70 results about "Topographic relief" patented technology

The invention relates to the technical field of image processing, in particular to an irrigation prescription map inversion method based on unmanned aerial vehicle spectrum data. The method comprises the following steps: carrying out unmanned aerial vehicle data acquisition on a farmland area to obtain a multispectral image, DEM data and meteorological interpolation data; performing micro-landform parameter calculation according to the multispectral image, DEM data and unmanned aerial vehicle GPS data, and constructing a terrain-spectrum coupling model to obtain a terrain relief degree map and the terrain-spectrum coupling model; performing vegetation multi-dimensional state analysis by using a terrain-spectrum coupling model to obtain a vegetation index map, a canopy temperature map and a water stress preliminary judgment map; performing net radiation calculation by using the meteorological interpolation data to obtain a net radiation diagram; according to the net radiation diagram, the vegetation index diagram and the topographic relief degree diagram, soil heat flux estimation is conducted, and a soil heat flux diagram is obtained. According to the method, collaborative inversion is carried out by eliminating the terrain effect, and the precision and reliability of irrigation prescription map inversion are improved.

The invention belongs to the technical field of landslide susceptibility analysis, and relates to a landslide susceptibility ensemble learning evaluation method considering spatial heterogeneity partitioning and factor feature screening, which comprises the following steps: generating a landslide sample based on historical landslide catalog data, and selecting a non-landslide sample through environmental factor frequency ratio analysis; the method comprises the following steps of: extracting static and dynamic environment factor data sets, realizing factor space interpretation force transformation by utilizing a t-SNE-ISO clustering algorithm and a feature screening strategy, eliminating high-correlation factors through a Pearson correlation coefficient method, quantifying interpretation force of each factor on landslide space differentiation by combining a geographic detector, screening optimal feature combinations under global and partition frameworks respectively, and performing landslide space differentiation on the landslide space. According to the method, a Stacking integrated learning framework is combined with CNN, DNN, MLP-based learners and LR element learners, a landslide susceptibility probability prediction model is formed, the generalization ability and prediction accuracy of the model are improved, and the method is especially suitable for landslide high-incidence areas with severe topographic relief and complex geological conditions.

The invention relates to the technical field of data visualization, in particular to an earthquake emergency information rapid visualization method and system. The method comprises the following steps: acquiring earthquake monitoring real-time data; based on earthquake monitoring real-time data grid division, earthquake region population density distribution and key facility positions are generated, and an earthquake region sensitivity matrix is generated; dynamically simulating a seismic wave propagation process based on the seismic monitoring real-time data and the seismic region sensitivity matrix, and generating fluctuation propagation time sequence data; and identifying epicenter topography and landform vector data of the earthquake monitoring real-time data, and respectively evaluating topographic relief and geological stability to obtain earthquake topographic relief index data and earthquake geological stability score data. According to the invention, rapid and accurate assessment and visualization of earthquake damage distribution are realized through a data visualization technology, so that the earthquake emergency response efficiency and the disaster rescue decision scientificity are improved.

The invention discloses a garden landscape design method based on digital twinning, and belongs to the technical field of landscape garden design. According to the method, a target site environment data set is collected, and a site thermodynamic distribution cloud picture is generated through a geographic space grid processing unit; extracting a site space pattern feature matrix on the basis; constructing a landscape effect distribution model, decomposing the feature matrix into topographic relief, seasonal phase color and illumination reflection parameters, driving the model to generate a three-dimensional scene rendering sequence, embedding the model into a three-dimensional digital twin scene of the garden site, and associating the parameters with physical attributes of the twin scene in real time; dynamic optimization of the landscape scheme is executed, an element replacement instruction is generated, and the spatial topological relation network is updated; and outputting a site design scheme map, adjusting facility layout coordinates, and generating a final scheme data packet in combination with the path connectivity, so that the precision and adaptability of garden landscape design can be improved.

The invention relates to the field of natural resource right confirmation, and discloses a live-action three-dimensional construction method for right confirmation of a complex terrain area, and the method comprises the following steps: S1, carrying out the collaborative collection of multi-source data; s2, fusing the three-dimensional model and cadastral data; s3, automatically and intelligently extracting ownership boundaries; s4, dynamically binding and visualizing the ownership information; according to the method, the point cloud data and the image data are subjected to registration, de-noising and semantic segmentation processing, a real scene three-dimensional model with a layered cadastre label is constructed in combination with land block boundaries, ownership persons and land use type attributes in cadastre ownership investigation information, registration of the point cloud data and the image data is achieved through an improved ICP algorithm, and the registration accuracy of the point cloud data and the image data is improved. A weight coefficient, a regularization factor and a penalty matrix of point cloud curvature similarity are considered in a registration error function, the method is used for constraining registration distortion of a topographic relief area, and the improved registration algorithm can better adapt to characteristics of a complex topographic area and can effectively reduce registration errors especially in a place with large topographic relief.

The invention belongs to the technical field of digital terrain analysis and artificial intelligence crossing, and particularly discloses an automatic identification method and system for basins in DEM data based on a neural network. The method comprises the following steps: adaptively determining a topographic relief amplitude analysis window based on a mean value point change method, and extracting an initial plain area by combining elevation, gradient and topographic relief amplitude; noise is eliminated through morphological optimization (hole filling and edge smoothing), a buffer area is constructed, and key terrain factors such as elevation and gradient change rate are screened to generate multi-channel feature data; dividing landform types by using ISODATA dynamic clustering, and outputting binary basin data; an improved ResUNet + + network (integrating multiple encoders, a cavity space pyramid and a channel attention mechanism) is adopted for end-to-end training, and refined segmentation of a complex boundary is achieved. The method supports cross-regional generalization testing, can realize high-precision basin identification in drought and moist landform scenes, and can be widely applied to the fields of resource exploration, ecological protection and disaster prevention and control.

The invention provides a two-stage SAR (Synthetic Aperture Radar) image registration method based on SAR-SIFT (Scale Invariant Feature Transform) and template matching, which comprises a pre-registration stage and a fine registration stage, and is characterized in that in the pre-registration stage, a down-sampling image is calculated through an SAR-SIFT algorithm to obtain an initial registration image; updating the initial registration image to obtain a final matching pair; after the final matching pair is mapped back to the original image size, a pre-registration image is obtained according to the final affine transformation matrix; in the fine registration stage, stable feature points are extracted from the pre-registration image blocks, and mismatching pairs are eliminated to obtain a fine registration image; and judging whether a region with relatively large topographic relief exists through the clustering mismatching pair, sampling a candidate topographic relief region, and then carrying out local correction to obtain a fine registration image again. The SAR-SIFT algorithm and iterative optimization are utilized to realize initial registration, a similarity measurement method is utilized to realize accurate registration of a flat region, adaptive stretching transformation is utilized to transform a topographic relief region to realize accurate registration of the topographic relief region, and finally accurate registration of the SAR image is realized.

The invention relates to the technical field of robot navigation, and particularly provides a multi-layer map construction method for substation inspection navigation, which comprises the following steps: generating a point cloud map according to three-dimensional point cloud data of a substation, and marking passable areas, obstacle positions and road topological structures to obtain a geometric information layer; identifying the type of the equipment according to the image data of the equipment of the transformer substation, and generating a priority label of the equipment based on the type of the equipment to obtain an equipment information layer; according to the topographic relief degree matrix of the transformer substation, dividing the transformer substation environment into a flat area, a non-flat area and an obstacle area to obtain a topographic information layer; and carrying out space alignment on the geometric information layer, the equipment information layer and the topographic information layer to obtain a multi-layer map with a unified coordinate system. The problems that in the prior art, a transformer substation inspection map is single in information dimension, poor in reliability and not beneficial to inspection are solved.

The invention discloses a lunar rock abundance inversion method and system combining SAR and DEM data, a storage medium and an electronic device, and the method comprises the following steps: employing a reduced polarization Mini-RF SAR image, and extracting Stokes parameters and sub-parameters thereof; a polarization decomposition technology is applied to extract scattering mechanism characteristics, and various polarization decomposition components such as surface scattering, secondary scattering and volume scattering are obtained. And performing correlation evaluation on the multi-source characteristic parameters and DiViner rock abundance by applying a correlation analysis method, and screening out characteristic parameters with relatively high correlation with the rock abundance. And in combination with DEM data, terrain related parameters are calculated to represent topographic relief features. And an SSA optimized RF model is constructed, and SAR and DEM features are trained. The trained model is migrated and applied to a permanent shadow area of the South Pole of the moon, and a rock abundance distribution diagram of the PSR area is obtained through inversion. According to the method, the rock abundance inversion precision and stability in complex terrains and low-illumination areas are effectively improved, and reliable data support is provided for polar region landing point selection and resource evaluation.

The invention relates to a DSM reconstruction method, system and device based on airborne multi-angle InSAR, and the method comprises the steps: planning a multi-angle data obtaining task based on the topographic relief for a complex terrain of a measurement area, and obtaining a multi-angle route and radar parameters; performing multi-angle radar detection on the detection area based on the multi-angle air line and the radar parameters to obtain multi-angle SAR original echo data; carrying out data processing on the SAR original echo data at multiple angles by adopting an InSAR data processing method to obtain DSM data at multiple angles; and performing fusion processing on the DSM data at the multiple angles to obtain the DSM data of the complex terrain. According to the method, data acquisition tasks under multiple angles of topographic relief planning are considered, invalid DSM data caused by shadow and overlay can be avoided to the greatest extent, then high consistency of DSM data precision under each angle is ensured through InSAR data processing, and DSM high-precision reconstruction of a multi-angle InSAR system complex terrain is realized.

The invention discloses a farmland protection forest area windproof effect evaluation method based on a land-gas coupling model, and relates to the technical field of agricultural science, high-precision topographic data of a target farmland protection forest area is collected and preprocessed, and spatial distribution information of a farmland protection forest is obtained by combining remote sensing image interpretation; on the basis of topographic data and spatial distribution information, topographic relief and earth surface coverage features are analyzed, and a refined underlying surface classification system is constructed; and configuring high-resolution grid parameters of a WRF mode, and coupling a Noah-MP land surface process model. According to the farmland protection forest area wind-proof effect evaluation method based on the land-gas coupling model, through combination of high-resolution topographic data and a multi-source remote sensing image, topographic relief and surface heterogeneity of a farmland protection forest area can be accurately depicted, and a WRF mode is coupled with a Noah-MP land surface process model; fine simulation of complex terrains and various underlying surfaces is realized, the limitation of traditional uniform underlying surface hypothesis is overcome, and the accuracy of windproof effect evaluation is remarkably improved.

The invention discloses a laser wind-finding radar and a data processing and assimilation method thereof. The method comprises the following steps: acquiring observation data of the laser wind-finding radar, atmospheric stability parameters and topographic relief feature data; a three-dimensional space weight distribution function is constructed based on the parameters, and initial influence weights of the observation points on surrounding grids are represented; dynamically adjusting the horizontal influence range and the vertical attenuation characteristic of the function according to the geomorphic stratification degree and the atmospheric vertical stratification change information, and generating dynamic weight distribution matched with the local atmospheric physical and topographic characteristics; the distribution serves as a space adjustment factor of an observation error covariance matrix and is introduced into a cost function, and the wind field analysis field and the dynamic weight distribution are synchronously optimized by minimizing the cost function; and outputting the optimized wind field analysis field for numerical weather forecast. According to the method, accurate and efficient fusion of observation information in three-dimensional variational assimilation is realized, and the authenticity and forecast reliability of wind field analysis under complex conditions are remarkably improved.

The invention discloses terrain flatness measuring equipment for territorial planning space, and relates to the technical field of flatness measuring devices.The terrain flatness measuring equipment comprises a supporting disc, a vertically-arranged stand column is fixed to the supporting disc, the stand column is rotationally sleeved with a steering sleeve, a touch wheel is rotationally installed at the bottom of an elastic guide assembly, and canvas is wound around a cloth winding roller, a cloth unwinding roller and a guide roller; and a marking pen for marking on the canvas is mounted on the elastic guide assembly. According to the invention, the translation frame linearly moves relative to the extension frame, so that the trolley wheel can move up and down along with topographic fluctuation under the action of the elastic guide assembly, the arranged marking pen can mark on the canvas to measure the topographic flatness in a certain direction, and the steering sleeve drives the extension frame to rotate, so that the flatness of the topographic flatness in a certain direction can be measured. Switching adjustment of the moving direction of the trolley wheel is achieved, comprehensive and multi-directional terrain flatness measurement processing on a measurement area is greatly facilitated, and the terrain flatness measurement efficiency is effectively improved.

The invention discloses a gravity inversion method based on unstructured curved surface hexahedral mesh generation, and solves the problem that three-dimensional density structure characteristics in a complex terrain cannot be accurately calculated in the prior art. According to the method, a regular hexahedron is mapped into an unstructured curved surface hexahedron through isoparametric transformation, topographic relief is accurately fitted, forward modeling results of single curved surface hexahedron units are obtained, a global gravity field distribution model is generated through superposition, and three-dimensional gravity inversion is performed through a nonlinear conjugate gradient method. And acquiring a global optimal solution of the deep density parameter under the complex terrain, and finally testing effectiveness and accuracy through different models to acquire a clear and accurate high-precision geological underground structure model. Through the unstructured curved surface hexahedral mesh generation and nonlinear conjugate gradient inversion technology, the precision and efficiency of deep density parameter inversion under the complex terrain are remarkably improved, the accuracy of deep geophysical exploration is improved, and a reliable basis is provided for mineral resource exploration and geological structure analysis.

The invention discloses a topographic relief region tree height estimation method and system based on SAR scattering intensity correction. The method comprises the steps that the irradiation area of SAR image pixels in a geographic coordinate system is converted into an SAR pixel coordinate system through inverse distance weighting; calculating the SAR backscattering intensity after the irradiation area correction by combining the image SAR resolution and the irradiation area of the SAR image pixel in the SAR pixel coordinate system; the actual measurement forest height is used as priori knowledge, forest height estimation is carried out based on a random volume-earth surface model, an optimal fitting coefficient of the random volume-earth surface model is obtained by using a nonlinear regression analysis method, and then the forest height of a research area is obtained. According to the method, the problem of estimation deviation caused by insufficient compensation of the terrain effect in the rugged terrains such as hills and mountainous regions in the existing forest height estimation technology based on single SAR backscattering is solved, and large-range and high-robustness forest height mapping can be realized.

The invention discloses a land area measurement method and system for land planning and design, and the method comprises the steps: collecting a sequence aerial image of a target land parcel, and synchronously recording the positioning and attitude determination data of each frame of image; edge sharpening processing and multi-view three-dimensional reconstruction are carried out on the sequence aerial image, and a digital elevation model with texture features is generated; performing terrain gradient analysis and edge contour extraction based on a digital elevation model, and identifying a land block boundary through an adaptive threshold segmentation algorithm and generating an initial vector boundary diagram; performing topological relation verification and fragment pattern spot fusion on the initial vector boundary diagram, and outputting an optimized plot vector boundary; and carrying out coordinate registration on the optimized plot vector boundary and a geographic information system base map, calculating a projection area through integral operation, and generating a final measurement report containing a topographic relief coefficient. According to the embodiment of the invention, the operation efficiency, boundary recognition precision and result reliability of land area measurement under complex terrains can be improved.

This invention relates to the field of water conservancy engineering construction technology, and discloses a method and system for controlling blasting vibration during lock excavation. By collecting information on the geological hardness of rock strata and the distance to surrounding buildings, and combining this with finite element analysis to simulate the blasting energy distribution, a dynamic matching mechanism for borehole depth and charge quantity is constructed. This method overcomes the limitations of traditional empirical parameter settings and uses iterative simulation and optimization algorithms to achieve coordinated adjustment of borehole depth and charge quantity. For example, in areas with high rock hardness, by increasing the borehole depth and matching a segmented charge strategy, the blasting energy is uniformly released in the deep rock mass, effectively reducing the peak ground vibration and avoiding structural damage to adjacent buildings. Furthermore, by introducing multi-dimensional data analysis and multi-scenario simulation verification mechanisms, it can flexibly cope with complex working conditions such as uneven rock hardness, topographical undulations, adjacent water bodies, or dense building clusters.

The invention provides a mine operation repairing method based on an unmanned aerial vehicle and a related device, and relates to the technical field of mine repairing. Based on factors such as a topographic relief factor, a wind field turbulence intensity factor, an unmanned aerial vehicle interference factor, a throwing angle, a wind direction angle, throwing delay and the like, firstly, an initial drop point of seed settlement is given through a physical model, and deviation caused by complex wind field turbulence, unmanned aerial vehicle interference, irregular topography and the like is corrected in combination with a machine learning algorithm; according to the method, the nonlinear and interaction problems in an actual scene are considered, a quadratic term and a small number of physical meaning interaction items are adopted for feature mapping, correction is conducted through a physical model in combination with a machine learning algorithm, and accurate prediction of the seed settlement drop point can be better achieved.

The invention belongs to the technical field of environmental protection, and particularly discloses an intelligent environmental impact assessment method and system, and the method comprises the following steps: obtaining real-time monitoring data, and constructing a pollutant initial distribution model according to the real-time monitoring data; the real-time monitoring data are environmental parameters including temperature, humidity, wind speed, rainfall and pollutant concentration; correcting the initial distribution model by using meteorological prediction data, wherein the meteorological prediction data comprises a wind speed change trend and a wind direction change trend in a future time period; simulating a pollutant diffusion path based on the corrected distribution model, wherein the simulation of the diffusion path is realized by applying a fluid mechanics principle in combination with a topographic relief effect and a vegetation interception effect; and generating a spatial distribution probability distribution diagram of the environmental influence according to the simulation result, wherein the probability distribution diagram is used for displaying the difference of the threat degrees of environmental pollution to different regions. According to the method, the spatial distribution uncertainty can be reasonably quantified while the prediction precision is improved.

The invention discloses a deep space probe landing point selection method based on topographic feature quantification, and the method comprises the steps: constructing a multi-target topographic feature optimization model through analyzing the parameters of a landing region, such as the topographic slope, the surface roughness, the obstacle distribution and the regional area, and achieving the automatic selection of a safe landing point. The method comprises the following specific steps: firstly, detecting spatial distribution of potential obstacles such as rocks and meteorite craters, and secondly, quantitatively analyzing topographic feature parameters such as topographic roughness, gradient change rate and topographic relief; and finally, an optimal landing position is determined under multiple terrain constraint conditions by using an optimization search algorithm, so that the landing safety and task success rate of the detector are effectively improved. According to the method, the safe landing point of the detector is accurately obtained by innovatively applying terrain feature quantification and a search algorithm, so that effective detection of a safe position in a landing area is efficiently realized, and safe landing of the detector is practically guaranteed.

The present invention relates to the field of data visualization technology, and in particular to a method and system for rapid visualization of earthquake emergency information. The method comprises the following steps: acquiring real-time earthquake monitoring data; gridding the population density distribution and key facility locations in the earthquake zone based on the real-time earthquake monitoring data to generate a sensitivity matrix for the earthquake zone; dynamically simulating the seismic wave propagation process based on the real-time earthquake monitoring data and the seismic zone sensitivity matrix to generate wave propagation time series data; identifying epicenter topographic and geomorphological vector data from the real-time earthquake monitoring data, and separately evaluating the topographic relief and geological stability to obtain earthquake topographic relief index data and earthquake geological stability score data. The present invention utilizes data visualization technology to achieve rapid and accurate assessment and visualization of earthquake damage distribution, thereby improving the efficiency of earthquake emergency response and the scientific nature of disaster relief decision-making.

The present application provides a kind of complex stratum structure and topographic effect considering slope microseismic event 3D positioning method, the technical scheme includes S1: the theoretical travel time model suitable for complex slope environment is built;S2: the actual travel time difference is extracted from slope monitoring data;S3: based on the 3D positioning of theoretical and observed travel time difference matching, the ambient noise surface wave tomography technology is applied to slope velocity modeling, without artificial seismic source, high resolution velocity structure can be obtained;Realize 3D ray tracing under the constraint of digital elevation model, ensure that wave propagation path meets physical law, positioning accuracy reaches 3 m, can reach 1.5 m in dense array coverage area, compared with traditional method, it is improved by one order of magnitude;Successfully applied to complex environment with more than 700m topographic relief and extreme velocity variation, can be applied to microseismic monitoring in various complex geological environments.

This invention discloses a fully automated atmospheric and topographic radiometric correction method and device for high-resolution remote sensing imagery, belonging to the field of remote sensing science and technology. The method includes: constructing a fully automated batch processing framework; developing an atmospheric correction module based on the 6S radiative transfer model to automatically retrieve atmospheric parameters and eliminate their influence; and developing a topographic radiometric correction module based on the digital elevation model (DEM) and VECA model to compensate for radiometric distortion caused by complex terrain. The key innovation lies in constructing a joint correction framework and integrating a geomorphic feature recognition algorithm, which can automatically identify and select to execute either single atmospheric correction or a combined atmospheric-topographic correction process based on the degree of topographic relief in the image. This invention achieves fully automated batch processing from data input to result output, requiring no manual intervention, significantly improving the processing efficiency, intelligence level, and adaptability to different terrain scenarios in high-resolution image radiometric correction.

The application discloses a landform flatness measuring device for land planning space, and relates to the technical field of flatness measuring devices.The device comprises a supporting disc, a vertical column fixed on the supporting disc, a steering sleeve rotatably sleeved on the vertical column, a contact wheel rotatably installed at the bottom of an elastic guide assembly, a canvas windingly arranged on winding rollers, a marking pen installed on the elastic guide assembly and used for marking on the canvas.The linear movement of the translation frame relative to the extension frame makes the contact wheel move up and down along with the changes of the landform under the action of the elastic guide assembly, and the marking pen can mark on the canvas, so that the flatness of the landform in a certain direction is measured.The steering sleeve drives the extension frame to rotate, the moving direction of the contact wheel is switched and adjusted, the overall multidirectional landform flatness measurement of the measurement area is facilitated, and the measurement efficiency of the landform flatness is improved.

The invention discloses a vegetation classification method based on space-time multi-modal deep learning, and relates to the technical field of image processing, and the method comprises the steps: obtaining a multi-temporal optical image, a radar image and digital elevation model data of a to-be-classified region, and forming multi-modal data; extracting spectral features, microwave features, topographic features and texture features to form fusion features; calculating graph node features, and updating the graph node features to form graph features; and fusing the fusion features and the graph features to obtain pixel-level coarse classification logs, carrying out feature extraction and fusion on the fusion features to form region-level coarse classification logs, carrying out fusion to obtain a coarse classification probability, and carrying out fine classification on coarse basic features to obtain a final classification result. The method provided by the invention can adapt to a mountainous area environment with multiple clouds, multiple shadows and large topographic relief, improves the stability and classification fineness of vegetation type identification, and is suitable for wide-range vegetation monitoring and ecological assessment scenes.

The application discloses a kind of unmanned plane ecological monitoring aerial survey coverage degree measurement method based on geographic grid, it is related to aerial survey coverage degree measurement technical field, obtains the boundary of the region to be monitored and generates grid unique number, bind topographic relief parameter, canopy height parameter and observation stability parameter, collect each grid incident angle set, calculate the range of view angle variation, when not satisfied, calculate the risk parameter of obstruction and compare with obstruction risk threshold, obtain coverage determination, construct local three-dimensional voxel model to invalid grid coverage, reproduce historical observation posture and execute line-of-sight penetration analysis, calculate three-dimensional visibility ratio and compare with three-dimensional visibility threshold to revise coverage state, still invalid grid is revised, and coverage improvement amount is calculated according to visibility ratio change, combined with revisit frequency to obtain revisit benefit ratio, according to benefit order classification and clustering generation revisit route according to spatial proximity relationship, recalculate key criterion after revisit and output grid coverage result set according to convergence criterion.

This application discloses a vegetation classification method based on spatiotemporal multimodal deep learning, relating to the field of image processing technology. The method includes: acquiring multi-temporal optical images, radar images, and digital elevation model data of the region to be classified, forming multimodal data; extracting spectral features, microwave features, topographic features, and texture features to form fused features; calculating graph node features and updating the graph node features to form graph features; fusing the fused features and graph features to obtain pixel-level coarse classification logits; extracting and fusing the fused features to form region-level coarse classification logits; fusing to obtain coarse classification probabilities; and performing fine classification on the coarse class basic features to obtain the final classification result. The method of this application can adapt to mountainous environments with frequent cloud cover, shadows, and large topographic relief, improving the stability and classification precision of vegetation type identification, and is suitable for large-scale vegetation monitoring and ecological assessment scenarios.

The invention discloses a three-dimensional terrain modeling method based on regional adaptive important point extraction, which comprises the following steps of: reading a digital elevation model image, and setting coordinate conversion parameters; calculating an optimal analysis window of the topographic relief degree of the digital elevation model image, dividing the digital elevation model into a plurality of areas, and calculating the average topographic relief degree of the digital elevation model image and the topographic relief degree of each area; taking the average topographic relief as a judgment threshold, and setting a standard value of a dynamic threshold; extracting a TIN point set of each region by adopting a VIP algorithm feature extraction window, identifying important points of the ground undulation degree, and converting coordinates; and constructing an irregular triangulation network for the important points after coordinate conversion, endowing a triangular patch with gradient colors according to the elevation grade of the point set, and performing precision evaluation. According to the method, the key terrain feature points can be accurately extracted, the three-dimensional model conforming to the actual terrain is constructed, and the applicability of the method under various terrain conditions is improved.

The invention provides a karst area linear structure identification method and system based on multi-source remote sensing, and the method comprises the steps: firstly obtaining an optical remote sensing image, radar remote sensing data and terrain elevation model data of a target area, constructing a target remote sensing data set, carrying out the ground surface coverage feature enhancement processing of the target remote sensing data set, and carrying out the recognition of a karst area linear structure. The method comprises the following steps: generating an enhanced multi-source feature set containing features such as a karst area earth surface coverage type, topographic relief and radar scattering characteristics, and then calling a pre-trained linear structure recognition model to recognize the enhanced set to obtain an initial result containing linear structure spatial distribution data and a structure type label; and finally, performing spatial topological relation verification processing on the initial result based on the geological background data of the target area, and generating an optimized linear structure identification result for outputting the fault and fracture spatial position and the structure confidence coefficient parameter of the karst area, thereby effectively identifying the linear structure of the karst area.

The invention discloses a low-error surveying and mapping unmanned aerial vehicle and a surveying and mapping method, relates to the technical field of distance measurement, and can solve the technical problems of low data processing precision and poor efficiency in a surveying and mapping process due to the fact that real topographic relief and unmanned aerial vehicle bumping errors cannot be effectively distinguished under complex topographic conditions. Comprising the steps that a ground height data set obtained by measuring a target area through a surveying and mapping unmanned aerial vehicle is acquired, and the ground height data set comprises ground height data of multiple measurement points; screening out a plurality of alternative error data from the ground height data set, wherein the alternative error data are measurement point data deviating from a normal fluctuation range; multiple pieces of target error data are identified from the multiple pieces of alternative error data, wherein the target error data are error data caused by jolting of the surveying and mapping unmanned aerial vehicle in the multiple pieces of alternative error data; performing data correction on the target error data to obtain a corrected ground height data set; and determining a ground height map according to the corrected ground height data set.