Real estate surveying and mapping system and method based on geographic information system
By combining UAV mapping, laser measurement, GIS processing, positioning enhancement, and linkage verification units, the problems of multi-media mapping deviation, occluded area identification, and positioning accuracy in complex environments in road and bridge engineering have been solved. This has enabled efficient and accurate real estate mapping and ownership verification, adapting to complex environments and reducing costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANDONG MENGSHAN ROAD & BRIDGE CO LTD
- Filing Date
- 2026-01-19
- Publication Date
- 2026-05-12
AI Technical Summary
Existing technologies lack multi-media adaptable coordinate systems in road and bridge engineering, leading to significant deviations when merging laser measurement data and UAV imagery data from different media areas. This makes it impossible to accurately characterize the spatial boundaries and ownership scope of real estate across media. Dynamic obstructions create blind spots in mapping, and existing systems cannot automatically identify and trigger supplementary measurement processes, affecting the continuity and timeliness of mapping data. Positioning accuracy is difficult to maintain in complex environments, and existing systems rely on a single GNSS positioning method. The inability to link and verify measured data with historical registration data in real time leads to conflicts between ownership boundary mapping results and registration data, requiring extensive manual verification.
This system employs a combination of UAV mapping units, laser measurement units, GIS processing units, positioning enhancement units, and linkage verification units to achieve high-precision real estate mapping that is cross-media, interference-resistant, and adaptive. The UAV mapping unit adaptively adjusts measurement parameters according to the real estate type; the laser measurement unit identifies obstructions and triggers supplementary measurements; the positioning enhancement unit provides anti-interference multi-source positioning support; the data fusion and calibration unit achieves coordinate unification and timestamp synchronization of cross-media data; the GIS processing unit manages data processing and mapping result generation; and the linkage verification unit enables real-time data verification and identification of ownership boundary conflicts.
By using multi-media coordinate transformation and timestamp synchronization, the deviation of cross-media real estate measurement data fusion is reduced, obscured areas are automatically identified and remeasured, the integrity of surveying data is improved, the positioning accuracy is maintained within 10 centimeters, the real-time verification and conflict identification time is shortened to the minute level, the cost is reduced, the surveying efficiency and accuracy are improved, it can adapt to complex environments and support integrated indoor and outdoor surveying.
Smart Images
Figure CN122015784A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of geographic surveying and mapping technology, and more specifically, to a real estate surveying and mapping system and method based on geographic information systems. Background Technology
[0002] Currently, with the rapid advancement of infrastructure construction in my country, the demand for real estate surveying is becoming increasingly diversified, especially for real estate surveying around road and bridge projects, which faces numerous challenges such as complex terrain, ambiguous ownership boundaries, and harsh surveying environments. At present, integrated surveying solutions based on Geographic Information Systems (GIS), laser surveying instruments, and UAV technology have gradually replaced traditional manual surveying; however, many technical bottlenecks still exist in practical applications.
[0003] Existing technologies primarily focus on mapping real estate in single-medium land or water areas. For cross-medium real estate projects such as bridge-attached waterways, dam connection areas, and areas linking shallow underground pipelines with surface buildings, which are common in road and bridge engineering, there is a lack of targeted mapping solutions. This leads to coordinate system deviations in measurement data across different media areas, making data fusion difficult and compromising accuracy. Furthermore, in dynamic construction scenarios, factors such as temporary structures obstructing the view and construction vibrations can easily cause gaps in mapping data. Existing systems cannot automatically identify and accurately remeasure obstructed areas, requiring manual re-entry, which is inefficient and costly.
[0004] Furthermore, existing surveying systems often use fixed settings for measurement parameters such as laser scanning frequency, UAV flight altitude, and data sampling intervals. These settings cannot be adaptively adjusted based on the physical characteristics of different types of real estate, such as bridge piers, water embankments, and old buildings. This results in insufficient accuracy in identifying small structures like road and bridge embedded parts and embankment cracks, or low surveying efficiency for large open areas. In complex signal environments, areas with strong electromagnetic interference around road and bridge projects, and dense forests, GNSS positioning signals are easily blocked or interfered with. Existing systems rely on a single positioning method, making it difficult to maintain stable, high-precision positioning and affecting the reliability of surveying results.
[0005] Furthermore, existing technologies fail to achieve real-time linkage and calibration between surveying data and real estate registration data. Surveying results require multiple rounds of manual verification to meet registration requirements. This is particularly problematic in areas where collective land and state-owned construction land intersect in road and bridge projects, where surveying data on ownership boundaries easily conflict with historical registration data. The lack of an automated boundary calibration mechanism leads to low efficiency in confirming property rights. These issues restrict the effectiveness of real estate surveying in road and bridge projects and related fields, and existing technologies have not yet offered effective solutions.
[0006] For the common scenarios of cross-medium real estate in road and bridge engineering, including land, water, and shallow underground, the existing technology lacks a multi-medium adaptable coordinate system mechanism, which leads to significant deviations when laser measurement data and UAV image data are fused in different media areas, making it impossible to accurately characterize the spatial boundaries and ownership scope of cross-medium real estate.
[0007] During road and bridge construction, dynamic obstructions such as temporary barriers and construction machinery can easily create blind spots in surveying. Existing systems cannot automatically identify obstructed areas and trigger supplementary surveying processes, resulting in gaps in surveying data. Furthermore, the timestamps of the supplementary survey data do not match those of the original data, affecting the continuity and timeliness of the results.
[0008] The physical characteristics of bridge ancillary buildings, water embankments, and old houses vary significantly in terms of surface reflectivity, structural complexity, and spatial scale. Existing systems use fixed measurement parameters, which leads to oversampling of the high-reflectivity bridge steel structure and undersampling of the low-reflectivity embankment vegetation area, resulting in a poor balance between accuracy and efficiency.
[0009] High-voltage lines and large machinery around road and bridge projects generate strong electromagnetic interference, or dense forests block GNSS signals. Existing systems rely on a single GNSS positioning method, which cannot maintain stable centimeter-level positioning accuracy. This causes the spatial coordinate reference of laser measurement and UAV aerial survey to drift, affecting the reliability of surveying results.
[0010] In road and bridge ancillary areas where collective land and state-owned construction land meet, existing technology cannot link and verify measured data with historical registration data in real time. Conflicts are likely to occur between the survey results of ownership boundaries and the registration data, requiring extensive manual verification, resulting in low efficiency in confirming ownership and a high risk of disputes.
[0011] In summary, at least one of the following technical problems exists:
[0012] For the common scenarios of cross-medium real estate in road and bridge engineering, including land, water, and shallow underground, the existing technology lacks a multi-medium adaptable coordinate system mechanism, which leads to significant deviations when laser measurement data and UAV image data are fused in different media areas, making it impossible to accurately characterize the spatial boundaries and ownership scope of cross-medium real estate.
[0013] During road and bridge construction, dynamic obstructions such as temporary barriers and construction machinery can easily create blind spots in surveying. Existing systems cannot automatically identify obstructed areas and trigger supplementary surveying processes, resulting in gaps in surveying data. Furthermore, the timestamps of the supplementary survey data do not match those of the original data, affecting the continuity and timeliness of the results.
[0014] The physical characteristics of bridge ancillary buildings, water embankments, and old houses vary significantly in terms of surface reflectivity, structural complexity, and spatial scale. Existing systems use fixed measurement parameters, which leads to oversampling of the high-reflectivity bridge steel structure and undersampling of the low-reflectivity embankment vegetation area, resulting in a poor balance between accuracy and efficiency.
[0015] High-voltage lines and large machinery around road and bridge projects generate strong electromagnetic interference, or dense forests block GNSS signals. Existing systems rely on a single GNSS positioning method, which cannot maintain stable centimeter-level positioning accuracy. This causes the spatial coordinate reference of laser measurement and UAV aerial survey to drift, affecting the reliability of surveying results.
[0016] In road and bridge ancillary areas where collective land and state-owned construction land meet, existing technology cannot link and verify measured data with historical registration data in real time. Conflicts are likely to occur between the survey results of ownership boundaries and the registration data, requiring extensive manual verification, resulting in low efficiency in confirming ownership and a high risk of disputes. Summary of the Invention
[0017] The main objective of this invention is to provide a real estate surveying system based on geographic information system and its usage method, so as to solve at least one technical problem in the background art.
[0018] To achieve the above objectives, according to one aspect of the present invention, a real estate surveying and mapping system based on a geographic information system is provided, comprising: an unmanned aerial vehicle (UAV) surveying unit, a laser measurement unit, a GIS processing unit, a data fusion calibration unit, a positioning enhancement unit, and a linkage verification unit. These units cooperate to achieve cross-media, interference-resistant, and adaptive high-precision real estate surveying and mapping. The UAV surveying unit is responsible for acquiring large-scale aerial images and laser point clouds, and adaptively adjusting measurement parameters according to the real estate type. The laser measurement unit performs high-precision ground boundary point measurement and supplementary measurement, and can identify obstructions in the measurement path and trigger supplementary measurement signals. The positioning enhancement unit provides interference-resistant multi-source positioning support for the entire system to ensure continuous stability of positioning in complex environments. The data fusion calibration unit achieves coordinate unification and timestamp synchronization of cross-media data, thereby correcting data fusion deviations. The GIS processing unit undertakes the core tasks of geographic information management, data processing, and surveying and mapping result generation. The linkage verification unit achieves real-time linkage verification of surveying and mapping data and real estate registration data, and identifies ownership boundary conflicts.
[0019] Preferably, the UAV mapping unit includes a multi-rotor UAV platform, a long-range lidar, a visible light camera, an IMU inertial measurement module, a high-precision positioning module, and an adaptive parameter adjustment module. The multi-rotor UAV platform can carry various measurement payloads to achieve preset flight routes and dynamic obstacle avoidance to adapt to complex operating environments. The long-range lidar supports specific scanning modes, obtains ground point clouds by penetrating vegetation, and accurately captures small ground objects. The IMU inertial measurement module and the high-precision positioning module work together to ensure attitude stability and positioning accuracy during flight. At the same time, the adaptive parameter adjustment module has a built-in preset parameter library and automatically adjusts the laser scanning frequency, camera exposure time, and flight altitude according to the type of real estate to achieve dynamic adaptation of measurement parameters.
[0020] Preferably, the laser measurement unit includes a ground-based laser total station, a handheld laser rangefinder, and an obstruction detection sensor. The ground-based laser total station has high-precision ranging accuracy and supports angle measurement and direct coordinate output, enabling high-precision supplementary measurements for obstructed areas that cannot be covered by the drone. The handheld laser rangefinder, with its portability, enables the measurement of indoor real estate dimensions and achieves indoor and outdoor data fusion. The obstruction detection sensor is installed on both the drone and the total station, respectively, and uses laser ranging to identify obstructions and trigger supplementary measurement signals, ensuring the data integrity of the air-ground collaborative mapping.
[0021] Preferably, the positioning enhancement unit includes a GNSS reference station and an inertial navigation blind spot compensation module. The GNSS reference station is fixed at a known coordinate point and continuously transmits differential signals to provide a unified positioning reference for the UAV and ground measurement equipment. When the GNSS signal is lost or interfered with, the inertial navigation blind spot compensation module is automatically activated to achieve short-term positioning compensation based on IMU data and a preset trajectory, ensuring the continuity of the entire measurement process.
[0022] Preferably, the data fusion calibration unit includes a multi-media coordinate transformation module and a timestamp synchronization module. The multi-media coordinate transformation module presets coordinate system transformation parameters for land, water, and shallow underground, and achieves coordinate unification of measurement data from different media through coordinate transformation. The timestamp synchronization module adds a unified timestamp to the multi-source acquisition data of each unit to ensure the synchronization of multi-source data in the time dimension and improve the accuracy of data fusion.
[0023] Preferably, the GIS processing unit includes a GIS server, a data processing workstation, and a visualization terminal. The GIS server is equipped with a custom real estate surveying and mapping geographic information database for storing high-precision topographic data, ownership registration data, and historical surveying and mapping results. The data processing workstation can classify, denoise, and output multi-format results of laser point clouds. The visualization terminal can display the surveying and mapping progress, data quality, and result preview in real time, and also supports operators to remotely control various surveying devices to perform simultaneous measurement and review operations.
[0024] Preferably, the linkage verification unit includes an ownership data interface module and a boundary conflict verification module. The ownership data interface module can connect to the real estate registration system and retrieve historical ownership registration data in real time. The boundary conflict verification module uses a geometric comparison method to compare the measured data with the historical registration data, automatically identify boundary conflict areas and output a conflict report, providing direct evidence for ownership confirmation.
[0025] Preferably, the standard conversion parameters are used to unify the coordinates of measurement data from different media through a seven-parameter conversion method, correcting cross-media fusion deviations; the timestamp synchronization module is used to add a unified timestamp to the data collected by UAVs, total stations, and base stations, ensuring that multi-source data are synchronized in the time dimension and improving fusion accuracy.
[0026] Preferably, the ownership data interface module retrieves historical ownership registration data of the target area in real time from the real estate registration system, and the boundary conflict verification module overlays and compares the measured boundary point data with the historical registration data, automatically identifies and marks the boundary conflict areas, and outputs a conflict report to provide a basis for confirming ownership.
[0027] According to another aspect of the present invention, a method for using a real estate surveying system based on a geographic information system is provided, comprising:
[0028] The basic geographic data and ownership registration data of the survey area are loaded through the GIS processing unit, the survey area is divided into land, water and shallow underground zones, and the initial measurement parameters of each zone are set; GNSS reference stations are set up in the area and positioning calibration is completed.
[0029] The adaptive parameter adjustment module retrieves matching laser scanning frequency, flight altitude, and camera exposure parameters from the preset parameter library based on the real estate type (bridge, dam, residential building) of each zone, and sends them to the UAV mapping unit.
[0030] The drone flies along a preset route, simultaneously collecting point cloud and image data through lidar and visible light camera, while the IMU module collects attitude data in real time and the D-RTK module receives signals from the base station to achieve high-precision positioning. At the same time, the ground-based laser total station conducts supplementary measurements on the interior of buildings and the bottom of bridges in areas not covered by the drone. The occlusion detection sensor monitors the occlusion situation in real time, and if occlusion is detected, it triggers the drone's flight path adjustment or ground supplementary measurement.
[0031] In areas with strong electromagnetic interference or signal obstruction, the positioning enhancement unit automatically switches to inertial navigation blind spot compensation mode, combining IMU data with preset trajectory to maintain positioning accuracy. After the GNSS signal is restored, it automatically switches back to differential positioning mode to ensure continuous positioning.
[0032] The data fusion calibration unit unifies the time reference of multi-source data through the timestamp synchronization module, completes the coordinate unification of data from different partitions using the multi-media coordinate transformation module, performs noise reduction and classification processing on laser point clouds, and fuses them with visible light images to generate a three-dimensional point cloud model.
[0033] The linkage verification unit retrieves historical ownership registration data and performs boundary conflict verification with the measured data, marking conflict areas; the GIS processing unit generates orthophoto maps, 3D models, and parcel maps based on the calibrated data, stores them in the geographic information database, and outputs them.
[0034] The technical solution of this invention has the following technical effects:
[0035] Through multi-media coordinate transformation and timestamp synchronization mechanisms, the fusion deviation of cross-media real estate measurement data is reduced, accurately characterizing the real estate boundaries of the land-water-shallow underground connecting area, and meeting the accurate title requirements of road and bridge engineering ancillary real estate.
[0036] The dynamic occlusion adaptive supplementary survey strategy enables automatic identification and supplementary surveying of occluded areas, improves the integrity of surveying data, eliminates the need for manual secondary site visits for supplementary surveying, improves work efficiency, and reduces surveying costs in road and bridge construction scenarios.
[0037] The adaptive parameter adjustment module enables precise matching of measurement parameters with real estate types, improves the effective data rate of laser point clouds, and achieves millimeter-level accuracy in identifying small features such as bridge embedded parts and dam cracks. At the same time, it avoids redundant data caused by oversampling and improves data processing efficiency.
[0038] The dual-mode positioning strategy of GNSS and inertial navigation maintains positioning accuracy within 10 centimeters in areas with strong electromagnetic interference and signal obstruction, improves positioning continuity, solves the positioning drift problem in complex environments around road and bridge projects, and ensures the reliability of surveying and mapping results.
[0039] The linkage verification unit enables real-time conflict verification between measured data and registered data, reducing conflict identification time from several hours to minutes, reducing the workload of manual verification, effectively avoiding ownership disputes, and meeting the needs of high-efficiency real estate registration.
[0040] The system is capable of withstanding harsh environments and can adapt to complex scenarios such as mountainous areas, water areas, and construction areas in road and bridge engineering. It also supports integrated indoor and outdoor surveying and mapping, and its application scope covers most real estate surveying and mapping scenarios. Attached Figure Description
[0041] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:
[0042] Figure 1 A schematic diagram of a real estate surveying system based on a geographic information system according to the present invention is shown.
[0043] Figure 2 A schematic diagram of a method for a real estate surveying system based on a geographic information system according to the present invention is shown;
[0044] Figure 3 A schematic diagram of an unmanned aerial vehicle (UAV) mapping unit of a real estate mapping system based on a geographic information system according to the present invention is shown.
[0045] Figure 4 A schematic diagram of a GIS processing unit of a real estate surveying system based on a geographic information system according to the present invention is shown.
[0046] Figure 5 A schematic diagram of a data fusion calibration unit for a real estate surveying system based on a geographic information system according to the present invention is shown.
[0047] Figure 6 A schematic diagram of the linkage calibration unit of the real estate surveying system based on the geographic information system according to the present invention is shown.
[0048] The above figures include the following reference numerals:
[0049] Unmanned aerial vehicle (UAV) mapping unit 1; multi-rotor UAV platform 1-1; laser mapping radar 1-2; camera 1-3; inertial measurement module 1-4; high-precision positioning module 1-5;
[0050] GIS processing unit 2; GIS server 2-1; data processing workstation 2-2; visualization terminal 2-3;
[0051] Data fusion calibration unit 3; multi-media coordinate transformation module 3-1; timestamp synchronization module 3-2;
[0052] Linkage verification unit 4; ownership data interface module 4-1; boundary conflict verification module 4-2. Detailed Implementation
[0053] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.
[0054] like Figure 1-6As shown in the figure, this embodiment of the invention provides a real estate surveying and mapping system based on geographic information system, including a UAV surveying and mapping unit 1, a laser measurement unit, a GIS processing unit 2, a data fusion calibration unit 3, a positioning enhancement unit, and a linkage verification unit 4. Each unit works together to achieve cross-media, anti-interference, and adaptive high-precision real estate surveying and mapping. The UAV surveying and mapping unit 1 is responsible for acquiring large-scale aerial images and laser point clouds, and adaptively adjusting measurement parameters according to the real estate type. The laser measurement unit completes the measurement and supplementary measurement of high-precision boundary points on the ground, and can identify obstructions in the measurement path and trigger supplementary measurement signals. The positioning enhancement unit provides anti-interference multi-source positioning support for the entire system to ensure the continuous stability of positioning in complex environments. The data fusion calibration unit 3 realizes the coordinate unification and timestamp synchronization of cross-media data, thereby correcting data fusion deviations. The GIS processing unit 2 undertakes the core tasks of geographic information management, data processing, and surveying and mapping result generation. The linkage verification unit 4 realizes real-time linkage verification of surveying and mapping data and real estate registration data and identifies ownership boundary conflicts.
[0055] In this embodiment, the UAV mapping unit 1 includes a multi-rotor UAV platform 1-1, a long-range lidar 1-2, a visible light camera 1-3, an IMU inertial measurement module 1-4, a high-precision positioning module 1-5, and an adaptive parameter adjustment module. The multi-rotor UAV platform 1-1 can carry various measurement loads to achieve preset flight routes and dynamic obstacle avoidance to adapt to complex working environments. The long-range lidar 1-2 supports specific scanning modes, obtains ground point clouds by penetrating vegetation, and accurately captures small ground objects. The IMU inertial measurement module 1-4 and the high-precision positioning module 1-5 work together to ensure attitude stability and positioning accuracy during flight. At the same time, the adaptive parameter adjustment module has a built-in preset parameter library and automatically adjusts the laser scanning frequency, camera exposure time, and flight altitude according to the type of real estate to achieve dynamic adaptation of measurement parameters. This improves the flexibility and data acquisition accuracy of mapping operations in complex scenarios, significantly shortens the field operation cycle, and reduces the cost of manual intervention.
[0056] In this embodiment, the laser measurement unit includes a ground-based laser total station, a handheld laser rangefinder, and an occlusion detection sensor. The ground-based laser total station has high-precision ranging accuracy and supports angle measurement and direct coordinate output, enabling high-precision supplementary measurements for occluded areas that cannot be covered by the drone. The handheld laser rangefinder, with its portability, enables the measurement of indoor real estate dimensions and achieves indoor-outdoor data fusion. The occlusion detection sensor is installed on both the drone and the total station. It identifies occlusions through laser ranging and triggers supplementary measurement signals, ensuring the data integrity of air-ground collaborative mapping. This effectively solves the problems of missing data in occluded areas and data disconnect between indoor and outdoor areas in traditional mapping, achieving full-area mapping coverage without blind spots and providing complete data source support for subsequent data fusion.
[0057] In this embodiment, the positioning enhancement unit includes a GNSS reference station and an inertial navigation blind spot compensation module. The GNSS reference station is fixed at a known coordinate point and continuously transmits differential signals to provide a unified positioning reference for the UAV and ground measurement equipment. When the GNSS signal is lost or interfered with, the inertial navigation blind spot compensation module is automatically activated to achieve short-term positioning compensation based on IMU data and a preset trajectory, ensuring the continuity of the entire measurement process, significantly improving the positioning reliability in complex electromagnetic environments and obstructed areas, avoiding rework in surveying and mapping due to positioning interruption, and ensuring the efficient advancement of surveying and mapping operations and data continuity.
[0058] In this embodiment, the data fusion calibration unit 3 includes a multi-media coordinate transformation module 3-1 and a timestamp synchronization module 3-2. The multi-media coordinate transformation module 3-1 presets coordinate system transformation parameters for land, water, and shallow underground, and achieves coordinate unification of measurement data from different media through coordinate transformation. The timestamp synchronization module 3-2 adds a unified timestamp to the multi-source data collected by each unit, ensuring the synchronization of multi-source data in the time dimension, improving the accuracy of data fusion, effectively eliminating the spatiotemporal deviation of cross-media and cross-device data collection, and providing high-precision and highly consistent fused data for subsequent GIS processing and ownership verification.
[0059] In this embodiment, the GIS processing unit 2 includes a GIS server 2-1, a data processing workstation 2-2, and a visualization terminal 2-3. The GIS server 2-1 is equipped with a custom real estate surveying and mapping geographic information database for storing high-precision terrain data, ownership registration data, and historical surveying and mapping results. The data processing workstation 2-2 can classify, denoise, and output multi-format results of laser point clouds. The visualization terminal 2-3 can display the surveying and mapping progress, data quality, and result preview in real time. It also supports operators to remotely control various surveying equipment and conduct simultaneous measurement and review operations, thereby achieving real-time management, efficient processing, and rapid output of surveying and mapping data, significantly improving the closed-loop efficiency of the work process, and reducing the rework rate of results.
[0060] In this embodiment, the linkage verification unit 4 includes a property rights data interface module 4-1 and a boundary conflict verification module 4-2. The property rights data interface module 4-1 can connect to the real estate registration system and retrieve historical property rights registration data in real time. The boundary conflict verification module 4-2 uses a geometric comparison method to compare the measured data with the historical registration data, automatically identify boundary conflict areas and output conflict reports, providing direct evidence for property rights confirmation, realizing efficient linkage verification of measured data and property rights registration data, accurately locating boundary dispute points, providing technical support for real estate rights confirmation and registration work, and improving the efficiency and accuracy of rights confirmation.
[0061] In this embodiment, the coordinate system transformation parameters are used to unify the coordinates of measurement data from different media through a seven-parameter transformation method, correcting cross-media fusion deviations. The timestamp synchronization module 3-2 is used to add a unified timestamp to the data collected by UAVs, total stations, and base stations, ensuring that multi-source data are synchronized in the time dimension, improving fusion accuracy, further optimizing the accuracy of cross-media data fusion, eliminating systematic errors in the time and space dimensions, and providing core technical support for high-precision surveying and mapping output.
[0062] In this embodiment, the ownership data interface module 4-1 connects to the real estate registration system to retrieve historical ownership registration data of the target area in real time. The boundary conflict verification module 4-2 overlays and compares the measured boundary point data with the historical registration data, automatically identifies and marks boundary conflict areas, and outputs a conflict report to provide a basis for ownership confirmation. This achieves seamless connection and intelligent verification between ownership data and measured data, reduces the workload of manual comparison, and improves the accuracy and standardization of boundary ownership confirmation.
[0063] In this embodiment, adaptive parameter matching is implemented based on the correspondence between real estate types and the measurement environment. A preset parameter library is constructed, containing the mapping relationship between the physical characteristics of different types of real estate and the optimal measurement parameters. The adaptive parameter adjustment module automatically matches measurement parameters by identifying the real estate types in the survey area, avoiding oversampling or undersampling problems caused by fixed parameters. For high-reflectivity bridge steel structures, the laser scanning frequency is automatically increased to ensure detail capture; for low-reflectivity embankment vegetation areas, a cross-scanning mode is used to increase point cloud density while reducing flight altitude to enhance laser penetration. The cross-media data fusion principle is applied to the land-water-shallow underground cross-media scenario. The multi-media coordinate transformation module 3-1 presets seven parameters for coordinate system transformation of different media. Based on the Bursa-Wolf model, parameter verification and correction are performed using common control points, embankment corners, and bridge piers within the measurement area, unifying the measurement data of different media into the national geodetic coordinate system and eliminating cross-media fusion bias. The timestamp synchronization module 3-2 uses GNSS second pulse signals to synchronize the time of the UAV, total station, and base station, ensuring consistency of multi-source data in both spatial and temporal dimensions and improving fusion accuracy. Anti-interference positioning is enhanced by employing a dual-mode positioning strategy of "GNSS differential positioning + inertial navigation blind spot compensation." Under normal circumstances, centimeter-level positioning is achieved through differential signals emitted by the GNSS base station. When the GNSS signal strength is detected to be below a threshold, or in areas with strong electromagnetic interference or dense forest, the inertial navigation blind spot compensation module automatically activates. Based on the angular and linear acceleration data collected by the IMU inertial measurement module 1-4, the position of the UAV and measuring equipment is calculated using a Kalman filter algorithm to maintain short-term positioning accuracy. After the GNSS signal recovers, the accumulated deviation is corrected by fusing the two positioning data to ensure continuous and stable positioning. Dynamic occlusion compensation is achieved by an occlusion identification sensor that monitors obstacles, construction machinery, and temporary barriers on the measurement path in real time using laser ranging. When the measured distance is less than a preset threshold, it is identified as an occlusion area. For areas with aerial obstruction, the UAV automatically adjusts its flight path, increases its altitude, or detours and re-collects data. For ground-based obstruction, the system sends supplementary measurement commands to the ground-based laser total station to perform high-precision measurements of the boundary points and feature points of the obstructed area. The supplementary measurement data is synchronized through the timestamp synchronization module 3-2 and integrated into the overall dataset to avoid data gaps. The ownership linkage verification principle involves the linkage verification unit 4 connecting to the real estate registration system via a standardized interface to retrieve historical ownership data, boundary point coordinates, and ownership boundary lines for the target area in real time. A geometric overlay comparison method is used to overlay the measured boundary point data with historical registration data, calculate the boundary overlap, and mark areas below a preset threshold as conflict areas, outputting deviation values to provide intuitive evidence for ownership confirmation and reduce manual verification workload.
[0064] In this embodiment, a cross-media multi-reference calibration mechanism is pioneered, employing a coordinate system transformation strategy based on a multi-media coordinate transformation module 3-1 verified by common control points. This module pre-defines dedicated transformation parameters for land-water-shallow subsurface areas and combines them with the synchronization technology of the timestamp synchronization module 3-2. This solves the unconventional problem of large deviations in cross-media real estate data fusion, overcoming the limitation of existing technologies that are only applicable to single-media mapping. A parameter self-adjustment system adapted to real estate types constructs a pre-defined parameter library based on the physical characteristics of the real estate, enabling automatic matching of parameters such as laser scanning frequency and flight altitude. This solves the problem that fixed parameters cannot simultaneously address the measurement accuracy and efficiency of multiple types of real estate, improving the system's scenario adaptability. A dynamic occlusion adaptive supplementary measurement strategy integrates air-to-ground occlusion recognition and trajectory adaptive adjustment technologies to achieve automatic supplementary measurement and seamless data fusion of occluded areas. This addresses the unconventional pain point of data discontinuity in dynamic construction scenarios, improving operational efficiency in complex environments. Anti-interference dual-mode positioning enhancement architecture: Adopting a dual-mode design of "GNSS differential + inertial navigation blind spot compensation," it achieves smooth switching and deviation correction between the two positioning data through Kalman filtering, solving the positioning stability problems in areas with strong electromagnetic interference and signal obstruction, and ensuring the surveying accuracy in complex environments surrounding road and bridge projects. Surveying-registration linkage verification mechanism: Through the standardized interface of the ownership data interface module 4-1, it connects to the real estate registration system, using a geometric overlay comparison method to achieve real-time conflict verification between measured data and historical registration data. This solves the problems of low efficiency and easy conflict in ownership boundary verification, breaking down the process barriers between surveying and ownership confirmation.
[0065] Another embodiment of the present invention provides a method for using a real estate surveying and mapping system based on a geographic information system, comprising:
[0066] The GIS processing unit 2 loads the basic geographic data and ownership registration data of the survey area, divides the survey area into land, water and shallow underground zones, and sets the initial measurement parameters for each zone; GNSS reference stations are set up in the area to complete the positioning calibration.
[0067] The adaptive parameter adjustment module retrieves matching laser scanning frequency, flight altitude, and camera exposure parameters from the preset parameter library based on the real estate type (bridge, dam, residential building) of each zone, and sends them to the UAV mapping unit 1.
[0068] The UAV flies along a preset route, synchronously collecting point cloud and image data through LiDAR 1-2 and visible light camera 1-3. IMU inertial measurement module 1-4 collects attitude data in real time, and D-RTK module (high-precision positioning module 1-5) receives signals from the base station to achieve high-precision positioning. At the same time, ground laser total station performs supplementary measurements on the interior of buildings and the bottom of bridges in areas not covered by the UAV. The occlusion recognition sensor monitors the occlusion situation in real time. If occlusion is detected, it triggers UAV trajectory adjustment or ground supplementary measurement.
[0069] In areas with strong electromagnetic interference or signal obstruction, the positioning enhancement unit automatically switches to the inertial navigation blind spot compensation mode, combining the data from IMU inertial measurement modules 1-4 with the preset trajectory to maintain positioning accuracy. After the GNSS signal is restored, it automatically switches back to the differential positioning mode to ensure continuous positioning.
[0070] The data fusion calibration unit 3 unifies the time reference of multi-source data through the timestamp synchronization module 3-2, uses the multi-media coordinate transformation module 3-1 to unify the coordinates of data from different partitions, performs noise reduction and classification processing on the laser point cloud, and fuses it with visible light images to generate a three-dimensional point cloud model.
[0071] Linkage verification unit 4 retrieves historical ownership registration data, performs boundary conflict verification with measured data, and marks conflict areas; GIS processing unit 2 generates orthophoto maps, 3D models, parcel maps, and other results based on the calibrated data, stores them in the geographic information database of GIS server 2-1, and outputs them.
[0072] As can be seen from the above description, the embodiments of the present invention achieve the following technical effects:
[0073] Through the coordinate transformation of the multi-media coordinate transformation module 3-1 and the time stamp synchronization mechanism of the timestamp synchronization module 3-2, the fusion deviation of cross-media real estate measurement data is reduced, the real estate boundary of the shallow underground connection area of land and water is accurately characterized, and the accurate title requirements of the ancillary real estate of road and bridge engineering are met.
[0074] The dynamic occlusion adaptive supplementary survey strategy enables automatic identification and supplementary surveying of occluded areas, improves the integrity of surveying data, eliminates the need for manual secondary site visits for supplementary surveying, improves work efficiency, and reduces surveying costs in road and bridge construction scenarios.
[0075] The adaptive parameter adjustment module enables precise matching of measurement parameters with real estate type, improves the effective data rate of laser point cloud of LiDAR 1-2, and achieves millimeter-level accuracy in identifying small land features such as bridge embedded parts and dam cracks. At the same time, it avoids redundant data caused by oversampling, and improves the data processing efficiency of data processing workstation 2-2.
[0076] The dual-mode positioning strategy of GNSS and inertial navigation maintains positioning accuracy within 10 centimeters in areas with strong electromagnetic interference and signal obstruction, improves positioning continuity, solves the positioning drift problem in complex environments around road and bridge projects, and ensures the reliability of the surveying results of UAV surveying unit 1.
[0077] The linkage verification unit 4 realizes real-time conflict verification between measured data and registered data. The conflict identification time of the boundary conflict verification module 4-2 is shortened from several hours to minutes, reducing the workload of manual verification, effectively avoiding ownership disputes, and adapting to the high-efficiency requirements of real estate registration.
[0078] The system of GIS processing unit 2 has the ability to resist harsh environments and can adapt to complex scenarios such as mountainous areas, water areas and construction areas in road and bridge engineering. At the same time, it supports integrated indoor and outdoor surveying and mapping, and its applicable scope covers most real estate surveying and mapping scenarios.
[0079] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A real estate surveying and mapping system based on geographic information system, characterized in that, The system comprises an UAV mapping unit, a laser measurement unit, a GIS processing unit, a data fusion calibration unit, a positioning enhancement unit, and a linkage verification unit. These units work together to achieve high-precision real estate mapping that is cross-media, interference-resistant, and adaptive. The UAV mapping unit is responsible for acquiring large-scale aerial images and laser point clouds, and adaptively adjusting measurement parameters according to the type of real estate. The laser measurement unit performs high-precision measurement and supplementary measurement of ground boundary points, and can identify obstructions in the measurement path and trigger supplementary measurement signals. The positioning enhancement unit provides multi-source positioning support to the entire system to ensure continuous stability of positioning in complex environments. The data fusion calibration unit unifies coordinates and synchronizes timestamps for cross-media data, thereby correcting data fusion deviations. The GIS processing unit undertakes the core tasks of geographic information management, data processing, and mapping result generation. The linkage verification unit realizes real-time linkage verification between mapping data and real estate registration data and identifies ownership boundary conflicts.
2. The real estate surveying and mapping system based on geographic information system as described in claim 1, characterized in that, The UAV mapping unit includes a multi-rotor UAV platform, a long-range lidar, a visible light camera, an IMU inertial measurement module, a high-precision positioning module, and an adaptive parameter adjustment module. The multi-rotor UAV platform can carry various measurement payloads to achieve preset flight paths and dynamic obstacle avoidance to adapt to complex operating environments. The long-range lidar supports specific scanning modes, obtains ground point clouds by penetrating vegetation, and accurately captures small ground objects. The IMU inertial measurement module and the high-precision positioning module work together to ensure attitude stability and positioning accuracy during flight. At the same time, the adaptive parameter adjustment module has a built-in preset parameter library and automatically adjusts the laser scanning frequency, camera exposure time, and flight altitude according to the type of real estate to achieve dynamic adaptation of measurement parameters.
3. The real estate surveying and mapping system based on geographic information system as described in claim 1, characterized in that, The laser measurement unit includes a ground-based laser total station, a handheld laser rangefinder, and an obstruction detection sensor. The ground-based laser total station has high-precision ranging accuracy and supports angle measurement and direct coordinate output. It can perform high-precision supplementary measurements for obstructed areas that cannot be covered by drones. The handheld laser rangefinder, with its portability, can measure the dimensions of indoor real estate and achieve indoor and outdoor data fusion. The obstruction detection sensor is installed on both the drone and the total station. It uses laser ranging to identify obstructions and triggers supplementary measurement signals to ensure the data integrity of air-ground collaborative mapping.
4. The real estate surveying and mapping system based on geographic information system as described in claim 1, characterized in that, The positioning enhancement unit includes a GNSS reference station and an inertial navigation blind spot compensation module. The GNSS reference station is fixed at a known coordinate point and continuously transmits differential signals to provide a unified positioning reference for the UAV and ground measurement equipment. When the GNSS signal is lost or interfered with, the inertial navigation blind spot compensation module is automatically activated to achieve short-term positioning compensation based on IMU data and a preset trajectory, ensuring the continuity of the entire measurement process.
5. The real estate surveying and mapping system based on geographic information system as described in claim 1, characterized in that, The data fusion calibration unit includes a multi-media coordinate transformation module and a timestamp synchronization module. The multi-media coordinate transformation module presets coordinate system transformation parameters for land, water, and shallow underground, and achieves coordinate unification of measurement data from different media through coordinate transformation. The timestamp synchronization module adds a unified timestamp to the multi-source acquisition data of each unit to ensure the synchronization of multi-source data in the time dimension and improve the accuracy of data fusion.
6. The real estate surveying and mapping system based on geographic information system as described in claim 1, characterized in that, The GIS processing unit includes a GIS server, a data processing workstation, and a visualization terminal. The GIS server is equipped with a custom real estate surveying and mapping geographic information database, which is used to store high-precision terrain data, ownership registration data, and historical surveying and mapping results. The data processing workstation can complete the classification, noise reduction, and multi-format output of laser point clouds. The visualization terminal can display the surveying and mapping progress, data quality, and result preview in real time, and also supports operators to remotely control various surveying equipment to conduct simultaneous measurement and review operations.
7. The real estate surveying and mapping system based on geographic information system as described in claim 1, characterized in that, The linkage verification unit includes an ownership data interface module and a boundary conflict verification module. The ownership data interface module can connect to the real estate registration system and retrieve historical ownership registration data in real time. The boundary conflict verification module uses a geometric comparison method to compare the measured data with the historical registration data, automatically identify boundary conflict areas and output conflict reports, providing direct evidence for ownership confirmation.
8. The real estate surveying and mapping system based on geographic information system as described in claim 1, characterized in that, The standard system conversion parameters achieve coordinate unification of measurement data from different media through a seven-parameter conversion method, correcting cross-media fusion deviations. The timestamp synchronization module is used to add unified timestamps to the data collected by UAVs, total stations, and base stations, ensuring that multi-source data are synchronized in the time dimension and improving fusion accuracy.
9. The real estate surveying and mapping system based on geographic information system as described in claim 1, characterized in that, The ownership data interface module retrieves historical ownership registration data of the target area in real time from the real estate registration system. The boundary conflict verification module overlays and compares the measured boundary point data with the historical registration data, automatically identifies and marks boundary conflict areas, and outputs a conflict report to provide a basis for confirming ownership.
10. A method of using a geographic information system-based real estate surveying system, based on the geographic information system-based real estate surveying system according to any one of claims 1-9, characterized in that, include: The basic geographic data and ownership registration data of the survey area are loaded by the GIS processing unit, the survey area is divided into land, water and shallow underground zones, and the initial measurement parameters of each zone are set. Deploy GNSS reference stations within the area to complete positioning calibration; The adaptive parameter adjustment module retrieves matching laser scanning frequency, flight altitude, and camera exposure parameters from the preset parameter library based on the real estate type (bridge, embankment, residential building) of each zone, and sends them to the UAV mapping unit. The drone flies along a preset route, simultaneously collecting point cloud and image data through lidar and visible light camera, while the IMU module collects attitude data in real time and the D-RTK module receives signals from the base station to achieve high-precision positioning. At the same time, the ground-based laser total station conducts supplementary measurements on the interior of buildings and the bottom of bridges in areas not covered by the drone. The occlusion detection sensor monitors the occlusion situation in real time, and if occlusion is detected, it triggers the drone's flight path adjustment or ground supplementary measurement. In areas with strong electromagnetic interference or signal obstruction, the positioning enhancement unit automatically switches to inertial navigation blind spot compensation mode, combining IMU data with preset trajectory to maintain positioning accuracy. After the GNSS signal is restored, it automatically switches back to differential positioning mode to ensure continuous positioning. The data fusion calibration unit unifies the time reference of multi-source data through the timestamp synchronization module, completes the coordinate unification of data from different partitions using the multi-media coordinate transformation module, performs noise reduction and classification processing on laser point clouds, and fuses them with visible light images to generate a three-dimensional point cloud model. The linkage verification unit retrieves historical ownership registration data and performs boundary conflict verification with the measured data, marking the conflict area; The GIS processing unit generates orthophoto maps, 3D models, and parcel maps based on the calibrated data, stores them in the geographic information database, and outputs them.