A multi-scale geographic information fusion method for national land spatial surveying and mapping

Abstract

The present invention belongs to the field of industrial data processing, and in particular relates to a multi-scale geographic information fusion method for land space surveying and mapping. First, multi-source data collection is carried out, including satellite images, drone mapping and ground measurement; then the original data is pre-processed by format conversion, noise removal and integrity verification; then the data is classified according to the information source and scale characteristics, and a first fused data set is generated through timestamp synchronization and spatial registration, and then fused to form a second fused data set; by constructing a comprehensive scoring index to quantify the scoring from multiple dimensions, the data with a scoring result of medium is screened for secondary scoring; finally, the screened data is merged into the final fused data set. Compared with the existing technology, the present invention can fully integrate the advantages of multi-source data, solve the problem of data differences, provide comprehensive, accurate, multi-scale geographic information, and meet the needs of land space surveying and mapping and planning.

Description

Technical Field

[0001] The present invention belongs to the field of industrial data processing, and in particular relates to a multi-scale geographic information fusion method for land space surveying and mapping. Background Art

[0002] In national land surveying and mapping, obtaining accurate, comprehensive, and multi-scale geographic information is crucial. With the continuous advancement of surveying and mapping technology, the data sources available for national land surveying and mapping are becoming increasingly diverse, including satellite imagery, drone mapping data, and ground measurement data. However, these data have different characteristics and limitations. Furthermore, data from different sources differ in format, temporal scale, and spatial scale. Directly using these data will result in ineffective collaboration between them, making it difficult to meet the national land surveying and mapping requirements for comprehensive, accurate, and timely geographic information. In the national land planning process, it is necessary to consider both the macroscopic topography and the microscopic details of key areas. Failure to rationally integrate multi-source data will make it impossible to provide a scientific and accurate basis for planning decisions. Currently, there is a lack of an effective multi-scale geographic information fusion method that can fully integrate the advantages of various data types and address the problems caused by data discrepancies. This has seriously restricted the efficient implementation of national land surveying and mapping and the development of related fields. Summary of the Invention

[0003] In response to the technical problems existing in the above-mentioned background technologies, the present invention proposes a multi-scale geographic information fusion method for national land space surveying and mapping that is reasonably designed, theoretically sound and feasible.

[0004] In order to achieve the above object, the technical solution adopted by the present invention includes the following steps:

[0005] S1. Multi-source data collection: including satellite imagery, drone mapping, and ground measurement.

[0006] Satellite image acquisition: using satellite remote sensing technology to obtain large-area, macro-scale geographic information;

[0007] UAV mapping and collection: Use UAV low-altitude mapping to collect high-resolution, small-scale detailed data;

[0008] Ground measurement collection: Use ground measurement equipment to accurately measure a specific area;

[0009] S2. Data preprocessing: Perform format conversion, noise removal, and data integrity verification on the three types of raw data to obtain preprocessed data;

[0010] S3. Classify the preprocessed data according to information source and scale characteristics, synchronize timestamps and perform spatial registration on similar data, and generate a first fused dataset, wherein the preliminary fused dataset includes a large-scale topographic and geomorphic dataset, a medium-scale key area dataset, and a small-scale specific location dataset;

[0011] S4, fusing datasets of different scales on the first fused dataset to form a second fused dataset;

[0012] S5. Based on different demand characteristics, a comprehensive scoring index is constructed to quantitatively score the second fused dataset based on the detail level of geographical elements, timeliness of data, and degree of data dispersion. The scoring results are divided into high, medium, and low.

[0013] S6. Data with high scores are recorded as the third fused dataset and stored. Data with low scores are directly removed. Data with medium scores are re-evaluated using the secondary comprehensive scoring index. If the scores are high, they are recorded as the fourth fused dataset and stored. Otherwise, they are removed.

[0014] S7. Finally, the third fused dataset and the fourth fused dataset are combined into a fifth fused dataset as the final fused dataset.

[0015] Preferably, the timestamp synchronization calculation formula for the same type of data before generating the first fused data set in S3 is as follows:

[0016] The synchronization cost function of timestamp synchronization is Where i and j represent two sampling moments respectively, d(x i ,y j ) is the Euclidean distance metric, and the condition needs to satisfy the elastic window restriction of |ij|≤δ, where δ is the maximum time shift tolerance threshold;

[0017] Preferably, the objective function for spatial registration of similar data before generating the first fused data set in S3 is: where w d is the adaptive weight, R is the orthogonal matrix, in are the curvature eigenvalues ​​of the source point cloud and the target point cloud at point d, σ c is the normalization parameter.

[0018] Preferably, the second fused data set in step S4 uses the large-scale terrain data in the first fused data set as the basic framework, the medium-scale data is embedded in the framework through feature point matching, the small-scale data is mounted to the corresponding parent node according to the spatial coordinates to establish a scale mapping rule, and the fusion boundary is detected, and local deformation compensation is performed for areas with sudden changes in terrain. Finally, based on the results of change detection, an update mechanism is triggered to re-perform the fusion operation on areas with elevation changes >0.5m.

[0019] Preferably, the comprehensive scoring index in step S5 is calculated as follows: Where α, β, γ∈[0,3] and satisfy α+β+γ=3;

[0020] in is the detail level of geographic features, N feature is the number of feature points in the second fusion data set, A is the area of ​​the region, ρ ref is the reference density threshold, when Time

[0021] where e -λ·Δt is the data timeliness coefficient, Δt is the difference between the data acquisition time and the current time, and λ is the aging attenuation factor;

[0022] in is the dispersion index of the data, σ spatial is the standard deviation of the spatial distribution of geographic elements, μ spatial is the spatial distribution mean.

[0023] Preferably, the results calculated based on the comprehensive scoring index are divided into three categories, high: Score ≥ 0.8, medium: 0.5 ≤ Score < 0.8, low: Score < 0.5; and the classification results are confidence verified, and if the classification confidence is less than 90%, the manual review process is triggered.

[0024] Preferably, the secondary comprehensive scoring index in step S6 is calculated based on the first scoring index calculation to compensate for the timeliness coefficient and the dispersion index;

[0025] Among them, let e -λ·Δt =T, then the compensation calculation method is: Where η is the historical data compensation coefficient, T hist,k is the historical timeliness value of the previous k periods, ω k is the exponential decay weight;

[0026] Among them, Then the dispersion index is calculated as: Among them Cterrain is the terrain complexity index, C ref is the reference complexity.

[0027] Preferably, the data with a medium result in the first comprehensive score is judged again using the secondary comprehensive score indicator according to the secondary comprehensive score. If the result is high, it is recorded as the fourth fused data set for storage.

[0028] Compared with the existing technology, the advantages and positive effects of the present invention are that, in the data collection link, through the collaborative collection of satellite images, UAV mapping and ground measurement, full coverage of macro, meso and micro geographic information is achieved to meet the needs of different scales. During data preprocessing, format conversion, noise removal and integrity verification ensure data quality. In data fusion, after classification, timestamp synchronization and spatial registration are performed to generate the first fused data set, and then the different scale data sets are fused to establish scale mapping rules and compensate for terrain mutation areas to ensure data consistency and accuracy. A comprehensive scoring index and a secondary scoring mechanism are constructed to quantitatively score and screen data from multiple dimensions to improve data quality. These technical points work together to overcome the data fusion difficulties of traditional technologies, provide comprehensive, accurate, multi-scale and timely geographic information, effectively meet the needs of national land space mapping and planning, and promote the development of related fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0030] Figure 1 A flowchart of a multi-scale geographic information fusion method for land space surveying and mapping provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described below in conjunction with the accompanying drawings and embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0032] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways than those described herein. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0033] Examples, such as Figure 1This is a flowchart provided by an embodiment of the present invention; taking a new urban area planning project in a large city as an example, the area contains a variety of landforms, including mountains, rivers, urban built-up areas and areas to be developed, and the planning involves macro layout and micro design, which places extremely high requirements on the accuracy and scale of geographic information.

[0034] First, multi-source data collection is carried out. Using satellite remote sensing technology, satellites with appropriate resolution are selected to obtain large-scale, macro-scale image data of the area, covering the entire new urban area and surrounding related areas, and obtaining macro information such as topography, landforms, and water system distribution. At the same time, multiple drones are deployed for low-altitude mapping, collecting high-resolution, small-scale detailed data for key areas such as core business districts and ecological protection areas, including the precise outlines of buildings and detailed information on vegetation cover. In addition, with the help of ground measurement equipment such as total stations and GPS receivers, precise measurements are carried out on specific areas such as geologically complex mountainous areas and planned transportation hubs to obtain high-precision terrain data and coordinate information.

[0035] Next, preprocess the dataset. Noise in the data can interfere with analysis results. Satellite imagery, influenced by the sensor and the atmosphere, produces speckle and stripe noise; drone mapping produces blur and pixel anomalies due to flight environment issues; and ground measurements, influenced by instrument accuracy and environmental interference, produce errors. Based on the characteristics of the noise, select an appropriate filtering algorithm to remove the noise. For example, when dealing with speckle noise in satellite images, Gaussian filtering can preserve edges while smoothing the image; median filtering can effectively suppress salt and pepper noise in drone images. Furthermore, data collection is prone to missing values. Cloud obscuration of satellite images, obstructions encountered in drone mapping, and malfunctions of ground measurement equipment can all lead to missing data. Data integrity checks are used to locate missing values, which are then supplemented using interpolation. For example, in terrain data processing, inverse distance weighted interpolation calculates missing values ​​based on the distance to surrounding points; kriging interpolation combines spatial autocorrelation and surrounding point information to accurately estimate missing values ​​and ensure data integrity.

[0036] Next, to achieve data classification and preliminary fusion, the preprocessed data is classified according to information source and scale characteristics. Timestamp synchronization and spatial registration are performed on similar data to generate a first fused dataset. The preliminary fused dataset includes a large-scale topographic dataset, a medium-scale key area dataset, and a small-scale specific location dataset. The objective function for spatial registration of similar data before generating the first fused dataset is: where w d is the adaptive weight, R is the orthogonal matrix, in are the curvature eigenvalues ​​of the source point cloud and the target point cloud at point d, σ c is the normalization parameter.

[0037] The first fused dataset is then fused with datasets of different scales to form a second fused dataset. When generating this second fused dataset, the large-scale terrain data from the first fused dataset serves as the foundation for the entire fusion framework. Large-scale terrain data covers large areas of mountains, rivers, plains, and other information, providing macro-context and spatial layout reference for the other data. Next, the mesoscale data is embedded into the framework using feature point matching. The mesoscale data focuses on key areas, such as the layout of urban built-up areas and the locations of large facilities. By extracting feature points from the mesoscale and large-scale data, such as building vertices and road intersections, and calculating their relative positional relationships, precise embedding is achieved, closely integrating the details of key areas with the macro-topography. Small-scale data is mounted to corresponding parent nodes according to spatial coordinates, and scale mapping rules are established. Small-scale data, such as 3D models of specific buildings and site topography data, are highly accurate. Mounting by coordinates allows for precise positioning within the macro- and meso-context, enabling seamless integration of data at different scales. Next, the fusion boundary is detected. An algorithm compares the differences in data from adjacent areas to identify the fusion boundary. For areas with abrupt topographic changes, such as the transition zone between mountainous areas and plains, local deformation compensation is used to adjust the terrain data based on the surrounding terrain trends, ensuring a smooth transition. Finally, an update mechanism is triggered based on change detection. By comparing fused data from different periods, areas of geographic feature change are identified. If an elevation change of more than 0.5m is detected, indicating significant topographical change, the area is re-fused. Combined with the latest multi-source data, the data is reprocessed according to the fusion process to ensure that the fused dataset reflects changes in the geographic environment in real time and provides accurate information for related applications.

[0038] Then, considering the detail of geographical elements, the timeliness of data, and the discreteness of data, the second fused dataset is quantitatively scored, and the scoring results are divided into high, medium, and low. The specific implementation is: the calculation of the comprehensive scoring index is: Where α, β, γ∈[0,3] and satisfy α+β+γ=3; is the detail level of geographic features, N feature is the number of feature points in the second fusion data set, A is the area of ​​the region, ρ ref is the reference density threshold, when Time where e -λ·Δt is the data timeliness coefficient, Δt is the difference between the data acquisition time and the current time, and λ is the aging attenuation factor; is the dispersion index of the data, σ spatial is the standard deviation of the spatial distribution of geographic elements, μ spatialis the mean of the spatial distribution. The results of the calculation of the comprehensive scoring index are divided into three categories: high: Score ≥ 0.8, medium: 0.5 ≤ Score < 0.8, and low: Score < 0.5. The classification results are then verified for confidence. If the classification confidence is less than 90%, a manual review process is triggered. Professionals will first conduct a comprehensive review of the data collection process to confirm whether the satellite imagery, drone mapping, and ground measurement operations are standardized and whether there are any equipment anomalies. Next, the data preprocessing process is checked to see if the format conversion is correct, whether the noise removal is thorough, and whether the missing value supplementation is reasonable. The fusion process, including timestamp synchronization, spatial registration, and scale fusion, is then carefully checked for deviations.

[0039] In addition, for data with high scores, it is recorded as the third fusion data set for storage, and for data with low scores, it is directly removed. For data with medium scores, the secondary comprehensive scoring index is used to judge again. If the score is high, it is recorded as the fourth fusion data set for storage, otherwise it is removed. The specific implementation is that the secondary comprehensive scoring index is based on the calculation of the first scoring index to compensate for the timeliness coefficient and dispersion index; where, let e -λ·Δt =T, then the compensation calculation method is: Where η is the historical data compensation coefficient, T hist,k is the historical timeliness value of the previous k periods, ω k is the exponential decay weight; where, let Then the dispersion index is calculated as: Among them C terrain is the terrain complexity index, C ref The second comprehensive scoring is used to evaluate the data with a medium score in the first comprehensive scoring. If the result is high, it is recorded as the fourth fused dataset and stored. Finally, the third and fourth fused datasets are combined to form the fifth fused dataset, which is the final fused dataset.

[0040] The above description is merely a preferred embodiment of the present invention and does not constitute any other form of limitation to the present invention. Any person skilled in the art may utilize the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes for application in other fields. However, any simple modification, equivalent change, and modification of the above embodiments made in accordance with the technical essence of the present invention without departing from the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A multi-scale geographic information fusion method for national land space surveying and mapping, characterized in that: The following steps are involved: S1. Multi-source data collection: including satellite imagery, drone mapping, and ground measurement. Satellite image acquisition: using satellite remote sensing technology to obtain large-area, macro-scale geographic information; UAV mapping and collection: Use UAV low-altitude mapping to collect high-resolution, small-scale detailed data; Ground measurement collection: Use ground measurement equipment to accurately measure a specific area; S2. Data preprocessing: Perform format conversion, noise removal, and data integrity verification on the three types of raw data to obtain preprocessed data; S3. Classify the preprocessed data according to information source and scale characteristics, synchronize timestamps and perform spatial registration on similar data, and generate a first fused dataset, where the first fused dataset includes a large-scale topographic and geomorphic dataset, a medium-scale key area dataset, and a small-scale specific location dataset; S4, fusing datasets of different scales on the first fused dataset to form a second fused dataset; S5. Based on different demand characteristics, a comprehensive scoring index is constructed to quantitatively score the second fused dataset based on the detail level of geographical elements, timeliness of data, and degree of data dispersion. The scoring results are divided into high, medium, and low. S6. Data with high scores are recorded as the third fused dataset and stored. Data with low scores are directly removed. Data with medium scores are re-evaluated using the secondary comprehensive scoring index. If the scores are high, they are recorded as the fourth fused dataset and stored. Otherwise, they are removed. S7, finally combining the third fused dataset and the fourth fused dataset into a fifth fused dataset as the final fused dataset; The calculation of the comprehensive scoring index in step S5 is: Where α, β, γ∈[0,3] and satisfy α+β+γ=3; in is the detail level of geographic features, N feature is the number of feature points in the second fusion data set, A is the area of ​​the region, ρ ref is the reference density threshold, when Time where e -λ·Δt is the data timeliness coefficient, Δt is the difference between the data acquisition time and the current time, and λ is the aging attenuation factor; in is the dispersion index of the data, σ spatial is the standard deviation of the spatial distribution of geographic elements, μ spatial is the spatial distribution mean.

2. The multi-scale geographic information fusion method for land space surveying and mapping according to claim 1, characterized in that: The calculation formula for synchronizing the timestamps of similar data before generating the first fused data set in S3 is as follows: The synchronization cost function of timestamp synchronization is Where i and j represent two sampling moments respectively, d(x i ,y j ) is the Euclidean distance metric, and the condition needs to satisfy the elastic window restriction of |ij|≤δ, where δ is the maximum time shift tolerance threshold.

3. The multi-scale geographic information fusion method for land space surveying and mapping according to claim 1, characterized in that: The objective function for spatial registration of similar data before generating the first fused data set in S3 is: where w d is the adaptive weight, R is the orthogonal matrix, in are the curvature eigenvalues ​​of the source point cloud and the target point cloud at point d, σ c is the normalization parameter.

4. The multi-scale geographic information fusion method for land space surveying and mapping according to claim 1, characterized in that: In step S4, the second fused dataset uses the large-scale terrain data in the first fused dataset as the basic framework, embeds the medium-scale data into the framework through feature point matching, and mounts the small-scale data to the corresponding parent node according to spatial coordinates to establish a scale mapping rule. The fusion boundary is detected, and local deformation compensation is performed for areas with sudden terrain changes. Finally, based on the results of change detection, an update mechanism is triggered to re-fuse areas with elevation changes greater than 0.5m.

5. The multi-scale geographic information fusion method for land space surveying and mapping according to claim 1, characterized in that: The results calculated based on the comprehensive scoring index are divided into three categories, including high: Score ≥ 0.8, medium: 0.5 ≤ Score < 0.8, and low: Score < 0.5; and the classification results are confidence verified. If the classification confidence is less than 90%, the manual review process is triggered.

6. The multi-scale geographic information fusion method for land space surveying and mapping according to claim 1, characterized in that: The secondary comprehensive scoring index in step S6 is calculated based on the first scoring index to compensate for the timeliness coefficient and the dispersion index; Among them, let e -λ·Δt =T, then the compensation calculation method is: Where η is the historical data compensation coefficient, T hist,k is the historical timeliness value of the previous k periods, ω k is the exponential decay weight; Among them, Then the dispersion index is calculated as: Among them C terrain is the terrain complexity index, C ref is the reference complexity.

7. The multi-scale geographic information fusion method for land space surveying and mapping according to claim 6, characterized in that: According to the second comprehensive score, the data with the medium result of the first comprehensive score is judged again using the second comprehensive score indicator. If the result is high, it is recorded as the fourth fusion data set for storage.

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