Multi-scale geographic information fusion method for territorial space surveying and mapping

Abstract

The invention belongs to the field of industrial data processing, and particularly relates to a multi-scale geographic information fusion method for territorial space surveying and mapping. Firstly, multi-source data acquisition including satellite image, unmanned aerial vehicle surveying and mapping and ground measurement is carried out; performing format conversion, noise removal and integrity verification preprocessing on the original data; according to the information source and the scale feature classification data, generating a first fusion data set through timestamp synchronization and spatial registration, and fusing to form a second fusion data set; performing multi-dimensional quantitative scoring by constructing a comprehensive scoring index, and performing secondary scoring screening on data in a scoring result; and finally, combining the screened data into a final fusion data set. Compared with the prior art, the advantages of multi-source data can be fully integrated, the problem of data difference is solved, comprehensive, accurate and multi-scale geographic information is provided, and the territorial space surveying and mapping and planning requirements are met.

Description

Technical Field

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

[0002] In the work of national land space mapping, it is crucial to obtain accurate, comprehensive and multi-scale geographic information. With the continuous development of mapping technologies, the current data sources available for national land space mapping are becoming increasingly rich, including satellite images, unmanned aerial vehicle (UAV) mapping data, and ground measurement data, etc. However, these data have different characteristics and limitations. In addition, the data from different sources vary in formats, time scales, space scales, etc. If these data are directly used, it will result in ineffective coordination between the data and it is difficult to meet the requirements of national land space mapping for the comprehensiveness, accuracy and timeliness of geographic information. In the process of national land space planning, it is necessary to consider both the macroscopic topographic features and the microscopic details of key areas simultaneously. If multi-source data cannot be reasonably fused, it will be 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 types of data and solve the problems brought about by data differences, which seriously restricts the efficient development of national land space mapping work and the development of related fields. Summary of the Invention

[0003] In view of the technical problems existing in the above background art, the present invention proposes a multi-scale geographic information fusion method for national land space mapping that is reasonably designed, highly theoretical and achievable.

[0004] To achieve the above object, the technical solution adopted by the present invention is as follows, including the following steps:

[0005] S1. Multi-source data acquisition: including satellite image acquisition, UAV mapping acquisition and ground measurement acquisition;

[0006] Satellite image acquisition: Using satellite remote sensing technology to obtain geographic information on a large area and macroscopic scale;

[0007] UAV mapping acquisition: Collecting high-resolution and small-scale detail data through low-altitude UAV mapping;

[0008] Ground measurement acquisition: Accurately measuring a specific area with the aid of ground measurement equipment;

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

[0010] S3. Classify the preprocessed data according to the information source and scale characteristics, synchronize the timestamps and register the spaces of the same type of data to generate a first fusion dataset, where the preliminary fusion dataset includes a large-scale topographic dataset, a medium-scale key area dataset, and a small-scale specific location dataset;

[0011] S4. Perform fusion of datasets at different scales on the first fusion dataset to form a second fusion dataset;

[0012] S5. Based on different requirement characteristics, construct a comprehensive scoring index, and quantitatively score the second fusion dataset from the aspects of the detail level of geographical elements, the timeliness of data, and the dispersion degree of data. The scoring results are divided into high, medium, and low;

[0013] S6. For the data with a high scoring result, record it as the third fusion dataset for storage. For the data with a low scoring result, directly perform a removal operation. For the data with a medium scoring result, use a secondary comprehensive scoring index to judge again. If the scoring result is high, record it as the fourth fusion dataset for storage, otherwise remove it;

[0014] S7. Finally, combine the third fusion dataset and the fourth fusion dataset to merge into a fifth fusion dataset as the final fusion dataset.

[0015] Preferably, the calculation formula for synchronizing the timestamps of the same type of data before generating the first fusion dataset in S3 is as follows:

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

[0017] Preferably, the objective function for registering the spaces of the same type of data before generating the first fusion dataset in S3 is: where w d is the adaptive weight, R is the orthogonal matrix, where are the curvature eigenvalue at d of the source point cloud and the target point cloud respectively, and σ c is the normalization parameter.

[0018] Preferably, in the step S4, the second fusion data set uses the large-scale terrain data in the first fusion data set as the basic framework, embeds the mesoscale data into the framework through feature point matching, mounts the small-scale data to the corresponding parent nodes according to the spatial coordinates to establish a scale mapping rule, and detects the fusion boundary. For areas with terrain mutations, local deformation compensation is performed. Finally, based on the results of change detection, the update mechanism is triggered, and the fusion operation is re-performed on areas with elevation changes > 0.5m.

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

[0020] where is the geographical feature detail degree, 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 take

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

[0022] where is the data dispersion index, σ spatial is the standard deviation of the spatial distribution of geographical features, μ spatial is the spatial distribution mean.

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

[0024] Preferably, in the step S6, the secondary comprehensive scoring index compensates for the timeliness coefficient and the dispersion index on the basis of the first scoring index calculation;

[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, let then the calculation method of the dispersion index is: where C terrain is the terrain complexity index, Cref is the reference complexity.

[0027] Preferably, for the data with the result of medium in the first comprehensive score according to the secondary comprehensive score, the secondary comprehensive scoring index is used again for judgment. If the result is high, it is recorded as the fourth fusion data set for storage.

[0028] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the data acquisition link, through the collaborative acquisition of satellite images, unmanned aerial vehicle mapping and ground measurement, the macro, meso and micro geographical information is fully covered to meet the requirements 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 fusion data set, and then through the fusion of data sets at different scales, scale mapping rules are established and topographic mutation areas are compensated 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. The cooperation of these technical points overcomes the data fusion problems of traditional technologies, provides comprehensive, accurate, multi-scale and highly time-sensitive geographical information, effectively meets the needs of national territorial space mapping and planning, and promotes 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 will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0030] Figure 1 It is a flowchart of a multi-scale geographical information fusion method for national territorial space mapping provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] In order to better understand the above objects, features and advantages of the present invention, the following further describes the present invention with reference to the drawings and embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0032] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the present invention is not limited by the specific embodiments disclosed in the following specification.

[0033] Embodiment, such as Figure 1It is a flowchart provided by an embodiment of the present invention. Taking the new urban area planning project of a large city as an example, this area contains various topographies and landforms, including mountains, rivers, urban built-up areas, and undeveloped areas. Moreover, the planning involves macroscopic layout and microscopic design, with extremely high requirements for the accuracy and scale of geographic information.

[0034] First, multi-source data collection is carried out. Using satellite remote sensing technology, select satellites with appropriate resolutions to obtain large-area and macroscopic-scale image data of this area, covering the entire new urban area and its surrounding related areas, and obtain macroscopic information such as topographies and landforms, water system distributions, etc. At the same time, arrange multiple drones for low-altitude mapping, and collect high-resolution and small-scale detailed data for key areas such as the core business district and ecological protection areas, including the precise outlines of buildings, detailed vegetation coverage, etc. In addition, with the help of ground measurement equipment such as total stations and GPS receivers, accurately measure specific areas such as mountains with complex geology and planned transportation hub sites to obtain high-precision topographic data and coordinate information.

[0035] Then, preprocessing operations are performed on the data set. The noise in the data will interfere with the analysis results. Satellite images are affected by sensors and the atmosphere, resulting in speckle and stripe noise; UAV mapping has blurring and pixel anomalies due to flight environment problems; ground measurements have errors due to instrument accuracy and environmental interference. According to the noise characteristics, select appropriate filtering algorithms to remove the noise. For example, when dealing with the speckle noise of satellite images, Gaussian filtering can retain edges while smoothing the image; median filtering can effectively suppress the salt-and-pepper noise of UAV images. In addition, missing values are likely to occur in data collection. Satellite images being blocked by clouds, UAV mapping encountering obstacles, and ground measurement equipment failures will all lead to data missing. Locate the missing values through data integrity verification, and then use the interpolation method to supplement them. For example, in terrain data processing, inverse distance weighted interpolation calculates the missing values based on the distances of surrounding points; Kriging interpolation combines spatial autocorrelation and the information of surrounding points to accurately estimate the missing values and ensure data integrity.

[0036] Next, in order to implement the data classification and preliminary fusion link, classify the preprocessed data according to the information source and scale characteristics. Synchronize the timestamps and perform spatial registration on the same type of data to generate the first fusion data set. The preliminary fusion data set includes a large-scale topographic data set, a medium-scale key area data set, and a small-scale specific location data set. The objective function for performing spatial registration on the same type of data before generating the first fusion data set is: where w d is the adaptive weight, R is the orthogonal matrix, where are the curvature eigenvalue of the source point cloud and the target point cloud at d respectively, and σ c is the normalization parameter.

[0037] Then, the first fused dataset is fused with datasets of different scales to form the second fused dataset. When generating the second fused dataset, the large-scale terrain data in the first fused dataset is first used as the basis for the entire fusion framework. The large-scale terrain data covers information such as large areas of mountains, rivers, plains, etc., providing a macroscopic background and a spatial layout reference for other data. Next, the medium-scale data is embedded into the framework by feature point matching. The medium-scale data focuses on key areas, such as the layout of urban built-up areas and the locations of large facilities. By extracting the feature points of the medium-scale and large-scale data, such as building vertices and road intersections, and calculating their relative positional relationships, precise embedding is achieved, enabling the details of key areas to be closely combined with the macroscopic terrain. The small-scale data is mounted to the corresponding parent nodes according to spatial coordinates, and a scale mapping rule is established. The small-scale data has high precision, such as the 3D models of specific buildings and site terrain data, etc. Mounting according to coordinates can accurately locate it in the macroscopic and mesoscopic backgrounds, realizing seamless docking of data at different scales. After that, the fusion boundary is detected. An algorithm is used to compare the data differences in adjacent regions to identify the fusion boundary. For areas with terrain mutations, such as the transition zone between mountains and plains, local deformation compensation is adopted, and the terrain data is adjusted according to the surrounding terrain trend to make the terrain transition smoothly. Finally, an update mechanism is triggered based on change detection. By comparing the fused data at different times, the areas where geographical elements have changed are identified. When an area with an elevation change greater than 0.5 m is detected, it indicates a significant terrain change, and this area will be refused. Combining the latest multi-source data, it is processed again according to the fusion process to ensure that the fused dataset can reflect the changes in the geographical environment in real time and provide accurate information for relevant applications.

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

[0039] In addition, for the data with a high scoring result, it is recorded and stored as the third fusion dataset. For the data with a low scoring result, direct removal operations are performed. For the data with a medium scoring result, the secondary comprehensive scoring index is used to make a judgment again. If the scoring result is high, it is recorded and stored as the fourth fusion dataset, otherwise it is removed. The specific implementation is that the secondary comprehensive scoring index compensates for the timeliness coefficient and the dispersion index on the basis of the first scoring index calculation; 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, and ω k is the exponential decay weight; among them, let then the calculation method of the dispersion index is: where C terrain is the terrain complexity index, and C ref is the reference complexity. According to the secondary comprehensive scoring, for the data with a medium result in the first comprehensive scoring, the secondary comprehensive scoring index is used to make a judgment again. If the result is high, it is recorded and stored as the fourth fusion dataset. Finally, the third fusion dataset and the fourth fusion dataset are combined to form the fifth fusion dataset as the final fusion dataset.

[0040] The above is only a preferred embodiment of the present invention, and it is not a limitation of the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments according to the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A multi-scale geographic information fusion method for national territorial space mapping, characterized in that, It includes the following steps: S1. Multi-source data collection: including satellite image collection, UAV mapping collection, and ground measurement collection; Satellite image collection: Using satellite remote sensing technology to obtain geographical information on a large area and macroscopic scale; UAV mapping collection: Collecting high-resolution and small-scale detailed data through low-altitude UAV mapping; Ground measurement collection: Conducting precise measurement on a specific area with the help of ground measurement equipment; S2. Data preprocessing: Performing format conversion, noise removal, and data integrity verification on the three types of original data to obtain preprocessed data; S3. Classify the preprocessed data according to the information source and scale characteristics, synchronize the timestamps and perform spatial registration on the same type of data to generate the first fusion dataset. The preliminary fusion dataset includes large-scale topographic and geomorphic datasets, medium-scale key area datasets, and small-scale specific location datasets; S4. Perform fusion of datasets at different scales on the first fusion dataset to form the second fusion dataset; S5. Based on different requirement characteristics, construct a comprehensive scoring index, and quantitatively score the second fusion dataset from the aspects of the detail level of geographical elements, data timeliness, and data dispersion degree. The scoring results are divided into high, medium, and low; S6. For the data with a high scoring result, record it as the third fusion dataset for storage. For the data with a low scoring result, directly perform removal operations. For the data with a medium scoring result, use the secondary comprehensive scoring index to judge again. If the scoring result is high, record it as the fourth fusion dataset for storage, otherwise remove it; S7. Finally, combine the third fusion dataset and the fourth fusion dataset to merge into the fifth fusion dataset as the final fusion dataset.

2. The multi-scale geographic information fusion method for national territorial space mapping according to claim 1, characterized in that, The calculation formula for timestamp synchronization of the same type of data before generating the first fusion dataset in S3 is as follows: where the synchronization cost function for timestamp synchronization is where i and j respectively represent two sampling instants, and d(x i , y j ) is the Euclidean distance metric, and the condition needs to satisfy the elastic window limit of |i - j| ≤ δ, where δ is the maximum time shift tolerance threshold.

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

4. The multi-scale geographic information fusion method for national territorial space mapping according to claim 1, wherein, In step S4, the second fusion dataset uses the large-scale topographic data in the first fusion dataset as the basic framework, embeds the medium-scale data into the framework through feature point matching, mounts the small-scale data to the corresponding parent nodes according to the spatial coordinates to establish a scale mapping rule, and detects the fusion boundary. For the areas with terrain mutations, local deformation compensation is performed. Finally, based on the results of change detection, the update mechanism is triggered, and the areas with elevation change > 0.5m are re-fused.

5. The multi-scale geographic information fusion method for national territorial space mapping according to claim 1, wherein The calculation of the comprehensive scoring index in step S5 is as follows: where α, β, γ ∈ [0, 3] and satisfy α + β + γ = 3; where is the geographical feature detail level, N feature is the number of feature points in the second fusion dataset, A is the area of the region, ρ ref is the reference density threshold, when take 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; wherein is the dispersion index of the data, and σ spatial is the standard deviation of the spatial distribution of geographical elements, and μ spatial is the mean of the spatial distribution.

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

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

8. The multi-scale geographic information fusion method for national territorial space mapping according to claim 7, characterized in that According to the secondary comprehensive scoring, for the data with a medium result in the first comprehensive scoring, use the secondary comprehensive scoring index to judge again. If the result is high, record it as the fourth fusion dataset for storage.

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