Geographic information high-precision spatial data acquisition and processing method and device

Abstract

The invention discloses a geographic information high-precision spatial data acquisition and processing method. The method comprises the following steps: acquiring a high-resolution image of a target area; the collected image data is preprocessed; performing spatial data processing operation on the preprocessed data; integrating the processed geographic information data with other spatial data, and carrying out comprehensive analysis and mining; and outputting the analyzed and processed high-precision spatial data as a map and a model, thereby solving the problems that the traditional geographic information data acquisition and processing method is low in precision, low in updating frequency and the like and cannot meet actual requirements.

Description

Technical Field

[0001] The present application relates to the field of geographic information, and more specifically, to a method and device for collecting and processing high-precision spatial data of geographic information. Background Art

[0002] With the rapid development of geographic information technology, the demand for high-precision spatial data is increasing. Traditional geographic information data collection methods, such as ground surveys and satellite remote sensing, are limited by equipment accuracy and collection methods and cannot obtain high-precision geographic information data. This low-precision data limits its application in areas such as precise positioning, navigation, and urban planning. In addition, traditional data collection methods are time-consuming, costly, and have low data update frequency, resulting in the inability of the acquired geographic information data to reflect the actual situation in a timely manner. Especially in application scenarios that require real-time data support, low update frequency will limit the practicality and accuracy of the data.

[0003] Geographic information data collected by traditional methods are often limited to a specific time and specific area, and cannot fully reflect the changes and diversity of the geographical environment. This will lead to data limitations and affect the comprehensive application and analysis of data. Traditional data processing methods require a lot of manpower and time investment and are inefficient. Human factors in the data processing process are also prone to introduce errors, affecting the accuracy and reliability of the data. Traditional methods lack real-time monitoring and feedback mechanisms and cannot capture geographic information in a timely manner. The changes and dynamics of data limit the real-time nature and application scope of geographic information data.

[0004] Regarding the related technologies, traditional geographic information data collection and processing methods have problems such as low accuracy and low update frequency, and cannot meet actual needs. No effective solutions have been proposed yet. Summary of the invention

[0005] The embodiments of the present application provide a method and device for collecting and processing high-precision spatial data of geographic information, so as to at least solve the problems in the related art that traditional geographic information data collection and processing methods have low accuracy, low update frequency, and cannot meet actual needs.

[0006] According to one embodiment of the present application, a method for collecting and processing high-precision spatial data of geographic information includes: Acquire high-resolution images of the target area; Preprocess the collected image data; Perform spatial data processing operations on the preprocessed data; Integrate the processed geographic information data with other spatial data for comprehensive analysis and mining; The analyzed and processed high-precision spatial data are output as maps and models.

[0007] Optionally, acquiring a high-resolution image of the target area includes: For data acquisition in large areas and large-scale geographic information application scenarios, high-resolution remote sensing satellites are used to acquire data; For small-scale and local area application scenarios, drones equipped with high-resolution cameras or sensors are used to obtain data.

[0008] Optionally, the preprocessing of the collected image data includes: Noise removal, image registration, image fusion, radiation correction; The noise removal includes: smoothing the image using a filter, identifying and eliminating outliers using a statistical method; The image registration includes: aligning images acquired from different sensors or at different times in the same coordinate system through feature point matching and geometric transformation; The image fusion includes: fusing images from different bands into a multispectral image through multi-band fusion, and superimposing multiple bands or multiple images into one image; The radiation correction includes: eliminating the influence of the atmosphere on the radiation of remote sensing images through an atmospheric correction model, using a ground radiation correction field and a radiation calibration coefficient to adjust the radiation measurements between different images to be consistent; performing surface reflectivity correction, multi-temporal data correction, and inter-sensor correction to eliminate the brightness and color differences between images acquired by different sensors.

[0009] Optionally, performing a spatial data processing operation on the preprocessed data includes: Feature extraction, target recognition, change detection, object classification, terrain analysis, and spatial relationship analysis; The feature extraction includes: extracting feature information of ground objects from preprocessed data; The target recognition includes: identifying and extracting the ground object of interest by analyzing the extracted feature information; The change detection includes: comparing data acquired at different times to detect changes in the ground surface; The ground object classification includes: classifying pixels or objects in the data according to ground object categories; The terrain analysis includes: using digital elevation model (DEM) and other data to perform terrain analysis and extract terrain features such as slope, elevation, and terrain undulation; The spatial relationship analysis includes: analyzing the spatial relationship between objects in the data.

[0010] Optionally, the step of integrating the processed geographic information data with other spatial data for comprehensive analysis and mining includes: Spatial data integration, spatial data connection, spatial data overlay, spatial data mining and spatial statistical analysis; The spatial data integration includes: integrating spatial data from different sources and in different formats through data format conversion, coordinate system conversion, and data matching to construct a unified spatial data set; The spatial data connection includes: connecting geographic information in different data sets to establish associations between them; The spatial data superposition includes: superimposing and analyzing the geographic information in different data sets; The spatial data mining includes: using data mining technology to analyze the integrated spatial data to discover the patterns, laws and trends hidden in the data; The spatial statistical analysis includes: using spatial regression analysis to perform statistical analysis on the integrated spatial data.

[0011] Optionally, the step of outputting the analyzed and processed high-precision spatial data as a map or a model includes: Map output, 3D model output and visualization output; The map output includes: constructing static maps, interactive maps, and thematic maps; using geographic information system (GIS) software to output the analyzed data into static maps, including point, line, and surface elements, and related attribute information; using WebGIS technology to make data into interactive maps, so that users can interact through the map interface to view information and data at different levels; making thematic maps based on the different contents and purposes of the analysis results to highlight specific geographic information; The three-dimensional model output includes: generating a digital surface model and a digital terrain model through elevation data, generating a three-dimensional building model based on the building information in the spatial data to display the appearance and structure of the building, and generating a terrain model using terrain data and landform features to display the three-dimensional shape and landform features of the terrain; The visualization output includes: using data visualization technology to intuitively display the analysis results in the form of charts, graphs, etc. and using dynamic simulation technology to display the change process and trend of spatial data.

[0012] Optionally, the geographic feature data includes: Spatial data, attribute data, image data, geographic indication data and geographic network data.

[0013] A high-precision spatial data acquisition and processing device for geographic information, comprising: Image data acquisition module: used to acquire high-resolution images of the target area; Data preprocessing module: preprocess the collected image data; Spatial data processing module: performs spatial data processing operations on preprocessed data; Data integration and analysis module: Integrate the processed geographic information data with other spatial data for comprehensive analysis and mining; Result output and application module: used to output the high-precision spatial data after analysis and processing into maps and models.

[0014] According to another aspect of the embodiment of the present application, a computer-readable storage medium is also provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned method for collecting and processing high-precision spatial data of geographic information when running.

[0015] According to another aspect of an embodiment of the present application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-mentioned method for collecting and processing high-precision spatial data of geographic information through the computer program.

[0016] In the embodiment of the present application, by adopting advanced remote sensing technology and spatial data processing algorithms, high-precision collection and processing of geographic information data is achieved, providing more accurate and reliable data support for applications in the field of geographic information, and using advanced remote sensing technology, such as high-resolution images, laser radar data, etc., to achieve high-precision collection of geographic information data. This can provide more detailed and accurate geographic information data, provide a more reliable basis for subsequent analysis and application, and adopt advanced spatial data processing algorithms, such as feature extraction, target recognition, change detection and other algorithms, to perform fine processing and analysis on the collected data. This helps to extract useful information from massive data and reveal the potential laws and associations of the data. Through high-precision collection and fine processing, the method can provide more accurate and reliable geographic information data. This is of great significance for applications in the field of geographic information, such as urban planning, resource management, environmental monitoring, etc., and can support decision-making and planning design. This method can be widely used in various fields, such as land use planning, natural resource management, disaster monitoring and early warning, etc. By providing accurate and reliable geographic information data support, better decision-making basis and support can be provided for work in these fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 is a flow chart of a method for collecting and processing high-precision spatial data of geographic information according to an embodiment of the present application; Figure 2 It is a structural block diagram of a device for collecting and processing high-precision spatial data of geographic information according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0021] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal, a device terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 1 The flowchart of a method for collecting and processing high-precision spatial data of geographic information according to an embodiment of the present application is as follows. According to an embodiment of the present application, a method for collecting and processing high-precision spatial data of geographic information includes: 101. Acquire high-resolution images of the target area; 102. Preprocess the collected image data; 103. Perform spatial data processing operations on the preprocessed data; 104. Integrate the processed geographic information data with other spatial data for comprehensive analysis and mining; 105. Output the analyzed and processed high-precision spatial data as maps and models.

[0023] Optionally, acquiring a high-resolution image of the target area includes: For data acquisition in large areas and large-scale geographic information application scenarios, high-resolution remote sensing satellites are used to acquire data; For small-scale and local area application scenarios, drones equipped with high-resolution cameras or sensors are used to obtain data.

[0024] Optionally, the preprocessing of the collected image data includes: Noise removal, image registration, image fusion, radiation correction; The noise removal includes: smoothing the image using a filter, identifying and eliminating outliers using a statistical method; Use filters for smoothing, smooth the image by calculating the average value of the neighborhood pixels around the pixel, reduce the noise in the image, use statistical methods to identify and eliminate outliers, and identify data points that exceed the threshold as outliers by calculating the mean and standard deviation of the data, and process them.

[0025] The image registration includes: aligning images acquired from different sensors or at different times in the same coordinate system through feature point matching and geometric transformation; First, unique and stable feature points, such as corner points and edge points, are extracted from each image; Describe the extracted feature points, usually using descriptors (such as SIFT, SURF, ORB, etc.) to represent the local features of the feature points; By comparing the feature point descriptors in different images, the best matching feature pair is found and the correspondence between the two images is established; Estimate the geometric transformation model between two images, such as affine transformation, perspective transformation, etc., through the random sampling consensus algorithm (RANSAC); Based on the matched feature point pairs, the geometric transformation matrix between the images is estimated, and one image is mapped to the coordinate system of another image; When resampling images, methods such as bilinear interpolation are used to maintain image quality and geometry.

[0026] The image fusion includes: fusing images from different bands into a multispectral image through multi-band fusion, and superimposing multiple bands or multiple images into one image; The principal component analysis (PCA) is used to perform linear transformation on the image data of different bands to obtain a new principal component image; the principal component image retains the most important information in the original image, can reduce redundant information, and improve the quality and information content of the image. The pixel-by-pixel maximum method is used to select the maximum gray value in multiple bands for each pixel as the fused pixel value.

[0027] Wavelet transform can be used to decompose images into wavelet coefficients of different scales and directions. By fusing the wavelet coefficients, multi-band image fusion can be achieved.

[0028] Through multi-band fusion technology, images from different bands or different sensors can be fused into a multispectral image, improving the information content and quality of the image and providing more data support for subsequent remote sensing image analysis and geographic information extraction.

[0029] The radiation correction includes: eliminating the influence of the atmosphere on the radiation of remote sensing images through an atmospheric correction model, using a ground radiation correction field and a radiation calibration coefficient to adjust the radiation measurements between different images to be consistent; performing surface reflectivity correction, multi-temporal data correction, and inter-sensor correction to eliminate the brightness and color differences between images acquired by different sensors.

[0030] Optionally, performing a spatial data processing operation on the preprocessed data includes: Feature extraction, target recognition, change detection, object classification, terrain analysis, and spatial relationship analysis; The feature extraction includes: extracting feature information of ground objects from preprocessed data; The target recognition includes: identifying and extracting the ground object of interest by analyzing the extracted feature information; The change detection includes: comparing data acquired at different times to detect changes in the ground surface; The ground object classification includes: classifying pixels or objects in the data according to ground object categories; The terrain analysis includes: using digital elevation model (DEM) and other data to perform terrain analysis and extract terrain features such as slope, elevation, and terrain undulation; Slope is one of the important terrain characteristics that describes the degree of surface inclination. It is usually calculated through elevation data and the slope value of each pixel is calculated using the central difference method or the neighborhood difference method.

[0031] Elevation is the vertical distance of the ground surface relative to a reference level surface and can be directly obtained from a digital elevation model.

[0032] Topographic relief describes the degree of ups and downs of the earth's surface. It is expressed as the difference in elevation between the highest and lowest points in an area. Topographic relief can help identify terrain features, landform types, and terrain morphology.

[0033] By drawing a topographic profile, we can intuitively display the elevation changes of the earth's surface, revealing the terrain characteristics and terrain overview; use elevation data to calculate various terrain parameters, such as slope distribution, elevation accumulation curve, terrain humidity index, etc., to describe the terrain characteristics and terrain morphology.

[0034] The spatial relationship analysis includes: analyzing the spatial relationship between objects in the data.

[0035] The spatial relationship between the objects in the analysis data includes: calculating the distance between the objects, including Euclidean distance, Manhattan distance, shortest path distance, etc., to determine the spatial proximity between the objects, drawing buffer zones around the objects, analyzing the spatial coverage, overlap or mutual influence between the objects, detecting the spatial correlation of the distribution of objects through spatial autocorrelation indicators (such as Moran's I), revealing the spatial agglomeration or dispersion rules between the objects, analyzing the spatial correlation between the objects, exploring the spatial influence and relationship between the objects, and inferring the distribution and change of the objects in space based on the spatial relationship between the objects by using the Kriging interpolation method; identifying the spatial clustering pattern of the objects, revealing the characteristics of the aggregation, discrete or random distribution of the objects; identifying the hot spots and cold spots of the objects, revealing the spatial clustering pattern of the distribution of the objects; using clustering algorithms (such as K-means clustering, DBSCAN, etc.) to identify the spatial clustering pattern of the objects.

[0036] Optionally, the step of integrating the processed geographic information data with other spatial data for comprehensive analysis and mining includes: Spatial data integration, spatial data connection, spatial data overlay, spatial data mining and spatial statistical analysis; The spatial data integration includes: integrating spatial data from different sources and in different formats through data format conversion, coordinate system conversion, and data matching to construct a unified spatial data set; The spatial data connection includes: connecting geographic information in different data sets to establish associations between them; Use geographic coordinate information (latitude and longitude, projection coordinates, etc.) to spatially connect and associate geographic information in different data sets, and connect geographic features in different data sets based on spatial relationships (such as proximity, overlap, inclusion, etc.).

[0037] Use common attribute fields (such as ID, name, type, etc.) to connect and associate geographic information in different data sets, perform attribute matching and association operations, and establish a corresponding relationship between geographic information entities in different data sets.

[0038] Integrate the geographic information in different data sets into spatial data to generate a unified geographic information data set for comprehensive query and analysis, and use GIS software or spatial database system to perform spatial data integration and connection operations.

[0039] Geographic information in different data sets can be associated with geographic coding (such as address coding, place name coding, etc.) to achieve unified identification and connection of geographic information. Geocoding can help match and associate geographic information in different data sets to achieve information integration and query across data sets.

[0040] Use the spatial connection tools (such as spatial query, spatial relationship analysis, etc.) provided by GIS software to connect and associate geographic information in different data sets. Use spatial connection tools to quickly and accurately associate and integrate geographic information in different data sets. Connect and associate geographic information in different data sets to achieve the integration of geographic information. This cross-dataset geographic information connection and association is of great significance for applications such as comprehensive geographic information analysis and spatial decision support.

[0041] The spatial data superposition includes: superimposing and analyzing the geographic information in different data sets; The overlay analysis of geographic information in different data sets includes: Step 1: Ensure that the geographic information in different datasets uses the same geographic coordinate system and projection method to facilitate correct overlay analysis; ensure that the datasets contain key attribute fields for overlay analysis, such as ID, name, type, etc.

[0042] Step 2: Use GIS software (such as ArcGIS, QGIS, etc.) to load different datasets that need to be overlaid for analysis and ensure that the datasets are correctly displayed on the map.

[0043] Step 3: Select appropriate overlay analysis tools in GIS software, such as spatial overlay, spatial intersection, etc., to analyze the spatial relationship of geographic information in different data sets.

[0044] Step 4: Select appropriate overlay analysis methods according to specific needs, such as point overlay, line overlay, surface overlay, etc., for analysis, and perform overlay analysis based on spatial relationships, such as intersection, inclusion, adjacency, etc., to identify the spatial relationships between geographic elements.

[0045] Step 5: Based on the overlay analysis, the attribute information in different data sets can be merged and integrated to generate new attribute information and perform attribute merging.

[0046] The spatial data mining includes: using data mining technology to analyze the integrated spatial data to discover the patterns, laws and trends hidden in the data; The spatial statistical analysis includes: using spatial regression analysis to perform statistical analysis on the integrated spatial data.

[0047] Optionally, the step of outputting the analyzed and processed high-precision spatial data as a map or a model includes: Map output, 3D model output and visualization output; The map output includes: constructing static maps, interactive maps, and thematic maps; using geographic information system (GIS) software to output the analyzed data into static maps, including point, line, and surface elements, and related attribute information; using WebGIS technology to make data into interactive maps, so that users can interact through the map interface to view information and data at different levels; making thematic maps based on the different contents and purposes of the analysis results to highlight specific geographic information; The three-dimensional model output includes: generating a digital surface model and a digital terrain model through elevation data, generating a three-dimensional building model based on the building information in the spatial data to display the appearance and structure of the building, and generating a terrain model using terrain data and landform features to display the three-dimensional shape and landform features of the terrain; The visualization output includes: using data visualization technology to intuitively display the analysis results in the form of charts, graphs, etc. and using dynamic simulation technology to display the change process and trend of spatial data.

[0048] Optionally, the geographic feature data includes: Spatial data, attribute data, image data, geographic indication data and geographic network data.

[0049] According to another aspect of the embodiment of the present application, a computer-readable storage medium is also provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned method for collecting and processing high-precision spatial data of geographic information when running.

[0050] According to another aspect of an embodiment of the present application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-mentioned method for collecting and processing high-precision spatial data of geographic information through the computer program.

[0051] Figure 2 is a structural block diagram of a road feature prediction device according to an embodiment of the present application, such as Figure 2 As shown, including: Image data acquisition module 201: used to acquire high-resolution images of the target area; Data preprocessing module 202: preprocessing the collected image data; Spatial data processing module 203: performs spatial data processing operations on the pre-processed data; Data integration and analysis module 204: integrates the processed geographic information data with other spatial data for comprehensive analysis and mining; Result output and application module 205: used to output the high-precision spatial data after analysis and processing into maps and models.

[0052] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0053] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0054] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0055] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0056] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0057] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for collecting and processing high-precision spatial data of geographic information, characterized in that: include: Acquire high-resolution images of the target area; Preprocess the collected image data; Perform spatial data processing operations on the preprocessed data; Integrate the processed geographic information data with other spatial data for comprehensive analysis and mining; The analyzed and processed high-precision spatial data are output as maps and models.

2. The method according to claim 1, characterized in that The acquisition of high-resolution images of the target area includes: For data acquisition in large areas and large-scale geographic information application scenarios, high-resolution remote sensing satellites are used to acquire data; For small-scale and local area application scenarios, drones equipped with high-resolution cameras or sensors are used to obtain data.

3. The method according to claim 1, characterized in that The preprocessing of the collected image data includes: Noise removal, image registration, image fusion, radiation correction; The noise removal includes: smoothing the image using a filter, identifying and eliminating outliers using a statistical method; The image registration includes: aligning images acquired from different sensors or at different times in the same coordinate system through feature point matching and geometric transformation; The image fusion includes: fusing images from different bands into a multispectral image through multi-band fusion, and superimposing multiple bands or multiple images into one image; The radiation correction includes: eliminating the influence of the atmosphere on the radiation of remote sensing images through an atmospheric correction model, using a ground radiation correction field and a radiation calibration coefficient to adjust the radiation measurements between different images to be consistent; performing surface reflectivity correction, multi-temporal data correction, and inter-sensor correction to eliminate the brightness and color differences between images acquired by different sensors.

4. The method according to claim 1, characterized in that: The performing of spatial data processing operation on the pre-processed data includes: Feature extraction, target recognition, change detection, object classification, terrain analysis, and spatial relationship analysis; The feature extraction includes: extracting feature information of ground objects from preprocessed data; The target recognition includes: identifying and extracting the ground object of interest by analyzing the extracted feature information; The change detection includes: comparing data acquired at different times to detect changes in the ground surface; The ground object classification includes: classifying pixels or objects in the data according to ground object categories; The terrain analysis includes: using digital elevation model (DEM) and other data to perform terrain analysis and extract terrain features such as slope, elevation, and terrain undulation; The spatial relationship analysis includes: analyzing the spatial relationship between objects in the data.

5. The method according to claim 1, characterized in that The processing of geographic information data and other spatial data integration for comprehensive analysis and mining includes: Spatial data integration, spatial data connection, spatial data overlay, spatial data mining and spatial statistical analysis; The spatial data integration includes: integrating spatial data from different sources and in different formats through data format conversion, coordinate system conversion, and data matching to construct a unified spatial data set; The spatial data connection includes: connecting geographic information in different data sets to establish associations between them; The spatial data superposition includes: superimposing and analyzing the geographic information in different data sets; The spatial data mining includes: using data mining technology to analyze the integrated spatial data to discover the patterns, laws and trends hidden in the data; The spatial statistical analysis includes: using spatial regression analysis to perform statistical analysis on the integrated spatial data.

6. The method according to claim 1, characterized in that The method of outputting the analyzed and processed high-precision spatial data into a map or model includes: Map output, 3D model output and visualization output; The map output includes: constructing static maps, interactive maps, and thematic maps; using geographic information system (GIS) software to output the analyzed data into static maps, including point, line, and surface elements, and related attribute information; using WebGIS technology to make data into interactive maps, so that users can interact through the map interface to view information and data at different levels; making thematic maps based on the different contents and purposes of the analysis results to highlight specific geographic information; The three-dimensional model output includes: generating a digital surface model and a digital terrain model through elevation data, generating a three-dimensional building model based on the building information in the spatial data to display the appearance and structure of the building, and generating a terrain model using terrain data and landform features to display the three-dimensional shape and landform features of the terrain; The visualization output includes: using data visualization technology to intuitively display the analysis results in the form of charts, graphs, etc. and using dynamic simulation technology to display the change process and trend of spatial data.

7. The method according to claims 1-6, characterized in that: The geographical feature data include: Spatial data, attribute data, image data, geographic indication data and geographic network data.

8. A high-precision spatial data acquisition and processing device for geographic information, characterized in that: include: Image data acquisition module: used to acquire high-resolution images of the target area; Data preprocessing module: preprocess the collected image data; Spatial data processing module: performs spatial data processing operations on preprocessed data; Data integration and analysis module: Integrate the processed geographic information data with other spatial data for comprehensive analysis and mining; Result output and application module: used to output the high-precision spatial data after analysis and processing into maps and models.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 7 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 7 through the computer program.

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