distance and direction of other parts of the image at the image positioning points, so that the data collected by the drones can be simply merged, with small errors and simple processing, reducing the requirements for surveying equipment and staff.

[0010] Preferably, the data acquisition module includes the following steps when collecting geographic information using various means such as remote sensing satellites, drones, and manual mapping: S31: Use various methods to collect geographic information of the surveying area; S32: manually reviewing the collected geographic information to determine whether the collected geographic information is complete and valid; S33: Repeat steps S31 to S33 in different time periods, perform data collection three or more times, and obtain three or more groups of original data of geographic information collected in different time periods.

[0011] Preferably, the data processing module includes the following steps when processing the images collected by the remote sensing satellite: S41: Use the target detection and segmentation algorithm based on PyTorch deep learning to identify remote sensing images, and identify and divide the characteristic objects and different areas in remote sensing images; S42: specifying images of the same characteristic object or characteristic point in multiple remote sensing images by manual designation or automatic positioning; S43: stretching, cropping, and scaling the multiple remote sensing images so that images of the same characteristic object or characteristic point in the multiple remote sensing images completely overlap; S44: Merge multiple remote sensing images to obtain a complete remote sensing image, and divide the remote sensing image according to the recognition and segmentation results of the target detection and segmentation algorithm based on PyTorch deep learning. The meaning of different areas in the remote sensing image is determined to obtain the geographic data of the surface.

[0012] Preferably, when the area to be surveyed is too large, the remote sensing images collected by the remote sensing satellite cannot contain all the regional images within the surveying area, resulting in incomplete data. By setting feature points or feature objects as the benchmark and fusing multiple images, it is possible to accurately merge multiple remote sensing images into one remote sensing image, thereby ensuring the integrity and validity of the data.

[0013] Preferably, when determining the meaning of different areas in a remote sensing image, the following methods are included: A21: Determine the meaning of different areas in remote sensing images through manual field investigation or manual designation; A22: Use drones to collect high-definition images of different areas in remote sensing images, and use image recognition algorithms based on convolutional neural networks to determine the meaning of different areas in remote sensing images; A23: Determine the meaning of different areas in remote sensing images by collecting relevant data, including map information released through different channels and platforms.

[0014] Preferably, the data judgment module judges the correctness of the ordered and analyzable geographic data obtained after being processed by the data processing module, including the following steps: S51: Processing and simplifying the original data of three or more groups of geographic information collected at different time periods by the data collection module, wherein the simplification method includes image grayscale processing, weighted smoothing processing and simple moving average processing; S52: comparing three or more groups of original geographic information data collected at different time periods, determining the degree of difference between the original geographic information data collected at different time periods, and when the degree of difference is greater than a set threshold, eliminating the original data with a large difference and retaining the remaining original data; S53: Perform manual review. After manual professional review, the correctness of the data is determined.

[0015] Preferably, by setting up the measurement of three or more groups of geographic information data and using three or more groups of geographic information data for differentiated detection, it is possible to effectively avoid data collection errors caused by operational reasons or equipment failures, which further lead to errors in surveying and mapping results and cause irreparable impacts. It can effectively ensure the correctness and reliability of the data and effectively increase the accuracy of surveying and mapping.

[0016] Preferably, the data encryption module includes a data encryption module, a timestamp generation module and a data decryption module. The timestamp generation module is used to generate a timestamp containing time information according to the specific time when the data is generated, and attach the generated timestamp to the data. The data encryption module is used to generate different encryption keys according to certain rules based on the timestamp generated by the timestamp generation module, and encrypt the data. The data decryption module is used to read the timestamp information in the data, and use the same rules as the data encryption module to generate a decryption key to decrypt the data.

[0017] Preferably, the data encryption module includes the following steps to prevent data from being lost when being transmitted between the data acquisition module, the data processing module and the data judgment module: S61: After the data is generated, the timestamp generation module generates a timestamp containing time information according to the specific time when the data is generated, and appends the generated timestamp to the data; S62: The data encryption module generates different encryption keys according to certain rules based on the timestamp generated by the timestamp generation module, and encrypts the data; S63: The data transmission module transmits the encrypted data; S64: After the data transmission is completed, the data decryption module reads the timestamp information in the data and uses the same rules as the data encryption module to generate a decryption key to decrypt the data.

[0018] Beneficial effects of the present invention: 1. Compared with existing technologies, when using drones for geographic information surveying and mapping, the existing surveying and mapping systems use a method of first modeling and then importing the data into the model to achieve data merging and association. The workload of modeling and importing the model is large, and therefore the requirements for surveying and mapping equipment are high. This surveying and mapping system sets reference points and uses the reference points as the basis for data collection and data fusion. It does not require modeling and can achieve data fusion through simple algorithms. It is simple to operate and has low requirements for surveying and mapping equipment and personnel. 2. When using drones for data collection, due to the effective flight altitude of drones, it is difficult for drones to collect all images and other data in the surveying area at the same location. Therefore, drones need to fly to different locations for data collection. Therefore, it is necessary to merge the data collected by drones. By setting image positioning points, the image positioning points can be used as a reference to determine the distance and direction of other parts of the image at the image positioning points. In this way, the data collected by drones can be simply merged, with small errors and simple processing, reducing the requirements for surveying equipment and personnel. 3. By setting up the measurement of three or more groups of geographic information data and using them for differential detection, it is possible to effectively avoid data collection errors caused by operational reasons or equipment failures, which may lead to errors in surveying and mapping results and cause irreversible impacts. This can effectively ensure the correctness and reliability of the data and effectively increase the accuracy of surveying and mapping. 4. When the area to be surveyed is too large, the remote sensing images collected by the remote sensing satellite cannot contain all the regional images within the surveying area, resulting in incomplete data. By setting feature points or feature objects as the benchmark and fusing multiple images, it is possible to accurately merge multiple remote sensing images into one remote sensing image to ensure the integrity and validity of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Shown is a schematic diagram of the structure of the geographic information surveying and mapping system of the present invention; Figure 2 Shown is a partial workflow diagram of the data acquisition module in the geographic information surveying and mapping system of the present invention. DETAILED DESCRIPTION

[0020] The present invention will be further described below with reference to the accompanying drawings and examples.

[0021] See also Figure 1 The present invention provides an embodiment: a geographic information surveying and mapping system, comprising: Data acquisition module, used to collect geographic information using remote sensing satellites, drones, manual surveying and mapping, and other means; The data processing module is used to process the data collected by the data collection module to obtain orderly and analyzable geographic data; The data judgment module is used to judge the correctness of the ordered and analyzable geographic data obtained after being processed by the data processing module; Data transmission module, used to realize data transmission between data acquisition module, data processing module and data judgment module; The data encryption module is used to encrypt data to prevent data loss when it is transmitted between the data acquisition module, the data processing module and the data judgment module.

[0022] Preferably, the data acquisition module collects the data required for surveying and mapping, the data processing module processes the data, the data judgment judges the correctness of the data, the data transmission module realizes the data transmission between the data acquisition module, the data processing module and the data judgment module, and the data encryption module ensures that the data will not be lost.

[0023] See also Figure 2 In this embodiment, the data collection module includes the following methods when collecting geographic information: A11: For surface geographic information, remote sensing satellites are used to collect surface image data as surface geographic data. A12: For geographic information on water bodies such as rivers, lakes, and streams, we use lidar and sonar to measure bottom data of rivers, lakes, and streams, and we use various sensors such as flow sensors to measure geographic information related to water flow. A13: Use a combination of drones and data acquisition equipment to collect geographic data of the surveying area.

[0024] Preferably, when using remote sensing satellites to collect image data of the earth's surface as geographic data of the earth's surface, the data is collected by the following steps: S11: using a remote sensing satellite to obtain a plurality of remote sensing images of the surveyed area, wherein the plurality of remote sensing images are remote sensing images obtained by the remote sensing satellite at multiple time points; S12: Use the CNN-based image recognition model to identify the acquired remote sensing images, determine whether there is cloud or fog obstruction in the remote sensing images, and remove the remote sensing images with cloud or fog obstruction areas greater than a certain percentage; S13: dividing the remaining remote sensing images into a plurality of regions of the same size; S14: Determine the clarity of each area, remove remote sensing images that do not meet the requirements, and use the remaining remote sensing images as the final surface geographic data.

[0025] Preferably, when using a method combining drones and data acquisition equipment to collect geographic data of a surveying area, the following steps are included: S21: Establish image positioning points within the surveying and mapping area. Multiple image positioning points are set, and the number of image positioning points per square kilometer of the surveying and mapping area is 3-8. The image positioning points are marked with eye-catching and durable signs, such as reflectors, triangular towers, or brightly colored signs, to indicate their locations and correspond to the image coordinate system. When the drone is collecting images, each image collected contains an image of at least one image positioning point. S22: Select one of the multiple sets of images collected by the drone as a reference image. The specific steps are as follows: (a) Eliminate images that are obscured by clouds or fog, have excessive lighting, have large angle differences, or are blurred. Eliminate images that have obvious human interference or motion blur. (b) Perform feature extraction on the selected image groups, extract SIFT / SURF feature points in the images, and determine the similarity between the images; (c) Using the extracted features, we compare and match different images, find the overlapping areas, and use the Ransac algorithm to filter the matching points and remove the noise matching points; (d) Select benchmark images based on coverage, image quality, and capture time; Coverage: Select an image group that covers the target area completely and has a high overlap rate as the benchmark; Image quality: select images with clear images, rich details and less noise as the benchmark; Shooting time: select the set of images with the most recent shooting time as the benchmark; S23: Using the selected reference image as the original image, other images collected by the UAV are merged into the reference image to obtain a complete mapping image. Image merging includes feature extraction, feature matching, image registration, and image fusion.

[0026] Preferably, when using drones for data collection, due to the effective flying altitude of drones, it is difficult for drones to collect all images and other data in the surveying area at the same location. Therefore, drones need to fly to different locations for data collection. Therefore, it is necessary to merge the data collected by the drones. By setting image positioning points, the image positioning points can be used as a reference to determine the distance and direction of other parts of the image at the image positioning points, so that the data collected by the drones can be simply merged, with small errors and simple processing, reducing the requirements for surveying equipment and staff.

[0027] Preferably, the data acquisition module includes the following steps when collecting geographic information using various means such as remote sensing satellites, drones, and manual mapping: S31: Use various methods to collect geographic information of the surveying area; S32: manually reviewing the collected geographic information to determine whether the collected geographic information is complete and valid; S33: Repeat steps S31 to S33 in different time periods, perform data collection three or more times, and obtain three or more groups of original data of geographic information collected in different time periods.

[0028] Preferably, the data processing module includes the following steps when processing the images collected by the remote sensing satellite: S41: Use the target detection and segmentation algorithm based on PyTorch deep learning to identify remote sensing images, and identify and divide the characteristic objects and different areas in remote sensing images; S42: specifying images of the same characteristic object or characteristic point in multiple remote sensing images by manual designation or automatic positioning; S43: stretching, cropping, and scaling the multiple remote sensing images so that images of the same characteristic object or characteristic point in the multiple remote sensing images completely overlap; S44: Merge multiple remote sensing images to obtain a complete remote sensing image, and divide the remote sensing image according to the recognition and segmentation results of the target detection and segmentation algorithm based on PyTorch deep learning. The meaning of different areas in the remote sensing image is determined to obtain the geographic data of the surface.

[0029] Preferably, when the area to be surveyed is too large, the remote sensing images collected by the remote sensing satellite cannot contain all the regional images within the surveying area, resulting in incomplete data. By setting feature points or feature objects as the benchmark and fusing multiple images, it is possible to accurately merge multiple remote sensing images into one remote sensing image, thereby ensuring the integrity and validity of the data.

[0030] Preferably, when determining the meaning of different areas in a remote sensing image, the following methods are included: A21: Determine the meaning of different areas in remote sensing images through manual field investigation or manual designation; A22: Use drones to collect high-definition images of different areas in remote sensing images, and use image recognition algorithms based on convolutional neural networks to determine the meaning of different areas in remote sensing images; A23: Determine the meaning of different areas in remote sensing images by collecting relevant data, including map information released through different channels and platforms.

[0031] Preferably, the data judgment module judges the correctness of the ordered and analyzable geographic data obtained after being processed by the data processing module, including the following steps: S51: Processing and simplifying the original data of three or more groups of geographic information collected at different time periods by the data collection module, wherein the simplification method includes image grayscale processing, weighted smoothing processing and simple moving average processing; S52: comparing three or more groups of original geographic information data collected at different time periods, determining the degree of difference between the original geographic information data collected at different time periods, and when the degree of difference is greater than a set threshold, eliminating the original data with a large difference and retaining the remaining original data; S53: Perform manual review. After manual professional review, the correctness of the data is determined.

[0032] Preferably, by setting up the measurement of three or more groups of geographic information data and using three or more groups of geographic information data for differentiated detection, it is possible to effectively avoid data collection errors caused by operational reasons or equipment failures, which further lead to errors in surveying and mapping results and cause irreparable impacts. It can effectively ensure the correctness and reliability of the data and effectively increase the accuracy of surveying and mapping.

[0033] Preferably, the data encryption module includes a data encryption module, a timestamp generation module and a data decryption module. The timestamp generation module is used to generate a timestamp containing time information according to the specific time when the data is generated, and attach the generated timestamp to the data. The data encryption module is used to generate different encryption keys according to certain rules based on the timestamp generated by the timestamp generation module, and encrypt the data. The data decryption module is used to read the timestamp information in the data, and use the same rules as the data encryption module to generate a decryption key to decrypt the data.

[0034] Preferably, the data encryption module includes the following steps to prevent data from being lost when being transmitted between the data acquisition module, the data processing module and the data judgment module: S61: After the data is generated, the timestamp generation module generates a timestamp containing time information according to the specific time when the data is generated, and appends the generated timestamp to the data; S62: The data encryption module generates different encryption keys according to certain rules based on the timestamp generated by the timestamp generation module, and encrypts the data; S63: The data transmission module transmits the encrypted data; S64: After the data transmission is completed, the data decryption module reads the timestamp information in the data and uses the same rules as the data encryption module to generate a decryption key to decrypt the data.

[0035] The embodiments of the present invention are described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Various changes can be made within the scope of knowledge of those skilled in the art without departing from the spirit of the present invention.

Claims

1. A geographic information surveying and mapping system; characterized in that: Includes: Data acquisition module, used to collect geographic information using remote sensing satellites, drones, manual surveying and mapping, and other means; The data processing module is used to process the data collected by the data collection module to obtain orderly and analyzable geographic data; The data judgment module is used to judge the correctness of the ordered and analyzable geographic data obtained after being processed by the data processing module; Data transmission module, used to realize data transmission between data acquisition module, data processing module and data judgment module; The data encryption module is used to encrypt data to prevent data loss when it is transmitted between the data acquisition module, the data processing module and the data judgment module.

2. A geographic information surveying and mapping system according to claim 1, characterized in that: The data collection module includes the following methods when collecting geographic information: A11: For surface geographic information, remote sensing satellites are used to collect surface image data as surface geographic data. A12: For geographic information on water bodies such as rivers, lakes, and streams, we use lidar and sonar to measure bottom data of rivers, lakes, and streams, and we use various sensors such as flow sensors to measure geographic information related to water flow. A13: Use a combination of drones and data acquisition equipment to collect geographic data of the surveying area.

3. A geographic information surveying and mapping system according to claim 2, characterized in that: When using remote sensing satellites to collect surface image data as surface geographic data, the following steps are performed: S11: using a remote sensing satellite to obtain a plurality of remote sensing images of the surveyed area, wherein the plurality of remote sensing images are remote sensing images obtained by the remote sensing satellite at multiple time points; S12: Use the CNN-based image recognition model to identify the acquired remote sensing images, determine whether there is cloud or fog obstruction in the remote sensing images, and remove the remote sensing images with cloud or fog obstruction areas greater than a certain percentage; S13: dividing the remaining remote sensing images into a plurality of regions of the same size; S14: Determine the clarity of each area, remove remote sensing images that do not meet the requirements, and use the remaining remote sensing images as the final surface geographic data.

4. A geographic information surveying and mapping system according to claim 3, characterized in that: When using a combination of drones and data collection equipment to collect geographic data of the surveying area, the following steps are included: S21: Establish image positioning points within the surveying and mapping area. Multiple image positioning points are set, and the number of image positioning points per square kilometer of the surveying and mapping area is 3-8. The image positioning points are marked with eye-catching and durable signs, such as reflectors, triangular towers, or brightly colored signs, to indicate their locations and correspond to the image coordinate system. When the drone is collecting images, each image collected contains an image of at least one image positioning point. S22: Select one of the multiple sets of images collected by the drone as a reference image. The specific steps are as follows: (a) Eliminate images that are obscured by clouds or fog, have excessive lighting, have large angle differences, or are blurred. Eliminate images that have obvious human interference or motion blur. (b) Perform feature extraction on the selected image groups, extract SIFT / SURF feature points in the images, and determine the similarity between the images; (c) Using the extracted features, we compare and match different images, find the overlapping areas, and use the Ransac algorithm to filter the matching points and remove the noise matching points; (d) Select benchmark images based on coverage, image quality, and capture time; Coverage: Select an image group that covers the target area completely and has a high overlap rate as the benchmark; Image quality: select images with clear images, rich details and less noise as the benchmark; Shooting time: select the set of images with the most recent shooting time as the benchmark; S23: Using the selected reference image as the original image, other images collected by the UAV are merged into the reference image to obtain a complete mapping image. Image merging includes feature extraction, feature matching, image registration, and image fusion.

5. A geographic information surveying and mapping system according to claim 4, characterized in that: The data acquisition module collects geographic information using remote sensing satellites, drones, and manual mapping, including the following steps: S31: Use various methods to collect geographic information of the surveying area; S32: manually reviewing the collected geographic information to determine whether the collected geographic information is complete and valid; S33: Repeat steps S31 to S33 in different time periods, perform data collection three or more times, and obtain three or more groups of original data of geographic information collected in different time periods.

6. A geographic information surveying and mapping system according to claim 5, characterized in that: The data processing module processes images collected by remote sensing satellites, including the following steps: S41: Use the target detection and segmentation algorithm based on PyTorch deep learning to identify remote sensing images, and identify and divide the characteristic objects and different areas in remote sensing images; S42: specifying images of the same characteristic object or characteristic point in multiple remote sensing images by manual designation or automatic positioning; S43: stretching, cropping, and scaling the multiple remote sensing images so that images of the same characteristic object or characteristic point in the multiple remote sensing images completely overlap; S44: Merge multiple remote sensing images to obtain a complete remote sensing image, and divide the remote sensing image according to the recognition and segmentation results of the target detection and segmentation algorithm based on PyTorch deep learning. The meaning of different areas in the remote sensing image is determined to obtain the geographic data of the surface.

7. A geographic information surveying and mapping system according to claim 6, characterized in that: When determining the meaning of different areas in remote sensing images, the following methods are included: A21: Determine the meaning of different areas in remote sensing images through manual field investigation or manual designation; A22: Use drones to collect high-definition images of different areas in remote sensing images, and use image recognition algorithms based on convolutional neural networks to determine the meaning of different areas in remote sensing images; A23: Determine the meaning of different areas in remote sensing images by collecting relevant data, including map information released through different channels and platforms.

8. A geographic information surveying and mapping system according to claim 7, characterized in that: The data judgment module judges the correctness of the ordered and analyzable geographic data obtained after processing by the data processing module, including the following steps: S51: Processing and simplifying the original data of three or more groups of geographic information collected at different time periods by the data collection module, wherein the simplification method includes image grayscale processing, weighted smoothing processing and simple moving average processing; S52: comparing three or more groups of original geographic information data collected at different time periods, determining the degree of difference between the original geographic information data collected at different time periods, and when the degree of difference is greater than a set threshold, eliminating the original data with a large difference and retaining the remaining original data; S53: Perform manual review. After manual professional review, the correctness of the data is determined.

9. A geographic information surveying and mapping system according to claim 8, characterized in that: The data encryption module includes a data encryption module, a timestamp generation module and a data decryption module. The timestamp generation module is used to generate a timestamp containing time information according to the specific time when the data is generated, and attach the generated timestamp to the data. The data encryption module is used to generate different encryption keys according to certain rules based on the timestamp generated by the timestamp generation module, and encrypt the data. The data decryption module is used to read the timestamp information in the data, and use the same rules as the data encryption module to generate a decryption key to decrypt the data.

10. A geographic information surveying and mapping system according to claim 9, characterized in that: The data encryption module prevents data from being lost when being transmitted between the data acquisition module, the data processing module, and the data judgment module, and includes the following steps: S61: After the data is generated, the timestamp generation module generates a timestamp containing time information according to the specific time when the data is generated, and appends the generated timestamp to the data; S62: The data encryption module generates different encryption keys according to certain rules based on the timestamp generated by the timestamp generation module, and encrypts the data; S63: The data transmission module transmits the encrypted data; S64: After the data transmission is completed, the data decryption module reads the timestamp information in the data and uses the same rules as the data encryption module to generate a decryption key to decrypt the data.

Images