1407results about "Picture interpretation" patented technology

The invention discloses a multi-sensor fusion-based position and posture estimation method for a working device of a mining robot, which realizes accurate position and posture estimation by installing a body posture sensor and an environment sensing sensor and combining a kinematic model, visual inertia state estimation and a nonlinear least square optimization algorithm. The method comprises the following steps: firstly, carrying out preliminary prediction on a kinematic model through feedback data of an encoder and an inclinometer, and constructing a local visual inertia estimator by utilizing a camera and an MEMS to correct a prediction result; and then, a combined constraint modeling and optimization method is adopted to improve the precision of the pose of each working mechanism. Besides, geometric features of the bucket are extracted through a semantic segmentation network and a template matching technology, six-degree-of-freedom pose estimation of the bucket is completed, finally data fusion of a body pose sensor and an environment perception sensor is performed through a factor graph optimization algorithm, and comprehensive pose estimation of the whole working device is achieved. The working pose of the mining robot can be monitored in real time, the operation precision is improved, human errors are reduced, the working efficiency and safety can be optimized through data analysis, and more effective support is provided for path planning of a working device of the mining robot.

The invention relates to the technical field of image processing, in particular to a dual-branch coding desert segmentation model network structure based on structure state space duality, which adopts multi-dimensional dynamic convolution to replace traditional convolution in the initial stage of an encoder, introduces a mamba2 module based on the structure state space duality into the backbone design of the encoder, and improves the robustness of the encoder. The efficiency and adaptability of the model are remarkably improved, a double-branch parallel design is adopted, one branch uses cavity convolution to extract multi-scale context information, the other branch reinforces feature expression through a mamba2 module, the model is connected in series with a space attention module and a channel attention module between an encoder and a decoder, and the algorithm is more accurate. The method has the advantages that the method is simple and easy to implement, interference of irrelevant information on segmentation results is suppressed, a deformable large kernel attention module is introduced to the tail end of a decoder, and global and local modeling capability of the model in processing desert complex boundary regions is effectively improved by combining flexibility of deformable convolution and global receptive field characteristics of large kernel convolution.

The invention relates to the technical field of unmanned aerial vehicle perception, and discloses a visual navigation method and system of a six-rotor full-drive unmanned aerial vehicle, a terminal and a storage medium. A navigation framework integrating dynamic visual perception and full-drive control characteristics is constructed, and a visual encoder is adopted to extract multi-scale environment characteristics; according to the method, time sequence information is processed in combination with a long-short-term memory network, efficient feature extraction and motion trail prediction of an image sequence under high-speed rotation and violent attitude change of the unmanned aerial vehicle are achieved, the problem of feature tracking loss of a traditional algorithm in a high-dynamic scene is solved, global correlation modeling of environment features is enhanced through an attention mechanism, and the tracking accuracy of the unmanned aerial vehicle is improved. According to the six-rotor-wing full-drive unmanned aerial vehicle navigation method, the navigation precision and robustness of the unmanned aerial vehicle in a complex dynamic environment are remarkably improved by combining control instruction generation of dynamics constraints, high-precision inertial navigation compensation is not needed, and an efficient and reliable navigation solution is provided for application of the six-rotor-wing full-drive unmanned aerial vehicle in emergency rescue, high-speed inspection and other scenes.

The invention provides a three-dimensional modeling and mapping method, system and device based on a fixed-wing unmanned aerial vehicle, and relates to the technical field of geographic mapping. According to the method, through synchronous data acquisition of an inclined camera, a laser scanner and a position and attitude measurement system, and in combination with navigation trajectory calculation, point cloud coordinate transformation, image attitude calculation and multi-source data fusion processing, space collaboration of image data and point cloud data is realized under a unified coordinate system. And performing aerial triangulation based on the fused data, further carrying out spatial modeling and texture mapping, and effectively improving geometric consistency and modeling continuity among multi-source data. According to the method, ground control points do not need to be arranged, high-precision and high-consistency geographic space information expression can be achieved, and real scene modeling and multi-type surveying and mapping data result generation in a large-scale mapping scene are supported.

The invention relates to the technical field of digital surveying and mapping of constructional engineering and construction quality control, and discloses a house surveying and mapping system for constructional engineering, which comprises a data acquisition module for synchronously generating a panoramic image signal and an inertial measurement unit signal; the preprocessing module receives a panoramic image signal and an inertial measurement unit signal, and fuses the panoramic image signal and the inertial measurement unit signal through a visual inertial odometer to generate a pose calibration signal; the fusion modeling module receives the purification point cloud signal and the pose calibration signal, and generates building component parameterized data based on a deep learning component recognition algorithm; the result output module receives the building component parameterized data and generates a construction deviation analysis report based on a preset tolerance threshold value; the intelligent control module generates an acquisition quality feedback signal through a real-time quality evaluation algorithm to the data acquisition module so as to adjust a scanning path, and sends a dynamic sampling rate control signal to the preprocessing module so as to optimize a calculation load. The problems of low house surveying and mapping efficiency, poor modeling precision and weak field real-time feedback are solved.

The invention relates to the technical field of path planning, in particular to a path planning method and system for an inspection robot, and provides the following scheme: executing an inspection task through an initial electronic map, collecting first image data and pose information, recognizing an inspection target, completing image registration, and generating second image data; inverting and calibrating the optimal observation direction based on the structural sensitivity distribution of the local visual features and the illumination variation trend, and determining the shooting position and angle to form a path node; a multi-objective optimization model is adopted, the path length, the view angle continuity and the illumination interference are comprehensively considered, and an optimal inspection path is generated; the method is suitable for outdoor inspection scenes with complex structures and non-uniform target distribution.

The invention relates to the technical field of surveying and mapping geographic information, in particular to an unmanned aerial vehicle intelligent surveying and mapping system based on a remote sensing technology, which comprises a remote sensing image acquisition module, an image correction splicing module, an ortho-rectification module, a ground feature recognition module, an attribute information input module, a map making module and a management database, according to the system, matching pairs are generated by obtaining all surface remote sensing images and extracting descriptors of all the surface remote sensing images, the corresponding relation of the same surface features among different images is determined, a basis is provided for image splicing, and a correction model is trained to convert coordinates of ground control points into image coordinates; the basic attribute table is created according to the basic attributes corresponding to the types of the ground features, the information amount and practicability of the map are improved, the elevation, obtained through interpolation calculation, of each interpolation point in the surveying and mapping area is combined to generate the complete map, the map can present ground feature distribution and can also reflect topographic relief, and the map rich in content is formed.

The invention discloses a highway engineering road and bridge measurement method based on BIM, and belongs to the technical field of highway engineering measurement. According to the method, the problems that the modeling efficiency and accuracy are difficult to consider, the modeling precision of a non-important area is redundant and the like in the existing road and bridge measurement method for the highway engineering are solved. The method comprises the following steps: acquiring satellite positioning data by using a satellite positioning system in a measurement area, acquiring point cloud data by using laser scanning equipment, and acquiring road and bridge images by using image acquisition equipment; converting satellite positioning data into unified coordinate system data information, carrying out spatial registration on the unified coordinate system data information and the point cloud data, and dividing an important region and a non-important region; constructing a precise three-dimensional point cloud model for the important area, constructing a path point cloud model for the non-important area, and integrating road and bridge images; and finally fusing the model to obtain road and bridge parameters. The method can improve the model efficiency and reduce the model resource overhead while effectively improving the measurement precision, and is suitable for the measurement work of roads and bridges of highway engineering in various environments.

The invention discloses a robot gait self-adaptive adjustment method and system combined with topographic feature recognition, and relates to the technical field of robot gaits, and the method comprises the steps: collecting a topographic image in front of a robot advancing path, carrying out the deblurring processing, and extracting ground feature data through a lightweight neural network; matching the ground characteristic data with a preset gait control parameter database to obtain a gait control parameter set, and generating a control signal through a proportional-differential control method according to the gait control parameter set; correcting the control signal based on a reinforcement learning algorithm, and generating a target control instruction for driving a joint actuator; in the process of executing the target control instruction, collecting state feedback data in real time, and comparing and optimizing the feedback data with the currently executed target control instruction to realize feedback adjustment; the problems of insufficient terrain recognition precision caused by image blurring, response lag caused by sensing and control decoupling and lack of adaptive ability are effectively solved.

The invention relates to the technical field of remote sensing surveying and mapping, in particular to a digital construction method based on three-dimensional surveying and mapping of an unmanned aerial vehicle, which comprises the following steps: importing a digital earth surface model and identifying an obstacle, calling identification information to calculate a path avoidance cost to plan an unmanned aerial vehicle path, and adjusting a flight height in combination with an image definition demand. And analyzing the measurement data to reconstruct a three-dimensional model, analyzing the actual project progress and the size deviation, and obtaining a construction project measurement record. According to the invention, through regional obstacle identification, bypass cost score calculation combined with multiple measurement paths, safety and rationality of route planning are optimized, and a flight height and image definition linkage adjustment mode is utilized, so that dynamic control of imaging quality and construction of construction engineering model components are realized; the construction progress identification and the dimensional deviation detection are realized, the progress judgment accuracy and the space monitoring capability of digital construction are effectively improved, a data basis is provided for project management decision, and the construction quality is improved.

The invention discloses a heading equipment cutting head position calibration method based on image acquisition, which comprises the following steps: S1, acquiring image data and preprocessing the image data; s2, extracting cutting head feature points, and constructing a two-dimensional feature point set; s3, calculating a three-dimensional coordinate by using perspective transformation, and generating a point cloud set; s4, performing rigid registration on the point cloud set and a standard structure model of the tunneling equipment, and iteratively solving a rotation matrix and a translation vector to obtain pose parameters; s5, simulating change tracks of the feature points in continuous frames, constructing a time consistency constraint, and optimizing pose parameters; s6, the optimized three-dimensional coordinates are converted to a reference coordinate system, space alignment is completed, and a position calibration result is generated; and S7, outputting a calibration result for control instruction generation and path planning. According to the invention, high-precision and non-contact three-dimensional position calibration of the cutting head of the tunneling equipment is realized, and operation automation and control precision are improved.

The invention relates to the technical field of goods shelf detection, in particular to a self-detection method and device for goods shelf settlement, and provides the following scheme: obtaining a top view image through an image sensor arranged right above the top of a goods shelf, dividing the image into a plurality of grid units, and positioning a rectangular geometric shape by utilizing Hough transform; and screening a plurality of to-be-detected areas in combination with the edge features. For an area to be measured, homographic registration and ortho-rectification are carried out based on a reference image, a displacement field is obtained by adopting sub-pixel-level dense registration, and a geometric parallax component field corresponding to imaging parameters is obtained through robust estimation. And under the hypothesis of small deformation, inverting the parallax into a pixel normal distance, and carrying out weighted aggregation on the local region to obtain a local distance measurement result. And by iteratively combining adjacent grids, determining a settlement area boundary, and finally outputting a settlement detection result. Millimeter-level settlement quantification can be realized under a single-frame image, hardware transformation is avoided, and the method is suitable for automatic detection and long-term monitoring of multi-specification goods shelves.

The invention provides a heading machine positioning method and system, and relates to the technical field of heading machines, and the method comprises the steps: pre-arranging a target on a heading machine, and building a heading machine gravity center coordinate system; capturing a plurality of frames of images with targets by adopting imaging equipment; performing image recognition on the multi-frame image by adopting a target detection model, extracting a feature point set of an area where the target is located, and generating multi-frame data of each feature point in the feature point set; performing multi-angle fusion on each feature point based on the multi-frame data of each feature point, and converting each feature point from a two-dimensional coordinate in an image coordinate system to a three-dimensional coordinate in a world coordinate system; based on the centroid coordinate system of the heading machine, converting the three-dimensional coordinates of the feature points in the world coordinate system into the three-dimensional coordinates in the centroid coordinate system of the heading machine, so as to generate position information of the heading machine; the problem that an existing heading machine is poor in pose positioning result accuracy is solved.

The invention provides a historical building digital modeling and outer wall diagnosis method based on multi-source data fusion, and relates to the technical field of historical building protection and digitization, the method aims at comprehensively obtaining historical building data and analyzing the condition of an outer wall body, firstly, a building main body image is captured through aerial photography of an unmanned aerial vehicle at multiple angles and multiple levels, and the flight height is 30-80 m; sites are arranged on the ground, millimeter-level scanning is conducted on the building facade through a three-dimensional laser scanner, the distance between every two adjacent sites is 10-30 m, and the sites cover the area 10-15 m away from the ground; the method comprises the following steps: firstly, carrying out multi-source data fusion on a wall body, then carrying out close-range shooting on upward view and typical local parts by using a micro lens of a single lens reflex, then preprocessing various data, and finally, measuring and analyzing the concave-convex condition of the wall body according to fused point cloud data, thereby providing accurate data support for historical building protection and research. By means of unmanned aerial vehicle aerial photography and three-dimensional laser scanning station arrangement, it is ensured that high-quality data is obtained.

The invention relates to a flood prevention block stone reserve high-precision measuring and calculating method, which comprises the following steps: aerial photography and three-dimensional modeling are carried out on the overground part of a flood prevention block stone by using two modes of unmanned aerial vehicle oblique photography and unmanned aerial vehicle laser radar, and the overground volume of the flood prevention block stone is measured and calculated; a ground penetrating radar is adopted to detect the settlement depth of the flood prevention block stone, the volume of the underground part of the block stone is calculated, the volume of the whole block stone can be obtained by adding the volume of the overground part of the flood prevention block stone and the volume of the underground part of the block stone, and the weight of the flood prevention block stone can be calculated. According to the invention, the reserve of the overground and underground parts of the flood prevention block stone can be accurately measured, and technical support is provided for flood prevention and emergency rescue.

The invention discloses a building surveying and mapping system based on unmanned aerial vehicle remote sensing, and belongs to the technical field of building surveying and mapping. The system comprises a data acquisition module, a data preprocessing module, a feature extraction module, a spatial relationship analysis module and a dynamic change analysis module, and the data acquisition module is responsible for automatically acquiring multi-temporal remote sensing data covering a target area; the data preprocessing module is responsible for performing cleaning, geometric correction and multi-source data fusion on the acquired remote sensing data; the feature extraction module is responsible for extracting key features of the building from the multi-resolution remote sensing data; the spatial relationship analysis module analyzes the spatial distribution characteristics and the mutual relationship of the building group; and the dynamic change analysis module is responsible for monitoring and analyzing the dynamic change of the building group, including automatic identification of new building, dismantling and reconstruction conditions.

The invention provides a bridge dynamic displacement measurement method based on an unmanned aerial vehicle, and relates to the field of bridge health monitoring, and the method comprises the steps: obtaining a bridge image sequence through the unmanned aerial vehicle; obtaining transformation matrixes according to the relation between the other frames of images and the fixed measurement points of the reference frame, correcting the coordinates of the motion measurement points of the corresponding frames of images according to the transformation matrixes, obtaining a corrected bridge image sequence, and further obtaining an initial displacement signal; variational mode decomposition is carried out on the initial displacement signal, a constraint variational optimization problem is constructed, the optimization problem is solved through iteration to obtain each mode component of variational mode decomposition and a corresponding center frequency, and a low-frequency mode component is removed to obtain a displacement signal after secondary correction of the influence of the unmanned aerial vehicle; and obtaining the real displacement according to the scale factor between the image obtained by the unmanned aerial vehicle and the real world. The influence caused by the movement of the unmanned aerial vehicle is corrected, and the accuracy, reliability and stability of a measurement result are effectively improved.

A forest land landslide collapse area detection method based on unmanned aerial vehicle visual remote sensing is characterized by comprising the following steps: step 1, constructing a forest land landslide collapse area detection system based on unmanned aerial vehicle visual remote sensing; 2, the unmanned aerial vehicle shoots remote sensing image data; 3, an image preprocessing module performs preprocessing operation on the historical satellite image data and the remote sensing image data to obtain a remote sensing image and a historical image; step 4, matching the feature points of the remote sensing image and the historical image by an image alignment module, calculating a transformation matrix, and completing alignment of the images before and after the landslide through the transformation matrix; step 5, identifying a landslide area by a landslide area identification module; step 6, a landslide area identification module extracts a landslide area contour; and 7, calculating the average gradient of the landslide area by a landslide area calculation module, and calculating the actual area of the slope of the landslide in combination with the plane projection area and the average gradient. The method has the advantage that the forest land landslide collapse area can be rapidly and accurately detected.

The invention discloses a high-precision earthwork measurement and calculation method and system for constructing a three-dimensional model based on aerial survey of an unmanned aerial vehicle. The method comprises the steps that image control points and check points are arranged in a to-be-measured area in advance; starting to collect aerial survey image data of the unmanned aerial vehicle, and performing supplementary measurement on an obstacle shielding area to obtain supplementary measurement data; importing the aerial survey image data to generate a live-action three-dimensional model; and finding an image control point identification position on the three-dimensional model, performing corresponding point pricking on the image control point identification position and an actually measured image control point, performing conversion from longitude and latitude coordinates of the model to project coordinates after all the image control points are matched, regenerating the three-dimensional model, importing check point coordinate data, and checking the precision of the model. Seed points are added to remove obstacles to restore the surface topography, then drawings and design elevations are imported to carry out earthwork high-precision calculation, the problems that manual calculation combined with other software is complex in operation and prone to making mistakes and consumes a large amount of time are solved, accurate data support is provided for earthwork construction, and the project construction period is shortened.

The present invention relates to the field of unmanned aerial vehicles, and provides a method, system and medium for detecting the attitude of unmanned aerial vehicles based on solar images, including: obtaining a solar image by photographing with a camera carried by an unmanned aerial vehicle; determining the pixel coordinates of the solar center point in the solar image; calculating the actual solar altitude angle and the actual solar azimuth angle in the camera coordinate system according to the pixel coordinates of the solar center point; establishing a relative position relationship model among the earth, the sun and the unmanned aerial vehicle, and calculating the theoretical solar altitude angle and the theoretical solar azimuth angle based on the time information and position information of the unmanned aerial vehicle photographing the solar image; calculating the deviation between the actual solar altitude angle and the theoretical solar altitude angle, and between the actual solar azimuth angle and the theoretical solar azimuth angle, and obtaining the attitude of the unmanned aerial vehicle. The present invention uses solar information for attitude detection, and is not affected by complex electromagnetic environments; it can be used for initial attitude calibration of unmanned aerial vehicles, especially calibration of heading.

The invention relates to a method for the determination of the carbon balance in a predetermined geographic area, applicable to natural and / or human-managed systems, which integrates a series of known and validated technologies into a single information system on the carbon sequestered by natural and human-intervened systems. A system for determining the total carbon balance of a predetermined geographic area is also provided.

The invention belongs to the technical field of glacier identification, and provides a system for monitoring glacier dynamics by an unmanned aerial vehicle low-altitude flight photogrammetry method, which comprises the following steps that: a glacier area unmanned aerial vehicle low-altitude photogrammetry module selects an unmanned aerial vehicle as required, and plans a route and parameters; the image preprocessing module is used for extracting key feature points from each image by using Pi * 4Dcap; fine three-dimensional information, an ortho-image and a glacier digital surface model DSM are obtained through point cloud generation and densification, and glacier dynamic information can be identified by utilizing the glacier digital surface model DSM after correction and interpolation processing are carried out on the three-dimensional information, the ortho-image and the glacier digital surface model DSM; calculating the elevation, volume, speed and mass change of the glacier based on a digital surface model (DSM) of the glacier; the glacier fissure is identified by utilizing the orthoimage and the glacier digital surface model DSM, and the system can comprehensively and accurately monitor the dynamic information of the glacier.

The invention discloses a multi-unmanned-aerial-vehicle cooperative SLAM system based on common-view area detection. According to the system, firstly, an adaptive threshold value and a feature homogenization method are introduced to improve a feature extraction algorithm to extract image features; secondly, on the basis of a feature point screening algorithm fusing YOLO and geometric constraints, the influence of motion exterior points is reduced; then, an improved feature point screening mechanism is provided for the problem of feature mismatching existing in the unmanned aerial vehicle image; thirdly, on the basis of local positioning and mapping of the unmanned aerial vehicle, a common-view area detection algorithm based on a visual word bag and relative pose calculation are fused into a local map; and finally, performing global optimization by adopting an improved L-M solution algorithm to improve pose estimation, and generating a high-precision global map. According to the method, the multi-unmanned aerial vehicle cooperative SLAM system is improved, so that the interference of movement obstacles in an unknown environment is reduced, the mapping and positioning precision is improved, and better real-time performance and robustness are achieved.

The invention discloses a building height detection method and device and a storage medium, and relates to the technical field of image data processing, and the method comprises the steps: generating a digital elevation model of a target region and a digital orthoimage of the target region based on an aerial image; inputting the digital orthographic image into a building detection model for building identification to obtain an identification result of the building in the target area; according to an identification result of the building, establishing world coordinates of the building in the digital orthographic image; and determining a building elevation value and a ground elevation value of the building in the digital elevation model according to the world coordinates, and determining the height of the building according to the building elevation value and the ground elevation value. The technical problems of poor precision and low efficiency of building height detection in urban wide-area complex areas are solved, and the precision and efficiency of building height measurement are improved.

The invention relates to an end-to-end spacecraft pose measurement method based on deep learning, and belongs to the field of spacecraft pose measurement, the method uses a sliding window transformer as a feature extraction backbone network, introduces a multi-layer large kernel convolution module, and is used for enhancing a global receptive field and feature expression ability. A rotating head detection network and a position head detection network are arranged in parallel after the backbone network, a rotating head is used for predicting a 3D key point and a projection weight thereof, a position head is used for predicting a 2D key point, a 2D-3D corresponding relation is sent to a differentiatable EPro-PnP module for pose solving, it is ensured that a gradient can be completely transmitted back to a feature extraction layer, and a feature extraction layer is obtained; therefore, a globally optimal solution is obtained, and integrated network training of feature learning and geometric pose solution is realized. According to the method, a complex loss function is constructed, through multi-angle supervision and complementary optimization, it is ensured that the model can accurately predict the 6D pose of the spacecraft, and robustness and generalization performance in a complex environment are enhanced.

The invention discloses a non-contact wave measurement method and device based on machine vision. The method comprises the steps that a multi-view imaging system is used for synchronously shooting from multiple vision; the shot image is subjected to standardization processing, a wave surface image is obtained, the standardization processing comprises color adjustment, noise reduction processing, data enhancement, texture analysis and edge detection and frequency domain analysis, and the wave surface image comprises at least two complete wave periods; and performing image analysis on the wave surface image by using a pre-trained convolutional neural network to obtain a wave height prediction value. The problems that a traditional monitoring method needs to make contact with a measured object and the environment, deployment is complex, and the method is prone to being affected by the marine environment can be solved.

The invention provides a settlement detection method and device based on machine vision, belongs to the technical field of underground engineering, and is applied to a settlement monitoring area, a measuring point target is arranged in the area, and a reference point target is arranged outside the area; comprising the steps that S1, a settlement monitoring area is continuously shot, a monitoring image set is obtained, and the monitoring image set covers a monitoring point target arranged in the settlement monitoring area and a reference point target arranged outside the settlement monitoring area; s2, processing the monitoring image set to obtain the relative elevation of each measuring point target; and S3, comparing the relative elevation of each measuring point target with the corresponding initial elevation to obtain the settlement amount of each measuring point target. The beneficial effects are that by arranging the measuring point target and the reference point target, the normal operation of road traffic is not interfered, the road traffic is not easily damaged by rolling of running vehicles, the problem of machine vision wide-area monitoring is solved, and the settlement monitoring area can be monitored more comprehensively and accurately.

The invention relates to the technical field of crop growth monitoring, in particular to a method and system for monitoring crop growth through images, and the method comprises the following steps: deploying an unmanned aerial vehicle, flying over a target paddy field, collecting image data, transmitting the image data to a ground station in real time through data transmission, and generating a preliminary image data set; and cutting and correcting the initial image data set. According to the invention, the unmanned aerial vehicle is used for image acquisition, the problem that the visual angle of a fixed ground camera is limited is avoided, and the data hysteresis is reduced through real-time data transmission, so that the monitoring is more timely. In the image data cutting and correcting process, angle deviation and background noise are eliminated, and the leaf area index calculation accuracy is improved. Through introduction of time sequence analysis, dynamic monitoring of crop growth trend is realized, growth evaluation is no longer limited to a single time point, historical data comparison is formed, and the prediction capability of the growth state is improved.

The invention relates to a mangrove forest space expansion-atrophy monitoring method based on a continuous time sequence spectral index. The mangrove forest space expansion-atrophy monitoring method comprises the following steps: step (1), preprocessing a remote sensing image; (2) generating an original time sequence; (3) removing abnormal observation data in the time sequence; step (4), constructing a vegetation index time sequence sensitive to the mangrove forest within the year; step (5), extracting a mangrove forest potential area every year based on a threshold method; step (6), identifying and correcting potential areas of the mangrove forest in data abnormal years; (7) capturing the years of space expansion and atrophy of the mangrove forest; (8) drawing a space-time distribution diagram of expansion and atrophy of the mangrove forest; and (9) quantitatively evaluating the annual mangrove forest area increase and loss. The method draws the spatial distribution diagram of mangrove forest expansion and atrophy based on long-time series multispectral remote sensing data, can accurately monitor the time point when the mangrove forest is subjected to atrophy and expansion events, and ascertains the spatial and temporal characteristics of the mangrove forest distribution range.

The invention provides an open pit coal mine safety detection and production evaluation method based on unmanned aerial vehicle surveying and mapping. The method comprises the steps of obtaining surveying and mapping data of a target mine field through unmanned aerial vehicle oblique photography surveying and mapping; obtaining geographic information data including a digital orthoimage, a digital elevation model, an oblique photography model and digital vector data based on the surveying and mapping data; constructing a three-dimensional digital twinborn scene of the target mine field based on the geographic information data; performing profile detection on the three-dimensional digital twinborn scene, constructing a vertical profile, and performing safety detection on the slope angle, the step height and the width based on the vertical profile; and performing filling or excavation measurement on the coal mining area, the coal piling area or the soil piling area of the target mining area, and calculating the volume to obtain a production evaluation result. The problems that in the prior art, a geographic surveying and mapping method is low in efficiency and disordered in data, and a safety detection and production evaluation method depends on detection equipment, so that safety detection and production evaluation of open pit coal mining are poor in accuracy and low in efficiency are solved.