21 results about "Orthophoto" patented technology

This invention discloses a method and system for identifying apparent damage to bridge concrete based on texture analysis, belonging to the interdisciplinary field of bridge engineering inspection and computer vision technology. The method includes: acquiring surface images of bridge components using a drone and generating orthophotos; extracting contrast, correlation, and entropy features based on the gray-level co-occurrence matrix; extracting histogram features based on local binary patterns; detecting corrosion and seepage areas based on the HSV color space; fusing multi-dimensional features and inputting them into a multi-label classifier, simultaneously outputting pixel-level segmentation masks for multiple types of damage such as cracks, spalling, exposed rebar corrosion, and seepage; generating a damage level score and maintenance recommendation report according to bridge technical condition assessment standards. This invention integrates complementary texture features to achieve simultaneous identification of multiple types of damage, outputs pixel-level segmentation results, and automatically generates an assessment report that conforms to engineering specifications.

This invention relates to a method and medium for detecting building changes based on UAV imagery. By using UAV images of the same target area acquired at different times, the method incorporates UAV ground control points and aerial POS information into an aerial triangulation system to jointly calculate the camera's exterior orientation elements and imaging geometry. Orthophotos of the preceding and following times are generated under a unified coordinate reference, and building probability maps of the preceding and following times are obtained through processing. The merged orthophoto obtained after combining the preceding and following times UAV images is used to construct multiple object units based on cross-temporal consistency. Furthermore, the average first and second semantic confidence scores corresponding to the preceding and following times orthophotos are obtained. Based on whether the two average confidence scores meet three preset judgment conditions, each object unit is determined and labeled as belonging to a specific type of building change area. Thus, stable and accurate detection of building changes based on UAV imagery is achieved.

This invention discloses a method, system, and storage medium for LOD2 building reconstruction from single-view orthophotos. The method inputs a single-view orthophoto remote sensing image into a shared backbone network to generate a footprint mask probability map; constructs a first modulation factor, performs element-wise gating on the multi-scale feature map, and inputs it into a height decoder to output a height field; normalizes the height field to obtain a normalized height map and constructs a second modulation factor; fuses the second modulation factor with the first modulation factor to form a composite modulation factor, performs dual element-wise gating on the multi-scale feature map, and inputs it into a structure line decoder to output a distance field and orientation field; recovers the roof structure line based on the distance field and orientation field; and constructs the roof surface, walls, and ground by combining the roof structure line, height field, and footprint boundary to generate a LOD2 3D model of the building. This invention can automatically generate a topologically complete and geometrically accurate LOD2 3D model relying solely on a single orthophoto.

A method for classifying disturbing plants using drone orthophotos is disclosed, and the method for classifying disturbing plants using drone orthophotos may include: (a) forming a drone orthophoto in which a target area is shown during a preset period; and (b) determining a candidate object of a disturbing plant from the drone orthophoto as the disturbing plant.

This application discloses a multi-mode fusion method and system for calculating canopy height in fields and orchards, belonging to the field of agricultural remote sensing and precision agriculture monitoring technology. It utilizes an unmanned aerial vehicle (UAV) platform equipped with a lidar sensor, a multispectral sensor, and a pose acquisition module to simultaneously collect lidar point cloud data, multispectral image data, and timestamped pose data. Based on the pose data, spatial registration and stitching are performed on the point cloud data to generate a 3D point cloud model of the crop; geometric correction and stitching are performed on the multispectral image data to generate a multispectral orthophoto. The two are unified to the same geographic coordinate system to establish a spatial mapping relationship; spectral indices are calculated based on the multispectral orthophoto to identify land cover categories and regions; corresponding subsets of the point cloud are extracted based on the spatial mapping relationship to generate a digital surface model and a digital ground model; and canopy height is calculated by the difference. This application integrates multi-source data to achieve automated and high-precision calculation of canopy height for crops such as corn and fruit trees, and has strong applicability.

This invention discloses a method and related apparatus for identifying and calculating the excavation progress of cable trenches in photovoltaic power plants, belonging to the field of photovoltaic power generation technology. The method acquires orthophotos of the photovoltaic power plant and locates the cable trench; obtains point cloud data of the cable trench based on its location; performs plane fitting using the point cloud data to obtain the target plane of the cable trench; and compares the target plane with the original design drawings to obtain the excavation progress data of the cable trench. This invention can acquire cable trench excavation progress data in real time, providing timely and accurate information support for the construction and management of photovoltaic power plants. Furthermore, this method has a certain degree of flexibility and can adapt to the needs of cable trench excavation progress identification in photovoltaic power plants of different scales and types.

This invention discloses an intelligent monitoring and early warning method for small urban water bodies based on visible light drones, belonging to the field of water quality prediction and management technology. The method acquires water body images using a drone equipped with a visible light sensor, automatically stitches them together to generate orthophotos, automatically segments water body areas and outputs boundary vector layers, extracts band DN values ​​and constructs a water quality parameter feature dataset through normalization, band combination, and chromaticity angle calculation, uses a machine learning model to invert chlorophyll a concentration, turbidity, and phycocyanin concentration, and optimizes the model accuracy by combining measured data, performs statistical analysis and future trend prediction on the inversion results, and generates thematic maps, completes water quality evaluation according to standards, and automatically outputs reports and pushes them to relevant units when early warning conditions are met. This invention uses low-cost visible light equipment to achieve fully automated intelligent monitoring, effectively improving inversion accuracy and regulatory efficiency, and adapting to the needs of large-scale, routine monitoring and early warning of small urban water bodies.

This invention provides a method for identifying the severity of walnut leaf scorch based on UAV multispectral imagery, comprising the following steps: S1. Acquiring UAV multispectral orthophotos of the walnut canopy in the target area; S2. Extracting vegetation index features based on the multispectral orthophotos, and filtering the extracted vegetation index features using a minimum redundancy maximum correlation algorithm; S3. Inputting the filtered vegetation index features into a pre-trained model for identifying the severity of walnut leaf scorch, and outputting the severity level of walnut leaf scorch in the target area; wherein, the pre-trained model for identifying the severity of walnut leaf scorch is based on a sample dataset containing labels for the severity level of walnut leaf scorch and trained using a random forest algorithm. This invention solves the technical problems in existing technologies for walnut leaf scorch, such as the lack of available remote sensing data sources and insufficient classification accuracy and generalization ability of monitoring models due to weak correlation or redundancy of feature variables.

This invention discloses a method, system, platform, and storage medium for generating and processing tunnel orthophotos based on global constraint optimization and contour refinement distance determination. It generates and acquires first data corresponding to the tunnel; based on a longitudinal geometric reference with known physical spacing, it uses a global constraint optimization method to longitudinally calibrate the first data, generating corresponding second data; it then refines the contour distance determination for each section in the second data, generating corresponding third data. This method overcomes the limitations of local correction in dynamic filtering, achieving global compensation for longitudinal mileage errors and improving the geometric consistency of long-distance tunnel images. It accurately extracts the main lining contour in complex sections, effectively eliminating interference from auxiliary structures such as pipelines and cable troughs, ensuring that the distance division is based on the actual tunnel wall. Based on the distance division, it achieves adaptive grayscale equalization, compensating for intensity unevenness caused by changes in laser incident angle, point density, and curvature, thus improving image visual quality and detail recognition.

The utility model discloses a TBM rock slag catalog image automatic acquisition device based on high-speed camera shooting. The TBM rock slag catalog image automatic acquisition device comprises a TBM cutter head, a TBM slag discharging belt conveyor, a high-speed camera and a camera fixing support. A TBM cutterhead capable of achieving rotary cutting is installed at the end of the TBM deslagging belt conveyor, the camera fixing support is installed on the side, close to the TBM cutterhead, of the TBM deslagging belt conveyor, and the high-speed camera is installed on the camera fixing support, arranged above the starting end of the TBM deslagging belt conveyor and used for collecting orthoimages of rock slag on the TBM deslagging belt conveyor in real time. Rock slag image data are collected in real time through the high-speed camera, manual intervention is not needed, the data collecting and processing time is greatly shortened, meanwhile, the device can work continuously, the idle time of manual operation is effectively shortened, massive training samples are accumulated for rock slag image recognition and geological analysis, the precision of a machine learning model is improved, and the working efficiency is improved. And intelligence and automation of geological logging of the rock slag are promoted.

This invention discloses a remote sensing-based method for estimating farmland slope, relating to the field of farmland slope technology. The method includes the following steps: acquiring image data and coarse-resolution digital elevation model (DEM) data; processing the image data to obtain a digital terrain model and orthophotos; visually interpreting the orthophotos to extract farmland distribution and shape indices; extracting topographic factors based on the DEM and filtering key factors using the variance inflation factor method; weighting the key factors using the entropy weighting method to obtain a comprehensive membership degree, combining a threshold to filter target slope points and overlaying farmland to obtain a preliminary farmland slope; performing a weighted average of the coarse-resolution DEM data to obtain the topographic slope; calculating the main slope aspect correction coefficient using the K-means clustering method on the DEM; fitting the preliminary farmland slope, topographic slope, main slope aspect correction coefficient, and shape index using a power function model to obtain a farmland slope estimation model, thus achieving the estimation of farmland slope.

This invention relates to the field of road distress evaluation technology, and discloses a method and system for evaluating and selecting maintenance methods for the entire road structure based on multi-source data fusion. The method includes: establishing a mapping relationship to generate orthophotos of the road surface, and combining them with ground-penetrating radar images to form paired datasets; inputting the paired datasets into a two-stream convolutional neural network for physical feature extraction, and performing splicing and fusion processing on the physical features; outputting a full-structure descriptor based on the processing results to determine the evaluation results of the comprehensive condition of the road surface and deep layers; calculating the road full-structure health index based on the full-structure descriptor and the distress severity coefficient, and selecting the optimal maintenance method for the entire road structure based on the road full-structure health index and the road digital twin. This invention achieves a comprehensive assessment and classification of the full-structure distress status of the road unit's surface and base layers through strict spatiotemporal alignment and multimodal feature joint encoding.

This application relates to the field of high-resolution orthophoto recognition technology, specifically to a method for identifying tobacco-crop intercropping patterns based on multi-layer semantic segmentation and row structure analysis. The method includes the following steps: acquiring high-resolution orthophotos of the target area; inputting the images into an improved HTC model for pixel-level semantic segmentation to distinguish the background, flue-cured tobacco, and other crops; identifying intercropping interference zones formed by flue-cured tobacco and its associated crops based on the semantic segmentation results and distance transformation; and extracting the row structure and identifying the intercropping pattern of the interference zones. The core of this application is to leverage the differences in spectral response, texture, and spatial geometric arrangement of crops in high-resolution orthophotos. Through a multi-layered collaborative process of "semantic segmentation – neighborhood selection – row structure analysis – pattern discrimination," it achieves accurate identification of intercropping patterns between flue-cured tobacco and other crops, realizing specialized modeling and representation capabilities for the typical intercropping pattern of alternating rows and columns of flue-cured tobacco and other crops.

This application relates to the field of data processing technology, specifically disclosing a method and apparatus for generating true orthophotos from satellite images based on three-dimensional Gaussian projection. The method includes: acquiring a multi-view satellite image set of a target area and determining the initial imaging parameters of each satellite image; performing feature matching on each satellite image based on the initial imaging parameters to establish a two-dimensional feature point correspondence across images; generating an initial sparse point cloud based on the initial imaging parameters and the two-dimensional feature point correspondence; jointly optimizing the initial imaging parameters and the initial sparse point cloud to obtain optimized imaging parameters and a sparse point cloud; constructing an initial three-dimensional Gaussian scene based on the optimized sparse point cloud; iteratively optimizing the scene to obtain an optimized three-dimensional Gaussian scene; constructing virtual imaging parameters based on the optimized imaging parameters and determining the orthophoto projection method accordingly; and generating a true orthophoto image based on the optimized three-dimensional Gaussian scene and the virtual imaging parameters using the orthophoto projection method.

This invention discloses a method, system, storage medium, and product for unmanned vehicle (UAV) path planning in field environments under UAV collaboration, belonging to the field of UAV path planning, and solving the problem of low data accuracy in the global path planning of UAVs in existing technologies. This invention acquires a top-down visible light image of the passable area and performs image pose calculation and sparse point cloud data generation; then generates a two-dimensional orthophoto and digital surface model of the passable area; a semantic segmentation model performs semantic segmentation on the two-dimensional orthophoto and extracts ground feature classification data; after gridding the digital surface model and ground feature classification data, slope values ​​and ground feature classification values ​​are calculated; based on the slope value and ground feature classification value of each grid cell, the passage cost of each grid cell is calculated; the starting and ending coordinates of the UAV are input, and UAV path planning is performed based on the path planning algorithm and the obtained passage cost map. This invention is used for UAV-collaborated path planning in field environments.

This invention discloses a method and related apparatus for identifying and calculating the installation progress of fixed supports in photovoltaic power plants, belonging to the field of photovoltaic power generation technology. First, orthophotos and design drawings of the fixed supports in the photovoltaic power plant are acquired, resulting in point cloud data of the fixed supports. Then, the point cloud data of the fixed supports is preprocessed and fitted to obtain a target plane. Matching the target plane with the design drawings allows for accurate calculation of the installation progress of the fixed supports. This invention reduces the influence of noise points on plane fitting through the preprocessing of point cloud data, thus obtaining a more accurate target plane. By promptly identifying deviations and errors during the installation process, timely corrections and adjustments can be made, thereby avoiding potential safety hazards. This helps ensure the stability and safety of the power plant.

This invention provides a method for identifying the three-dimensional particle size of large debris flow deposits using UAV aerial photography combined with AI recognition and GIS calculation. The method involves using a UAV for aerial photography, importing the images into Agisoft Metashape to construct a dense point cloud, generating DEM data, and simultaneously producing orthophotos. The DEM data and orthophotos are then imported into software to draw contour lines and stitch together multi-source data. The orthophotos are imported into a SAM (Single Aspect Model) AI segmentation model, outputting standardized vector contours. The rock vector contours output by the SAM model are imported into software to calculate the horizontal equivalent particle size and maximum particle size. The rock contours are overlaid with the DEM data to calculate the vertical particle size, integrating them to generate a three-dimensional particle size table. This generates rock particle size data and exports multi-dimensional particle size distribution mapping results (horizontal and vertical). This invention achieves the calculation of horizontal and vertical dual-dimensional particle size of large rocks from UAV imagery, improving the accuracy, efficiency, and completeness of rock particle size measurement.

The application discloses a kind of multi-source surveying and mapping data intelligent fusion processing method and system, it is related to digital image processing technical field;The method includes the following steps: obtaining the multi-temporal orthophoto of flexible membrane surface to be processed area, multi-temporal photogrammetry elevation and multi-temporal laser elevation, constructs joint elevation field and extracts the candidate valley line set carrying cross direction information, further determines the geometric low center and mirror flash migration amount, constructs valley line locking weight, extracts global lock valley center line by particle swarm algorithm, and according to this, multi-temporal orthophoto, photogrammetry elevation and laser elevation are fused, and fusion image and fusion elevation are obtained;The application can improve the stability of real valley line positioning of flexible membrane surface, weaken mirror flash migration interference, improve the time sequence consistency and geometric reliability of fusion result.

This invention discloses a method for generating a main flight path based on the spatial relationship of seedling points under orthophoto photography, belonging to the field of agricultural engineering and intelligent navigation technology. This invention uses the set of seedling point coordinates extracted from orthophotos as input, and combines a dynamic search radius mechanism and a direction constraint strategy to gradually generate a continuous and reasonable main flight path. The system includes a seedling point identification module, a spatial filtering module, a direction constraint module, and a path generation module. The main flight path generated by this invention enables the main walking mechanism of the equipment to be precisely positioned between crop rows, while its arm-reaching working mechanism covers a preset number of crop rows. Furthermore, it directly uses the actual seedling point coordinates extracted from orthophotos as the basis for path generation, maximizing the fit between the actual growth positions of the crops. This allows the walking mechanism of the agricultural equipment to accurately traverse between seedling points, effectively avoiding the crop root area, significantly reducing the risk of damage to crops from the equipment wheels, and improving the safety of field operations.

This invention discloses an orthophoto mosaicking method using an adaptive cost A* algorithm. The method includes obtaining initial mosaicking lines between pairs of orthophotos to be mosaicked to construct an initial mosaicking line network; optimizing the initial mosaicking line network using an improved A* algorithm, i.e., using an adaptive threshold algorithm to determine traversable areas, and constructing a novel cost function to obtain the improved A* algorithm; then using the improved A* algorithm to optimize each initial mosaicking line in the initial mosaicking line network to obtain optimized mosaicking lines, thereby completing the mosaicking between orthophotos; the adaptive threshold algorithm uses the average grayscale value of the overlapping area of ​​adjacent images as the global threshold, and the smaller of the average grayscale values ​​of N*N blocks in the left and right images where the current search point is located as the local threshold. Based on the maximum grayscale difference cost of the current search point, it adaptively selects either the global threshold or the local threshold as the adaptive threshold to determine whether the current search point is a traversable area.

This invention provides a track health status detection method based on high-speed railway space-air-ground collaborative sensing, comprising: based on a detection mode, retrieving surface deformation interferograms, UAV orthophotos, train vibration data, and track circuit monitoring data corresponding to the target mileage segment under the detection mode from a pre-constructed heterogeneous data spatial index database; extracting static environmental features of the target mileage segment based on the surface deformation interferograms and UAV orthophotos, and extracting dynamic response features based on the train vibration data and track circuit monitoring data; performing cross-attention operations to fuse and obtain global track state features; inputting the global track state features into a classifier to obtain the track health status detection result of the target mileage segment as healthy state, potential defect state, or defect state. This invention achieves efficient fusion and accurate evaluation of heterogeneous asynchronous data, effectively improving the accuracy and robustness of track health status detection.