Rural house and land integrated right confirmation investigation system and method

Abstract

The invention belongs to the technical field of real estate right confirmation registration, and discloses a rural house and land integrated right confirmation investigation system and method, and the system comprises a data collection module which obtains image data and auxiliary geographic information data; the data processing module is used for preprocessing the acquired image data; the ownership investigation module is used for investigating and recording ownership information of the house and the land; the data fusion module is used for fusing the geometric data and the ownership data to form a complete house and land integrated data model; the right confirmation registration module is used for carrying out right confirmation registration and generating an electronic ownership certificate; and the data management and display module is used for storing, managing and visually displaying the right confirmation registration data. According to the invention, through multi-source data fusion and intelligent processing, the efficiency and precision of rural house and land integrated right confirmation investigation are improved, and reliable technical support is provided for rural real estate registration.

Description

Technical Field

[0001] This application relates to the technical field of real estate right confirmation and registration, and more specifically, to a rural housing and land integrated right confirmation investigation system and method. Background Art

[0002] Traditional rural housing and land right confirmation investigation methods mainly rely on manual on-site measurement and paper records, and have the following problems: First, the efficiency is low and it is difficult to meet the needs of large-scale right confirmation and registration; second, the accuracy is insufficient and it is difficult to accurately reflect the actual conditions of houses and land; third, data integration is difficult and it is difficult to achieve informatization management and sharing. Therefore, it is of great practical significance to develop an efficient, accurate and intelligent rural housing and land integrated right confirmation investigation system and method.

[0003] In view of this, we propose a rural housing and land integrated right confirmation investigation system and method. Summary of the Invention

[0004] 1. Technical Problems to be Solved

[0005] The purpose of this application is to provide an efficient, accurate and intelligent rural housing and land integrated right confirmation investigation system and method to solve the problems of low efficiency, insufficient accuracy and difficult data integration existing in the prior art. The data processing module corrects and splices the image data to generate a three-dimensional real scene model. Geometric features are accurately extracted, and the occupied area of the house and the boundary trend of the land can be accurately determined, avoiding ownership disputes caused by manual measurement errors or outdated traditional map data, and providing a solid data support for subsequent right confirmation and registration; the data fusion module fuses geometric data and ownership data to form a housing and land integrated data model, breaking data islands, enabling the mutual verification of housing and land spatial information and ownership information, and providing a comprehensive and accurate data basis for right confirmation and registration.

[0006] 2. Technical Solutions

[0007] The technical solution of this application provides a rural housing and land integrated right confirmation investigation system, including:

[0008] Data acquisition module: Obtain high-resolution image data of rural areas through drone oblique photogrammetry technology, and obtain auxiliary geographic information data through satellite remote sensing technology;

[0009] Data processing module: Preprocess the collected image data, including image correction, splicing and generating a three-dimensional real scene model, and extract geometric features of houses and land;

[0010] Ownership investigation module: Used to investigate and record the ownership information of houses and land in combination with geographic information system (GIS) and database technology, including rights holder information, house use and land nature, etc.;

[0011] Data fusion module: used to fuse geometric data and ownership data to form a complete real estate integrated data model;

[0012] Right confirmation and registration module: used to conduct right confirmation and registration based on the fused data model in accordance with relevant national laws, regulations and standards, and generate electronic ownership certificates;

[0013] Data management and display module: used to store, manage and visually display the right confirmation and registration data, and support data query, statistical analysis and dynamic update.

[0014] Furthermore, the data acquisition module includes:

[0015] UAV flight control system: used to plan the flight route of the UAV and control the UAV to fly and take pictures according to the preset route;

[0016] Image data storage unit: used to store the high-resolution image data taken by the UAV;

[0017] Satellite remote sensing data interface: used to receive and store the auxiliary geographic information data obtained by satellite remote sensing.

[0018] Furthermore, the UAV flight control system plans the flight route of the UAV and controls the UAV to fly and take pictures according to the preset route; it includes the following steps:

[0019] 1. Mission planning: Collect the basic geographic information of the target rural area, including topographic and geomorphic data (such as contour maps), village distribution, approximate locations of buildings, etc. Define the boundaries of the survey area and determine the areas that need to be focused on for shooting, such as sections with recent land changes or new house construction. Based on the survey accuracy requirements and the performance of the UAV equipment, determine the flight altitude and camera shooting parameters, such as exposure time, sensitivity, etc., to ensure that the captured images are clear and can accurately reflect the features of the ground objects. Calculate the flight speed to ensure that the camera can take pictures at appropriate intervals during flight, avoiding shooting blind spots or excessive overlaps.

[0020] 2. Route planning: Use professional UAV route planning software, such as DJI GS Pro (applicable to DJI series UAVs), Pix4Dcapture, etc. These software have powerful functions and can perform intelligent route planning according to the imported geographic information data and the set flight parameters.

[0021] 3. Simulated flight inspection: After completing the route parameter settings, use the simulated flight function of the software to conduct a simulated demonstration of the planned route. Check whether the route covers the entire survey area, and whether there are any omissions, unreasonable turns, overlaps, etc. If problems are found, adjust the route parameters or manually modify the waypoints in a timely manner.

[0022] 4. Flight shooting execution: Start the drone and make it take off smoothly. During the takeoff process, closely monitor the flight status of the drone, such as the ascent speed, attitude stability, etc., to ensure a smooth takeoff. After the drone takes off to the set altitude, the flight control system automatically switches to the flight mode according to the preset route. When the drone flies according to the route, the flight control system automatically controls the camera to take pictures according to the preset shooting parameters and flight speed. Ensure that clear and compliant image data can be obtained at each shooting point.

[0023] 5. Flight end: When the drone completes the flight shooting task of the preset route, the flight control system controls the drone to automatically return to the safe landing area near the takeoff point according to the planned landing route. The image data is transmitted to storage devices such as computers through wireless signals.

[0024] Furthermore, the data processing module includes: an image correction unit, a model generation unit, and a feature extraction unit;

[0025] Image correction unit: used to perform distortion correction and color correction on the collected image data;

[0026] Model generation unit: used to generate a three-dimensional real scene model of rural areas through three-dimensional modeling algorithms;

[0027] Feature extraction unit: used to extract geometric features of houses and land from the three-dimensional real scene model, including information such as boundaries, areas, and heights.

[0028] The data processing module preprocesses the collected image data, including the following steps:

[0029] 1. Data sorting: Classify and sort a large amount of image data obtained from drone flight shooting and auxiliary geographic information image data obtained from satellite remote sensing according to shooting time, area, etc., to ensure the orderliness of the data. At the same time, check the data integrity to confirm that there is no data loss or damage. Use a format conversion tool (such as Format Factory, etc.) to convert the images into a common and suitable format for processing. Establish a data index for convenient subsequent quick retrieval. The index information includes key information such as image file names, shooting times, and shooting locations.

[0030] 2. Image correction:

[0031] 2.1 Selection of ground control points (GCPs): Uniformly select a certain number of ground control points in the image through on-site measurement or reference to high-precision maps.

[0032] 2.2 Measurement of control point coordinates: Use measurement equipment such as total stations and GPS receivers to accurately obtain the true geographical coordinates of the selected ground control points.

[0033] 2.3. Calibration model construction: Input the selected ground control points and their corresponding image coordinates and true geographic coordinates into professional image calibration software (such as ENVI, Erdas Imagine, etc.), and use algorithms such as polynomial fitting to construct a geometric calibration model. The model can describe the geometric deformation in the image caused by factors such as camera lens distortion and UAV flight attitude changes.

[0034] 2.4. Image calibration implementation: Use the constructed calibration model to perform geometric calibration on the original image, so that the positions of ground objects in the calibrated image accurately correspond to the true geographic coordinates, and eliminate image deformation.

[0035] 3. Image stitching:

[0036] 3.1. Feature point extraction: Adopt algorithms such as SIFT (Scale-Invariant Feature Transform) and SURF (Speeded-Up Robust Features) to extract a large number of key points with unique features in the calibrated images. These key points can remain relatively stable under image translation, rotation, scaling and other transformations, which is convenient for subsequent matching between images.

[0037] 3.2. Feature point matching: Based on the extracted feature points, use feature matching algorithms (such as FLANN fast nearest neighbor search algorithm) to find homologous feature points between adjacent images, that is, the feature points corresponding to the same ground object in different images. By matching the homologous feature points, the relative position relationship between adjacent images is determined.

[0038] 3.3. Image registration: According to the feature point matching results, use optimization algorithms such as the least squares method to calculate the transformation parameters (such as translation, rotation, scaling parameters) between adjacent images, so that adjacent images are accurately aligned. This step ensures that the stitched images are seamlessly connected and the positions of ground objects are continuous and consistent.

[0039] 3.4. Stitching and fusion: Fuse the registered images according to a certain stitching strategy (such as pyramid-based stitching method). During the fusion process, process the stitching seams through methods such as feathering and weighted averaging to eliminate the stitching traces caused by differences in image brightness and tone, and generate a complete and continuous large-area image.

[0040] 4. Generation of 3D real scene model:

[0041] - 4.1. Dense matching: Use multi-view stereo matching algorithms (such as PMVS, CMVS, etc.) to perform dense matching on the stitched images. By analyzing the features of ground objects in images from different perspectives, a large number of dense point clouds are generated in the three-dimensional space, and these point clouds can accurately reflect the surface shape and position information of ground objects.

[0042] 4.2 Point Cloud Filtering and Denoising: Since noise points and outliers may be introduced during the point cloud generation process, filtering algorithms (such as Gaussian filtering, median filtering, etc.) are used to process the dense point cloud, removing noise and outliers and improving the quality of the point cloud. At the same time, according to the topographic and geomorphic features, by setting appropriate thresholds, the invalid point cloud data below the ground is removed.

[0043] 4.3 Surface Reconstruction: Based on the filtered point cloud data, algorithms such as Delaunay triangulation are used for surface reconstruction to generate a triangular mesh model. This model can initially construct the three-dimensional surface shape of the ground objects.

[0044] 4.4 Texture Mapping: The corrected and mosaicked images are used as texture information and mapped onto the triangular mesh model to make the three-dimensional model have a realistic appearance of ground object textures, generating a realistic three-dimensional virtual scene model. During the texture mapping process, it is necessary to ensure the accurate correspondence between the texture and the triangular mesh to avoid problems such as texture distortion and stretching.

[0045] 5 Extraction of Geometric Features of Buildings and Lands

[0046] 5.1 Extraction of Geometric Features of Buildings: Using algorithms such as Canny edge detection, in the orthophoto or point cloud data corresponding to the three-dimensional virtual scene model, detect the edge contours of buildings. By setting appropriate edge detection thresholds, accurately identify the boundaries between buildings and surrounding ground objects. Track the detected building edge contours and convert them into vector data format for subsequent geometric parameter calculation. During the contour tracking process, handle problems such as contour discontinuity and noise to ensure the integrity and accuracy of the contours.

[0047] According to the extracted vector data of building contours, calculate geometric parameters such as the building floor area, length, width, height, and number of floors. Use the polygon area calculation algorithm to calculate the floor area, and calculate the length and width through the coordinates of contour vertices, etc.

[0048] 5.2 Extraction of Geometric Features of Lands: Based on the current land use map, land ownership boundary data, etc., manually or semi-automatically divide the land boundaries in the three-dimensional virtual scene model or orthophoto. For lands with regular shapes, geometric figure fitting algorithms can be used to automatically extract the boundaries; for lands with complex shapes, manual interaction is combined for accurate boundary drawing. Conduct shape analysis on the divided land boundaries and calculate parameters such as the perimeter, area, and shape index of the land. The shape index can be used to describe the complexity of the land shape, such as obtaining the compactness index by calculating the ratio of the land area to the area of a circle with the same perimeter. Extract the topographic features of the land from the point cloud data, such as slope, aspect, elevation, etc. Use topographic analysis algorithms to obtain the slope and aspect information of the land by calculating the elevation difference and direction between adjacent points in the point cloud data.

[0049] 6. Result verification and optimization: Verify the accuracy of the extracted geometric features of houses and land by comparing with field measurement data and existing high-precision geographic information data. Calculate the relative errors of geometric parameters such as area and length to check whether they meet the accuracy requirements of the rural real estate and land rights confirmation survey (such as the area error is within a certain percentage). Arrange professionals to manually check the generated three-dimensional real-scene model and the extracted geometric features, focusing on the integrity of the model, the accuracy of the ground features, and the rationality of the geometric parameters. Manually correct and improve loopholes, incorrect geometric features and other problems in the model. Based on the results of accuracy verification and manual inspection, optimize and adjust the parameter settings and algorithm selection in the data processing process. For example, adjust the edge detection threshold to more accurately extract the house outline, optimize the point cloud filter parameters to improve the accuracy of terrain feature extraction, etc., re-extract features and generate models until satisfactory results are achieved.

[0050] Furthermore, the ownership investigation module includes:

[0051] On-site investigation unit: used to conduct on-site investigations through mobile terminal devices and collect ownership information of houses and land.

[0052] Ownership information entry unit: used to enter the collected ownership information into the system and associate it with geographic information; accurately import the massive and complex ownership information collected on-site into the system and achieve the important task of closely associating it with geographic information.

[0053] Ownership information verification unit: used to verify the entered ownership information to ensure the accuracy and completeness of the information.

[0054] Furthermore, the data fusion module includes:

[0055] Data matching unit: used to spatially match and associate attributes between geometric data and ownership data;

[0056] Data verification unit: used to perform consistency verification on the fused data to ensure the accuracy and integrity of the data;

[0057] Data storage unit: used to store the integrated real estate data model in the database.

[0058] Furthermore, the data management and display module includes:

[0059] Data storage unit: used to store the real estate and land data after property rights registration; in view of the complexity and massiveness of the real estate and land data, a hybrid mode combining a distributed storage architecture with a relational database and a spatial database is adopted.

[0060] Data query unit: used to support users to query data based on conditions such as rights holder information, house location, and land nature;

[0061] Data statistical analysis unit: used to perform statistical analysis on the data of rights confirmation and registration, generate relevant reports and charts; develop diverse query interfaces to meet the needs of different user groups.

[0062] Data visualization display unit: used to visually display the data of rights confirmation and registration in forms such as maps and charts. Based on Geographic Information System (GIS) technology, develop an interactive map display platform.

[0063] The present invention provides a method for rural housing and land integrated rights confirmation survey, including the following steps:

[0064] S1. The data acquisition module uses a drone to conduct oblique photography according to a preset route to obtain multi-angle image data of rural areas; uses satellite remote sensing technology to obtain large-scale basic geographic information data;

[0065] S2. The data processing module corrects and splices the acquired image data to generate a three-dimensional real scene model, and extracts the geometric features of houses and land, including boundaries, areas, heights, etc.;

[0066] S3. The ownership survey module obtains the ownership information of houses and land through on-site surveys and data collection, and enters it into the system; uses GIS technology to perform spatial positioning and attribute association on the ownership information;

[0067] S4. The data fusion module fuses the geometric data and the ownership data to form a housing and land integrated data model, and performs data verification and consistency check;

[0068] S5. The rights confirmation and registration module performs rights confirmation and registration according to the fused data model in accordance with relevant laws, regulations and standards, generates electronic ownership certificates, and stores them in the database;

[0069] S6. The data management and display module stores, manages and visually displays the data of rights confirmation and registration, and supports data query, statistical analysis and dynamic update.

[0070] 3. Beneficial effects

[0071] One or more technical solutions provided in the technical solution of the present application have at least the following technical effects or advantages:

[0072] 1. By using drone oblique photography, multi-angle image data of rural areas can be obtained. Compared with traditional ground shooting, drones can quickly cover large areas, capture house and land information from different angles, comprehensively record details such as the appearance of buildings, topography, etc., and improve the efficiency and comprehensiveness of data collection. Combining satellite remote sensing technology to obtain large-scale basic geographic information data can make up for the limitation that drones can only cover local areas, obtain the macroscopic geographical background, such as terrain undulation, land use type distribution, etc., and make the survey data complete on a larger spatial scale.

[0073] 2. The data processing module corrects and stitches the image data to generate a three-dimensional real scene model. Correction can eliminate image distortion caused by shooting angles, equipment errors, etc., stitching enables multiple images to be seamlessly connected, and the three-dimensional real scene model intuitively presents the real situation of rural areas, facilitating staff to accurately extract geometric features of houses and land, including boundaries, areas, heights, etc., and providing accurate spatial information for ownership determination. Accurately extracting geometric features can accurately determine the occupied area of houses and the trend of land boundaries, avoid ownership disputes caused by manual measurement errors or outdated traditional map data, and provide a solid data support for subsequent right confirmation registration.

[0074] 3. The ownership investigation module obtains ownership information through on-site investigation and data collection, and enters it into the system. On-site investigation ensures the authenticity of information, and data collection covers various aspects of information such as historical archives, enriching the dimension of ownership information. Using GIS technology for spatial positioning and attribute association of ownership information can intuitively display the distribution of house and land ownership in the geographical space, closely combine the ownership information with the actual geographical location, facilitate quick query and analysis of the ownership status of specific areas, and improve the scientificity and accuracy of ownership management.

[0075] 4. The data fusion module fuses geometric data and ownership data to form a real estate integrated data model, breaking data islands, enabling the mutual verification of house and land spatial information and ownership information, and providing a comprehensive and accurate data basis for right confirmation registration. Data verification and consistency check can timely discover and correct data errors and contradictions, ensure the authenticity and reliability of the fused data, guarantee the accuracy of the basis for right confirmation registration, and reduce potential disputes in the future.

[0076] 5. The right confirmation registration module conducts right confirmation registration according to the fused data model in accordance with laws, regulations and standards, generates and stores electronic ownership certificates. The standardized process ensures the legality and compliance of registration. Electronic ownership certificates are convenient for storage, query and management, conducive to long-term data preservation and sharing, and improve the informatization level of rural real estate right confirmation registration. Brief Description of the Drawings

[0077] Figure 1 It is the architecture diagram of the rural real estate integrated right confirmation investigation system disclosed in a preferred embodiment of this application;

[0078] Figure 2 The data acquisition flowchart of the rural housing and land integrated right confirmation survey method disclosed in a preferred embodiment of the present application;

[0079] Figure 3 The data processing and integration flowchart of the rural housing and land integrated right confirmation survey method disclosed in a preferred embodiment of the present application;

[0080] Figure 4 The right confirmation registration and data management flowchart of the rural housing and land integrated right confirmation survey method disclosed in a preferred embodiment of the present application. Detailed implementation manners

[0081] The present application will be further described in detail below with reference to the accompanying drawings of the specification.

[0082] Referring to Figure 1 , the embodiment of the present application provides a rural housing and land integrated right confirmation survey system, including:

[0083] Data acquisition module: used to obtain high-resolution image data of rural areas through unmanned aerial vehicle (UAV) oblique photogrammetry technology and obtain auxiliary geographic information data through satellite remote sensing technology;

[0084] Data processing module: used to preprocess the acquired image data, including image correction and stitching, generate a three-dimensional real scene model, and extract the geometric features of houses and land;

[0085] Ownership survey module: used to investigate and record the ownership information of houses and land in combination with geographic information system (GIS) and database technology, including rights holder information, house use, land nature, etc.;

[0086] Data fusion module: used to fuse geometric data and ownership data to form a complete housing and land integrated data model;

[0087] Right confirmation registration module: used to perform right confirmation registration based on the fused data model according to relevant national laws, regulations and standards, and generate electronic ownership certificates;

[0088] Data management and display module: used to store, manage and visually display the right confirmation registration data, support data query, statistical analysis and dynamic update.

[0089] Further, the data acquisition module includes:

[0090] UAV flight control system: used to plan the flight route of the UAV and control the UAV to fly and take pictures according to the preset route;

[0091] Image data storage unit: used to store the high-resolution image data taken by the UAV;

[0092] Satellite remote sensing data interface: used to receive and store auxiliary geographic information data obtained by satellite remote sensing.

[0093] Furthermore, the UAV flight control system plans the flight route of the UAV and controls the UAV to perform flight photography according to the preset route; it includes the following steps:

[0094] 1. Mission planning: Collect the basic geographic information of the target rural area, including topographic and geomorphic data (such as contour maps), village distribution, approximate locations of buildings, etc. Define the boundary of the survey area and determine the areas that need to be focused on for shooting, such as sections with recent land changes or newly built houses. According to the survey accuracy requirements and the performance of the UAV equipment, determine the flight altitude and camera shooting parameters, such as exposure time, ISO, etc., to ensure that the captured images are clear and can accurately reflect the features of the ground objects. Calculate the flight speed to ensure that the camera can take pictures at appropriate intervals during flight, avoiding shooting blind spots or excessive overlaps.

[0095] 2. Route planning: Use professional UAV route planning software, such as DJI GS Pro (applicable to DJI series UAVs), Pix4Dcapture, etc. These software have powerful functions and can perform intelligent route planning based on the imported geographic information data and set flight parameters. Import the geographic information data of the target area collected in the early stage into the route planning software, and the software will generate a map base map of the area on the interface. Set the route-related parameters in the software, including flight mode (usually using the parallel route mode to ensure full coverage of the survey area) and route spacing. The route spacing is determined according to the shooting angle and resolution requirements of the camera to ensure sufficient overlap of the images for subsequent image stitching and 3D modeling. Set waypoints. For some areas with irregular shapes or special shooting requirements, manually add waypoints to accurately plan the flight path.

[0096] 3. Simulated flight inspection: After completing the route parameter settings, use the simulated flight function of the software to conduct a simulated demonstration of the planned route. Check whether the route covers the entire survey area, and whether there are any omissions or unreasonable turns, overlaps, etc. If problems are found, adjust the route parameters or manually modify the waypoints in a timely manner.

[0097] 4. Flight shooting execution: Connect the drone to the ground station equipment and calibrate various sensors such as accelerometers and gyroscopes to ensure accurate attitude perception of the drone. Initialize the flight control system of the drone, load the flight path data, and confirm that all parameters are correct. Start the drone and make it take off smoothly. During the takeoff process, closely monitor the flight state of the drone, such as the ascent speed and attitude stability, to ensure a smooth takeoff. After the drone takes off to the set altitude, the flight control system automatically switches to the flight mode according to the preset flight path. When the drone flies according to the flight path, the flight control system automatically controls the camera to take pictures according to the preset shooting parameters and flight speed. Ensure that clear and compliant image data can be obtained at each shooting point. For some key areas or positions that need to be supplemented with shooting, the ground operator can also manually control the camera through the remote control for additional shooting.

[0098] 5. Flight end: When the drone completes the flight shooting task of the preset flight path, the flight control system controls the drone to automatically return to the safe landing area near the takeoff point according to the planned landing flight path. The ground operator closely monitors the landing state of the drone during the landing process to ensure a smooth landing of the drone. The image data is transmitted to storage devices such as computers through wireless signals. Save the flight log data recorded by the flight control system of the drone, including flight trajectory, flight parameters, equipment status and other information.

[0099] Furthermore, the data processing module includes: an image correction unit, a model generation unit, and a feature extraction unit;

[0100] Image correction unit: Used to perform distortion correction and color correction on the collected image data;

[0101] Model generation unit: Used to generate a three-dimensional real scene model of rural areas through three-dimensional modeling algorithms;

[0102] Feature extraction unit: Used to extract geometric features of houses and land from the three-dimensional real scene model, including information such as boundaries, areas, and heights.

[0103] The data processing module preprocesses the collected image data, including the following steps:

[0104] 1. Data Sorting: Classify and organize a large amount of image data obtained from drone flight photography and auxiliary geographic information image data obtained from satellite remote sensing according to shooting time, area, etc. to ensure the orderliness of the data. At the same time, check the data integrity to confirm that there is no data loss or damage. Use format conversion tools (such as Format Factory, etc.) to convert the images into common and suitable formats for processing, such as JPEG, TIFF, etc. Store the sorted image data in a large-capacity and high-performance storage device and establish a data index for quick subsequent retrieval. The index information includes key information such as image file name, shooting time, shooting location (latitude and longitude coordinates), etc.

[0105] 2. Image Rectification:

[0106] 2.1 Selection of Ground Control Points (GCPs): Select a certain number of ground control points evenly in the images through on-site measurement or by referring to high-precision maps. These control points should have obvious and easily recognizable features, such as road intersections, building corners, etc., and can be accurately matched in different images.

[0107] 2.2 Measurement of Control Point Coordinates: Use surveying instruments such as total stations and GPS receivers to accurately obtain the true geographic coordinates (plane coordinates and elevation) of the selected ground control points.

[0108] 2.3 Construction of Rectification Model: Input the selected ground control points and their corresponding image coordinates and true geographic coordinates into professional image rectification software (such as ENVI, Erdas Imagine, etc.), and use algorithms such as polynomial fitting to construct a geometric rectification model. The model can describe the geometric deformation in the images caused by factors such as camera lens distortion and changes in the flight attitude of the drone.

[0109] 2.4 Implementation of Image Rectification: Use the constructed rectification model to perform geometric rectification on the original images so that the positions of the ground objects in the rectified images accurately correspond to the true geographic coordinates, eliminating image distortion.

[0110] 3. Image Mosaic:

[0111] 3.1 Feature Point Extraction: Adopt algorithms such as SIFT (Scale-Invariant Feature Transform) and SURF (Speeded-Up Robust Features) to extract a large number of key points with unique features in the rectified images. These key points can remain relatively stable under image translation, rotation, scaling, and other transformations, facilitating subsequent matching between images.

[0112] 3.2 Feature Point Matching: Based on the extracted feature points, use feature matching algorithms (such as the FLANN Fast Approximate Nearest Neighbor Search Algorithm) to find homologous feature points between adjacent images, that is, the feature points corresponding to the same ground object in different images. By matching the homologous feature points, determine the relative position relationship between adjacent images.

[0113] 3.3 Image registration: Based on the feature point matching results, using optimization algorithms such as the least squares method, calculate the transformation parameters (such as translation, rotation, and scaling parameters) between adjacent images to accurately align adjacent images. This step ensures seamless connection of the spliced images and continuous and consistent ground object positions.

[0114] 3.4 Mosaic fusion: Fuse the registered images according to a certain mosaic strategy (such as the pyramid-based mosaic method). During the fusion process, process the mosaic seam through methods such as feathering and weighted averaging to eliminate the mosaic traces caused by differences in image brightness and tone, and generate a complete and continuous large-area image. The image mosaic fusion is performed according to the following formula:

[0115] I f (x,y) = w1(x,y)I1(x,y) + w2(x,y)I2(x,y); w1(x,y) + w2(x,y) = 1;

[0116] w1(x,y) = [1 - β(x,y)]{u1(x,y) / [u1(x,y) + u2(x,y)]} + β(x,y);

[0117] β(x,y) = [σ 2 1(x,y)] / [σ 2 1(x,y) + σ 2 2(x,y)]; In the formula, I f (x,y) represents the pixel value of the fused image at the coordinate (x,y). After performing the fusion operation on the two registered images, the value of the color, brightness, etc. of the finally obtained new image at this position. I1(x,y) and I2(x,y) respectively represent the pixel values of the two registered images at the coordinate (x,y). These two images are the original data to be fused. β(x,y) is an adaptive parameter, and its value is related to the local statistical characteristics of the image. This parameter plays an important role in calculating the weight function w1(x,y) and is used to dynamically adjust the contribution ratio of the two images during the fusion process. u1(x,y) and u2(x,y) respectively represent the means (average pixel values) of images I1 and I2 in the local area centered on the coordinate (x,y). The size of the local area can be set according to the actual situation, and the average value of all pixel values within this window is calculated. σ 2 1(x,y) and σ 22(x, y) represents the variances of images I1 and I2 within the local regions centered at the coordinates (x, y). The variance measures the degree of dispersion of pixel values within the local region. The larger the variance, the more drastic the change in pixel values within the region (richer texture, etc.). w1(x, y) and w2(x, y) are weight functions, and their values determine the contribution degrees of images I1(x, y) and I2(x, y) to the fused pixel value I f (x, y) during the fusion process.

[0118] 4. Generate a 3D real scene model:

[0119] 4.1. Dense matching: Apply multi-view stereo matching algorithms (such as PMVS, CMVS, etc.) to perform dense matching on the stitched images. By analyzing the features of ground objects in images from different viewpoints, a large number of dense point clouds are generated in 3D space. These point clouds can accurately reflect the surface shape and position information of ground objects. Calculate the photometric consistency measure according to the following formula:

[0120] C = Σ n i＝1 w i Σ[g1(x1, y1) - g i (x i , y i )] 2 ·{1 + a▽g1(x1, y1) / [max n j＝1 ▽g j (x j , y j )]};

[0121] (x, y) ∈ w; Σ n i＝1 w i = 1; In the formula, C is the photometric consistency measure, and this value is used to measure the similarity degree between corresponding pixel points in images from different viewpoints. The smaller the C value, the higher the similarity of the corresponding pixel points, and the more likely they are homologous points. n is the number of images participating in the matching. In actual multi-view stereo matching, usually multiple images from different viewpoints are used to determine homologous points, and here n is the total number of these images. w i is the adaptive weight factor of image I i . Its value range is [0, 1]. Its value can be dynamically adjusted according to factors such as the shooting quality of the image (such as clarity, noise level), and the scene coverage range (the integrity of the key scenes included in the image). For example, if an image is taken on a sunny day with a stable device and completely covers the key ground objects, then the corresponding w iThe value may be relatively large, indicating that the contribution of this image in the matching process is greater. w is a small window around the pixel, usually a 3×3 or 5×5 window. When calculating photometric consistency, instead of only considering a single pixel, the pixels within a small neighborhood window centered on this pixel are considered, which can reduce the influence of noise and better utilize local image features. g1(x1,y1) is the gray value at the pixel point (x1,y1) to be matched in the reference image I1. In multi-view stereo matching, usually one image is selected as the reference image, and other images are matched with it. g i (x i ,y i ) is the pixel point (x i in the i-th image I i ,y i ) that corresponds to the pixel point (x1,y1) in the reference image I1 through geometric relationships such as epipolar constraints. Through methods such as epipolar constraints, the corresponding relationship of pixel points is established between images with different perspectives. g i (x i ,y i ) represents the gray value of the pixel at the corresponding position on other images. a is a tuning parameter with a value between [0,1]. It is used to control the influence degree of image gradient information on the matching. ▽g1(x1,y1) is the gradient magnitude of the image I1 at the point (x1,y1);

[0122] ▽g j (x j ,y j ) is the gradient magnitude of the image I j at the point (x j ,y j ) and is obtained through image gradient calculation (such as Sobel operator). The image gradient reflects the change of pixel values in the image. Areas with large gradient magnitudes usually correspond to areas with rich features such as edges and textures in the image. Introducing gradient information in the matching process helps to more accurately determine corresponding points in areas with obvious features. max is the symbol for taking the maximum value.

[0123] 4.2. Point Cloud Filtering and Denoising: Since noise points and outliers may be introduced during the point cloud generation process, filtering algorithms (such as Gaussian filtering, median filtering, etc.) are used to process the dense point cloud to remove noise and outliers and improve the quality of the point cloud. At the same time, according to the topographic and geomorphic features, by setting appropriate thresholds, the invalid point cloud data below the ground is removed.

[0124] 4.3. Surface Reconstruction: Based on the filtered point cloud data, algorithms such as Delaunay triangulation are used for surface reconstruction to generate a triangular mesh model. This model can initially construct the three-dimensional surface shape of the ground object.

[0125] 4.4 Texture mapping: Use the corrected and mosaicked images as texture information and map them onto the triangular mesh model to give the 3D model a realistic ground object texture appearance and generate a realistic 3D scene model. During the texture mapping process, it is necessary to ensure the accurate correspondence between the texture and the triangular mesh and avoid problems such as texture distortion and stretching.

[0126] 5 Extraction of geometric features of houses and land:

[0127] 5.1 Extraction of geometric features of houses: Use algorithms such as the Canny edge detection algorithm to detect the edge contours of houses in the orthoimage or point cloud data corresponding to the 3D scene model. By setting appropriate edge detection thresholds, accurately identify the boundaries between houses and surrounding ground objects. Track the detected house edge contours and convert them into vector data format for subsequent geometric parameter calculation. During the contour tracking process, handle problems such as contour discontinuity and noise to ensure the integrity and accuracy of the contours.

[0128] Based on the extracted house contour vector data, calculate geometric parameters such as the building area, length, width, height (obtained from point cloud data), and number of floors (assist in judgment by combining image features and on-site surveys) of the house. Use the polygon area calculation algorithm to calculate the building area, and calculate the length and width through the contour vertex coordinates, etc.

[0129] 5.2 Extraction of geometric features of land: Based on the current land use map, land ownership boundary data, etc., manually or semi-automatically divide the land boundary in the 3D scene model or orthoimage. For lands with regular shapes, the geometric figure fitting algorithm can be used to automatically extract the boundary; for lands with complex shapes, combine manual interaction to accurately draw the boundary. Conduct shape analysis on the divided land boundary and calculate parameters such as the perimeter, area, and shape index of the land. The shape index can be used to describe the complexity of the land shape. For example, the compactness index can be obtained by calculating the ratio of the land area to the area of a circle with the same perimeter.

[0130] Terrain feature extraction: Extract terrain features of the land from the point cloud data, such as slope, aspect, elevation, etc. Use terrain analysis algorithms to obtain the slope and aspect information of the land by calculating the elevation difference and direction between adjacent points in the point cloud data.

[0131] 6. Result verification and optimization: Verify the accuracy of the extracted geometric features of houses and land by comparing with on-site measurement data and existing high-precision geographic information data. Calculate the relative errors of geometric parameters such as area and length, and check whether the accuracy requirements for the rural house and land rights confirmation survey are met (such as the area error is within a certain percentage). Arrange professional personnel to conduct manual inspections on the generated 3D real-scene model and the extracted geometric features, focusing on the integrity of the model, the accuracy of ground object features, and the rationality of geometric parameters. For problems such as loopholes in the model and incorrect geometric features, perform manual correction and improvement. According to the results of accuracy verification and manual inspection, optimize and adjust the parameter settings, algorithm selection, etc. in the data processing process. For example, adjust the edge detection threshold to more accurately extract the house contour, optimize the point cloud filtering parameters to improve the accuracy of terrain feature extraction, etc., and re-perform feature extraction and model generation until satisfactory results are achieved.

[0132] Furthermore, the rights and interests investigation module includes:

[0133] On-site investigation unit: Used to conduct on-site investigations through mobile terminal devices and collect the rights and interests information of houses and land; staff members, with the help of mobile terminal devices, such as professional tablets or smartphones with high-resolution screens, accurate GPS positioning, and powerful data processing capabilities, go to the field to carry out detailed investigations. When collecting the rights and interests information of houses, the staff not only need to record the location address of the house accurately to the house number, but also need to deeply understand the key identity information such as the name and ID number of the house owner, as well as the detailed conditions such as the building area, number of floors, use (residential, commercial, industrial, etc.), and construction year of the house. For the rights and interests information of land, it is necessary to record the four boundaries of the land, accurately mark the coordinates of each boundary point through the GPS positioning function of the mobile terminal, clarify the actual scope of the land, and at the same time record the information of the land owner or user, land use (cultivated land, forest land, construction land, etc.), and land use term and other core contents.

[0134] Rights and interests information entry unit: Used to enter the collected rights and interests information into the system and associate it with geographic information; import the massive and complex rights and interests information collected on-site into the system accurately and fulfill the important task of closely associating it with geographic information. The staff fill in the rights and interests data of houses and land into the corresponding fields one by one in the specially designed entry interface. For house information, associate the information such as the house owner and building area with the house spatial position data in geographic information, so that in the geographic information system, each house can accurately correspond to its detailed rights and interests information. When processing the rights and interests information of land, combine the information such as the land owner, use, and boundary point coordinates with the land layer in geographic information to ensure that the rights and interests status of each piece of land can be clearly displayed on the map, providing a solid data foundation for subsequent query, statistics, and analysis.

[0135] Ownership information verification unit: used to verify the entered ownership information to ensure the accuracy and completeness of the information. This unit will first perform a logical check on the information, such as checking whether the logical relationship between the building area and the number of floors of the house is reasonable. If there is an abnormal situation where the building area is too small but the number of floors is too many, a warning will be issued in time. For land information, it will verify whether the land use is consistent with the surrounding land use type to avoid conflicts of use. At the same time, a data integrity check is performed to check whether all required fields have been accurately filled in, such as whether the ID number of the house owner is complete and whether the coordinates of the boundary points of the land are complete. Through multiple verification methods, data errors and omissions are minimized to provide reliable data support for subsequent ownership management work.

[0136] Furthermore, the data fusion module includes:

[0137] Data matching unit: used to spatially match and associate attributes between geometric data and ownership data;

[0138] Data verification unit: used to perform consistency verification on the fused data to ensure the accuracy and integrity of the data;

[0139] Data storage unit: used to store the integrated real estate data model in the database.

[0140] The data fusion module fuses geometric data with ownership data to form a complete real estate data model. This includes the following steps:

[0141] 1. Data Preparation: Collect geometric data, including the precise location, shape, outline, and spatial dimensions of real estate such as houses and land. For example, vector data from high-precision topographic maps and cadastral maps, such as building boundary coordinates and plot polygon vertex coordinates, can be found. Also collect ownership data, focusing on legal information such as property ownership and usage rights, such as the name and ID number of the property owner, and the type and term of land use rights.

[0142] 2. Coordinate system unification: Because geometric and property data may originate from different measurement units or historical periods, resulting in different coordinate bases, coordinate conversion algorithms are used to unify all data into a national or regional geodetic coordinate system. For example, a Gauss-Krüger projection is used to convert longitude and latitude coordinates into rectangular coordinates.

[0143] 3. Spatial Matching: Leveraging spatial indexing technologies, such as R-tree indexing, we can quickly locate the spatial correspondence between geometric shapes and ownership information. For example, for a house, we can associate the geometric outline polygons of the house with the corresponding property rights information, ensuring that each house's spatial location accurately corresponds to its owner.

[0144] 4. Attribute association: Establishing connections based on common identifiers. For example, a unique parcel code in land data exists in both the parcel attribute table of the geometric data and the land registration information table of the ownership data. This code closely links geometric attributes such as land shape and area with ownership attributes such as land ownership and land use change records.

[0145] For complex situations, such as multiple owners in an apartment building sharing a building, there are multiple ownership entities corresponding to the same geometric space. Through additional association tables, detailed information such as the equity share and usage scope of each ownership entity in the geometric space is recorded to achieve many-to-many precise attribute association.

[0146] 5. Consistency Verification: Checks whether the internal logical relationships between data are reasonable. For example, this includes verifying whether the land use period is within a reasonable range. If the use period is earlier than the land registration date or exceeds the legal maximum use period, it is considered a logical error. The correct mathematical relationship between the building area, floor area, and shared area is also verified, meaning that the building area should equal the sum of the floor area and shared area. Data format consistency verification is performed to ensure that data from different sources is consistent in terms of data type and encoding rules.

[0147] 6. Accuracy Verification: For geospatial data, reference is made to basic geographic information data to verify the accuracy of the spatial location and shape of real estate. For ownership data, comparison is performed with the original archival materials at the Real Estate Registration Center to verify the authenticity and reliability of owner information and ownership change records. Error analysis techniques are used to calculate data deviations, and data exceeding the allowable error threshold is flagged and corrected.

[0148] 7. Completeness Verification: Checks for missing values in the dataset. For mandatory fields such as property owner name and land use rights type, blank records are considered incomplete. By establishing a data integrity rule base that clearly defines the required fields and integrity requirements for each data type, automatic scanning and detection are performed during the verification process to ensure that all key information is included.

[0149] 8. Data Storage: Adopt a storage solution that combines a relational database and a spatial database. The relational database (such as Oracle, MySQL) is used to store structured ownership data, facilitating data operations such as addition, deletion, modification, and query, and maintaining data consistency and integrity by establishing inter-table association relationships. The spatial database (such as PostGIS, a spatial extension based on PostgreSQL) is specifically used to store and manage geospatial data, supporting efficient spatial query and analysis functions. Design the data storage structure and construct multiple interrelated data tables. For housing data, establish a basic housing information table to record conventional information such as the housing address, floor area, and number of floors; the housing property owner information table stores the detailed identity information of the property owner and the property share; the housing spatial information table uses spatial data types (such as WKT, WKB) to store the housing geometric contour data. Land data is also stored through a similar multi-table structure, including a land parcel information table, a land use right owner information table, a land spatial location table, etc. Foreign key associations are established between different data tables through unique identifiers (such as housing codes, parcel codes) to ensure data relevance and traceability. Optimize the database index. In the relational database, create ordinary indexes for commonly queried fields such as the property owner's name and land use, to speed up data retrieval. In the spatial database, establish spatial indexes for spatial geometric data, such as quadtree indexes, R-tree indexes, etc., so that spatial query operations (such as querying all houses or lands within a certain area) can quickly locate the target data. At the same time, adopt data backup and recovery strategies, and perform full backups and incremental backups on the database regularly to prevent data loss caused by hardware failures, human errors, or other unexpected situations, and ensure the long-term secure storage and stable use of the land and housing integrated data model.

[0150] Further, the data management and display module includes:

[0151] Data storage unit: It is used to store the integrated real estate data after rights confirmation and registration. Given the complexity and large volume of the integrated real estate data, a hybrid mode combining a distributed storage architecture with a relational database and a spatial database is adopted. The distributed storage system (such as Ceph) can disperse and store data on multiple physical nodes, improving the reliability and scalability of storage. The relational database (such as MySQL cluster) is used to store structured ownership data, such as detailed information of the right holders (name, ID number, contact information, etc.), registration information of land and houses (registration time, certificate number, etc.). The spatial database (such as PostGIS) is specifically responsible for storing geospatial data, including the polygon boundaries of land, the precise geographical locations and geometric shapes of houses, etc. A data partitioning and table partitioning strategy is established to improve storage and query efficiency. For ownership data, the registration data is partitioned and stored according to the time dimension (such as by year) to facilitate quick query of the rights confirmation and registration information within a specific time period. For spatial data, it is stored in chunks according to geographical regions (such as administrative divisions) to reduce the data retrieval scope. For example, the land and house spatial data of a city is divided by districts and counties and stored in different spatial data tables respectively.

[0152] Data query unit: It is used to support users to query data according to conditions such as the information of the right holders, the location of the houses, and the nature of the land.

[0153] Data statistical analysis unit: It is used to perform statistical analysis on the data of confirmation registration, generate relevant reports and charts; develop diversified query interfaces to meet the needs of different user groups. Provide Web API interfaces to facilitate professional users to perform batch data queries through programming. For example, real estate agencies can obtain the housing ownership information within a specific area that meets certain price ranges and housing type requirements by calling the interfaces. At the same time, design a simple and easy-to-use graphical user interface (GUI) query window for ordinary users. Users only need to enter the name of the right holder, keywords of the housing address or select conditions such as land nature on the interface to easily initiate a query. Support fuzzy query and combined query functions. Fuzzy query allows users to use wildcards when entering query conditions. For example, when querying the housing location, if the user enters "near a certain street", the system can find all housing information within a certain range of the street according to the buffer analysis function of geospatial data. Combined query enables users to select multiple query conditions at the same time, and the system can quickly filter out the data that meets all conditions. Establish multi-dimensional indexes to accelerate data queries. In a relational database, create ordinary indexes for the commonly queried fields such as the name and ID number fields in the right holder information table, the address and building area fields in the housing information table, and the land nature and usage period fields in the land information table. For a spatial database, establish a spatial index for the spatial geometric data of land and housing, such as an R-tree index. Through index technology, the system can quickly locate the data that meets the query conditions, reduce the data scanning range, and greatly improve the query speed. Adopt caching technology to reduce the overhead of repeated queries. When users frequently query data with the same or similar conditions, the system caches the query results. In subsequent queries, first check whether there are results that meet the conditions in the cache. If so, directly return the cached data without performing the database query operation again. The cache is updated regularly to ensure the real-time nature of the data. For example, for the query results of houses in popular areas, the cache validity period is set to 1 hour, and the same query directly obtains the results from the cache within the validity period.

[0154] Data Visualization and Display Unit: It is used to visually display the data of rights confirmation and registration in the form of maps, charts, etc. Based on Geographic Information System (GIS) technology, an interactive map display platform is developed. The rights confirmation and registration information of land and houses is intuitively displayed on the map. Users can view the detailed data of different regions by zooming in and out and panning the map. The land is represented by different colors and textures to indicate its nature, and the houses are marked in the form of icons. Clicking on the icon can pop up a detailed information window, showing information such as the right holders, building area, and uses of the houses. The spatial analysis function of the map is utilized for data visualization and display. For example, through the overlay analysis function, the land use planning map is overlaid with the rights confirmation and registration data to display the distribution of the confirmed land and houses within the planned area, providing a decision-making basis for urban planning and construction. The buffer analysis function is used to display the information of houses and land within a certain range of a certain infrastructure (such as schools, hospitals), facilitating residents to understand the surrounding real estate situation. Multiple types of charts are designed to visually present the results of statistical analysis. Bar charts are used to show the comparison of land areas or the number of houses in different regions, line charts are used to show the changing trends of real estate prices or land transfer fees over time, and pie charts are suitable for showing the proportions of different land natures or house types. The charts have interactive functions. When the user hovers the mouse over the chart elements, detailed data information can be displayed.

[0155] The present invention provides a method for rural housing and land integrated rights confirmation survey, including the following steps:

[0156] S1. The data acquisition module uses a drone to conduct oblique photography according to a preset route to obtain multi-angle image data of rural areas; at the same time, satellite remote sensing technology is used to obtain large-scale basic geographic information data;

[0157] S2. The data processing module corrects and stitches the collected image data to generate a three-dimensional real scene model, and extracts the geometric features of houses and land, including boundaries, areas, heights, etc.;

[0158] S3. The rights investigation module obtains the rights information of houses and land through on-site investigation and data collection, and enters it into the system; the GIS technology is used to conduct spatial positioning and attribute association on the rights information;

[0159] S4. The data fusion module fuses the geometric data and the rights data to form an integrated housing and land data model, and conducts data verification and consistency check;

[0160] S5. The rights confirmation and registration module conducts rights confirmation and registration according to the fused data model in accordance with relevant laws, regulations and standards, generates electronic rights certificates, and stores them in the database;

[0161] S6. The data management and display module stores, manages and visually displays the rights confirmation and registration data, and supports data query, statistical analysis and dynamic update.

[0162] The working principle of a rural real estate and land integrated right confirmation investigation system of the present invention is as follows: The data acquisition module uses an unmanned aerial vehicle for oblique photography to obtain high-resolution image data of rural areas. At the same time, the satellite remote sensing data of the national Tianmap is used as auxiliary data. The data processing module corrects and splices the collected image data to generate a three-dimensional real scene model. Geometric features of houses and land, including boundaries, areas, heights, etc., are extracted through the model and stored in the database. The right confirmation investigation module conducts on-site investigations by investigators carrying mobile terminals, combines with the GIS map, collects the right confirmation information of houses and land, and enters it into the system. The system automatically conducts spatial positioning and attribute association on the right confirmation information to ensure the accuracy of the information. The data fusion module fuses the geometric data and the right confirmation data to form a real estate and land integrated data model, and conducts data verification and consistency check to ensure the integrity and accuracy of the data. The right confirmation registration module conducts right confirmation registration according to the fused data model in accordance with relevant laws, regulations and standards, generates electronic right confirmation certificates, and stores them in the database. Through the data management and display module, the right confirmation registration data is stored, managed and visually displayed. Users can query, statistically analyze the data through the system and conduct dynamic updates.

[0163] Through the use of drone oblique photography, the present invention can obtain multi-angle image data of rural areas. Compared with traditional ground shooting, drones can quickly cover large areas and capture house and land information from different angles, comprehensively recording details such as the appearance of buildings, topography, etc., improving the efficiency and comprehensiveness of data collection. By combining satellite remote sensing technology to obtain large-scale basic geographical information data, the limitation that drones can only cover local areas is made up for, and a macroscopic geographical background, such as terrain undulation, land use type distribution, etc., is obtained, making the survey data complete on a larger spatial scale. The data processing module corrects and stitches the image data to generate a three-dimensional real-scene model. Correction can eliminate image deformation caused by shooting angles, equipment errors, etc., and stitching enables multiple images to be seamlessly connected. The three-dimensional real-scene model intuitively presents the real situation of rural areas, facilitating staff to accurately extract geometric features of houses and land, including boundaries, areas, heights, etc., providing accurate spatial information for ownership determination. The ownership survey module obtains ownership information through on-site surveys and data collection and enters it into the system. On-site surveys ensure the authenticity of the information, and data collection covers various aspects of information such as historical archives, enriching the dimension of ownership information. Using GIS technology for spatial positioning and attribute association of ownership information can visually display the distribution of house and land ownership in the geographical space, closely combining the ownership information with the actual geographical location, facilitating quick query and analysis of the ownership status of specific areas, and improving the scientificity and accuracy of ownership management. The data fusion module fuses geometric data and ownership data to form a real estate integrated data model, breaking data silos, enabling the spatial information of houses and land to corroborate with the ownership information, and providing a comprehensive and accurate data basis for right confirmation registration. Data verification and consistency checks can promptly detect and correct data errors and contradictions, ensuring the authenticity and reliability of the fused data, guaranteeing the accuracy of the basis for right confirmation registration, and reducing potential disputes in the future. The right confirmation registration module conducts right confirmation registration according to laws, regulations and standards based on the fused data model, generates an electronic ownership certificate and stores it. The standardized process ensures the legality and compliance of the registration. The electronic ownership certificate is convenient for storage, query and management, facilitating long-term data preservation and sharing, and improving the informatization level of rural real estate right confirmation registration.

[0164] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for the confirmation and investigation of the integration of rural housing and land, characterized in that, It includes the following steps: S1. The data acquisition module conducts oblique photography by drone according to a preset flight path to obtain multi-angle image data of rural areas; uses satellite remote sensing technology to obtain large-scale basic geographic information data; S2. The data processing module corrects, stitches and generates a three-dimensional real scene model for the acquired image data, and extracts the geometric features of houses and land; S3. The ownership investigation module obtains the ownership information of houses and land through on-site investigation and data collection, and enters it into the system; uses GIS technology to perform spatial positioning and attribute association on the ownership information; S4. The data fusion module fuses the geometric data and the ownership data to form a real estate integrated data model; S5. The rights confirmation and registration module conducts rights confirmation and registration according to the fused data model, and generates an electronic ownership certificate; S6. The data management and display module stores, manages and visually displays the rights confirmation and registration data.

2. The rural housing and land integrated right confirmation survey method according to claim 1, characterized in that: Step S1 includes the following steps: S11. Task planning: Collect the basic geographic information of the target rural area, clarify the boundary of the investigation area, determine the areas that need to be focused on shooting, and determine the flight altitude, speed and camera shooting parameters; S12. Flight path planning: Use the Pix4Dcapture drone flight path planning software to intelligently plan the optimal route for the drone to fly; S13. Simulated flight inspection: Use the simulated flight function of the software to conduct a simulated demonstration of the planned flight path; S14. Flight shooting execution: After the drone takes off to the set altitude, conduct aerial photography according to the planned route and parameters to collect high-definition images; S15. Flight end: When the drone completes the flight shooting task of the preset flight path, it automatically returns to a safe landing area near the take-off point.

3. The rural housing and land integrated right confirmation survey method according to claim 1, wherein: Step S2 includes the following steps: S21. Data sorting: Classify and sort the image data obtained by drone flight shooting and the auxiliary geographic information image data obtained by satellite remote sensing according to the shooting time and area, and check the data integrity; establish a data index; S22. Image correction; S23. Image stitching; S24. Generate a three-dimensional real scene model: S24.

1. Dense matching: Use the PMVS multi-view stereo matching algorithm to perform dense matching on the stitched images; S24.

2. Point cloud filtering and denoising: Use the Gaussian filtering algorithm to process the dense point cloud, remove noise and outliers, and according to the topographic and geomorphic features, remove the invalid point cloud data below the ground by setting thresholds; S24.

3. Surface reconstruction: Based on the filtered point cloud data, use the Delaunay triangulation algorithm for surface reconstruction to generate a triangular mesh model; S24.

4. Texture mapping: Map the corrected and stitched images as texture information onto the triangular mesh model to generate a three-dimensional real scene model; S25. Extraction of geometric features of houses and land: S25.

1. Extraction of geometric features of houses: Use the Canny edge detection algorithm to detect the edge contour of the house in the orthophoto or point cloud data corresponding to the three-dimensional real scene model; according to the extracted house contour vector data, calculate the geometric parameters of the building area, length, width, height and number of floors of the house; S25.

2. Land Geometric Feature Extraction: Delineate land boundaries within the 3D real-world model based on the current land use map and land ownership boundary data. Perform shape analysis on the delineated land boundaries to calculate the perimeter, area, and shape index parameters of the land. Extract land topographic features from point cloud data. Utilize terrain analysis algorithms to obtain land slope and aspect information. S26. Result verification and optimization: By comparing with field measurement data and existing high-precision geographic information data, the accuracy of the extracted house and land geometric features is verified, and the parameter settings and algorithm selection in the data processing process are optimized and adjusted.

4. The rural housing and land integrated right confirmation survey method according to claim 3, characterized in that: Step S22 includes the following steps: S22.

1. Ground control point selection: A certain number of ground control points are evenly selected in the image through field measurement or reference to high-precision maps; S22.

2. Control point coordinate measurement: Use a total station to accurately obtain the true geographic coordinates of the selected ground control points; S22.

3. Calibration Model Construction: Input the selected ground control points and their corresponding image coordinates and real geographic coordinates into professional image calibration software, and construct a geometric calibration model using a polynomial fitting algorithm; S22.

4. Image correction implementation: Use the constructed correction model to perform geometric correction on the original image so that the position of the objects in the corrected image accurately corresponds to the real geographic coordinates and eliminate image distortion.

5. The rural housing and land integrated right confirmation survey method according to claim 3, wherein: Step S23 includes the following steps: S23.

1. Feature point extraction: Use the SIFT algorithm to extract key points with unique features from the rectified image; S23.

2. Feature Point Matching: Based on the extracted feature points, a feature matching algorithm is used to find feature points with the same name between adjacent images. By matching the feature points with the same name, the relative position relationship between the adjacent images is determined; S23.3, Image Registration: Based on the feature point matching results, use the least squares optimization algorithm to calculate the transformation parameters between adjacent images to accurately align adjacent images; S23.

4. Stitching and fusion: Fuse the registered images; perform image stitching and fusion according to the following formula: I f (x,y) = w1(x,y)I1(x,y) + w2(x,y)I2(x,y); w1(x,y) + w2(x,y) = 1; w1(x,y)=[1-β(x,y)]{u1(x,y) / [u1(x,y)+u2(x,y)]}+β(x,y); β(x,y) = [σ 2 1(x,y)] / [σ 2 1(x,y) + σ 2 2(x,y)]; where, I f (x,y) represents The pixel value of the fused image at the coordinate (x, y); I1(x, y) and I2(x, y) respectively represent the pixel values of the two registered images at the coordinate (x, y); β(x, y) is an adaptive parameter; u1(x, y) and u2(x, y) respectively represent the means of the images I1 and I2 in the local region centered at the coordinate (x, y); σ 2 1(x, y) and σ 2 2(x, y) respectively represent the variances of the images I1 and I2 in the local region centered at the coordinate (x, y); w1(x, y) and w2(x, y) are weight functions.

6. The rural housing and land integrated right confirmation survey method according to claim 3, characterized in that: In step S24.1, the photometric consistency measure is calculated as follows: C = Σ n i＝1 w i Σ[g1(x1,y1) - g i (x i ,y i )] 2 ·{1 + a▽g1(x1,y1) / [max n j＝1 ▽ g j (x j ,y j )]}; (x,y) ∈ w; Σ n i＝1 w i = 1; where C is the photometric consistency measure; n is the number of images participating in the matching; w i is the adaptive weight factor of image I i ; w is a small window around the pixel point; g1(x1,y1) is the gray value at the pixel point (x1,y1) to be matched in the reference image I1; g i (x i ,y i ) is the pixel point (x i ,y i ) in the i-th image I i corresponding to the pixel point (x1,y1) in the reference image I1 through geometric relationship; a is an adjustment parameter; ▽g1(x1,y1) is the gradient amplitude of the image I1 at the point (x1,y1); ▽g j (x j ,y j ) is the gradient amplitude of the image I j at the point (x j ,y j ); max is the symbol for taking the maximum value.

7. The rural housing and land integrated right confirmation survey method according to claim 1, wherein: Step S4 includes the following steps: S41. Data Preparation: Collect geometric data, including the precise geographic location, shape, outline, and spatial dimensions of buildings and land properties. Collect ownership data, focusing on legal information on ownership and use rights. S42. Coordinate system unification: Use coordinate conversion algorithms to unify all data into the national geodetic coordinate system; S43, Spatial Matching: Using spatial indexing technology, quickly locate the corresponding relationship between geometric figures and ownership information in space; S44, attribute association: establishing a relationship based on a common identifier; S45, consistency check: Check whether the internal logical relationship between data is reasonable; perform data format consistency check to ensure that data from different sources are consistent in data type and encoding rules; S46. Accuracy Verification: For geospatial data, check whether the spatial location and shape of real estate are accurate. Utilize error analysis techniques to calculate the data deviation range and mark and correct data that exceeds the allowable error threshold. S47. Completeness check: Check whether there are missing values in the data set; by establishing a data integrity rule base, clarify the required fields and integrity requirements for each type of data to ensure that all key information is not missed; S48. Data storage: A storage solution that combines relational database and spatial database is used.

8. The rural housing and land integrated right confirmation survey method according to claim 1, characterized in that: The ownership investigation module includes: On-site investigation unit: Conduct on-site investigations through mobile terminal devices to collect ownership information of houses and land; Ownership information entry unit: enters the collected ownership information into the system and associates it with geographic information; Ownership information verification unit: Verify the entered ownership information to ensure the accuracy and completeness of the information.

9. The rural housing and land integrated right confirmation survey method according to claim 1, characterized in that: The tenure investigation module includes: On-site investigation unit: Conduct on-site investigations through mobile terminal devices to collect ownership information of houses and land; Ownership information entry unit: enters the collected ownership information into the system and associates it with geographic information; Ownership information verification unit: Verify the entered ownership information to ensure the accuracy and completeness of the information.

10. A rural real estate integrated right confirmation survey system, comprising: Data collection module, data processing module, ownership investigation module, data management and display module, data fusion module and ownership registration module; its characteristics are: Data acquisition module: Use drone-assisted photogrammetry to obtain high-resolution image data of rural areas, and satellite remote sensing technology to obtain auxiliary geographic information data; Data processing module: pre-processes the collected image data, including image correction, stitching, and generation of 3D real-scene models, and extracts geometric features of houses and land; Ownership Investigation Module: Integrates geographic information system (GIS) and database technology to investigate and record ownership information of houses and land, including information on the right holder, house use, and land nature; Data fusion module: integrates geometric data with ownership data to form a complete real estate data model; Title confirmation and registration module: Based on the integrated data model, it performs title confirmation and registration and generates an electronic title certificate; Data management and display module: stores, manages and visualizes property rights registration data, and supports data query, statistical analysis and dynamic updates.

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