38 results about "Georeference" patented technology

The invention provides an ocean live big data fusion method based on deep learning, and relates to the technical field of ocean big data fusion, and the method comprises the steps: obtaining meteorological driving variables, ocean historical reanalysis data, satellite observation data and ocean field observation data, and performing unified format conversion processing on the basis of the acquired meteorological driving variables, the ocean historical reanalysis data, the satellite observation data and the ocean field observation data, and configuring a unified ocean environment feature tensor set. A unified marine environment feature tensor structure is configured, meteorological driving variables, marine historical reanalysis data, satellite remote sensing data and on-site buoy observation data are subjected to space resampling, time interpolation and missing complementation and are unified into a space-time grid under a geographic reference coordinate system, a four-dimensional tensor set is configured, and the four-dimensional tensor set is obtained. The problem of fusion obstacles caused by non-uniform data formats and inconsistent temporal-spatial resolutions in the prior art is effectively solved.

A system and method combines photometric crop surveillance with a ground-mounted directional, variable-flow sprayer that compensates for wind to enable selective, per-patch application of water, fertilizer, or crop-protection products. Photometric imagery is processed to produce georeferenced treatment targets and recommended application quantities. Wind-field estimations and local wind-sensing are used to compute aim offsets, droplet size, and flow / dwell and shielding parameters that compensate for advection and turbulence. The system commands a sprayer with aimable nozzles and proportional flow control with per-target, wind-compensated parameters to selectively treat sub-areas at different doses, while minimizing drift. Follow-up photometric verification and logged wind telemetry enable closed-loop learning of drift / advection models.

The invention discloses a map updating method and device, a server, a system and a medium, and is suitable for updating and maintaining a high-precision map, and the method comprises the steps: collecting the track information of a vehicle and a synchronous road environment image collected through a vehicle-mounted visual sensor; recognizing and extracting perception elements including lane lines and ground marks from the image; converting the identified perception elements from an image pixel coordinate system to a geographic coordinate system, and generating perception elements of geographic reference; correlation matching is carried out on the perception elements of the geographic reference and corresponding map elements in a map database, and the quality of each time of matching observation is evaluated to generate a corresponding matching probability value and a quality weight; based on the matching probability value and the quality weight, calculating the support degree of single observation on map element state hypothesis; fusing the observation support degrees from a plurality of vehicles and a plurality of moments, and calculating a posterior probability of map elements needing to be updated; and deciding whether to update the map elements according to the posterior probability.

The proposed technique introduces embodiments of a computer-implemented method for interpreting image delineations as vector objects and topological extraction from segmentation by visual computational methods applied to sections (slices) of seismic volumes, in order to aid geological interpretation and sampling of parameters originating from the fracture network and its topology. Embodiments of a developed method integrates a software / application that allows the loading and generation of statistical data related to the fracture network while maintaining georeferencing and scale of the two-dimensional input data. In addition, a fracture segmentation method is shown that uses pyramid image smoothing (decomposition into hierarchical levels of resolution) in order to reduce the amount of details and aid the identification of main faults or fractures. The segmentation after this smoothing is based on adaptive thresholding segmentation.

A system is provided for localizing an autonomous work vehicle within mapped agricultural environments when GPS may be unavailable. Range sensors capture LiDAR images that are segmented by a trained model into features such as trunks and canopy. Trunk points are clustered to compute centroids, which are used to estimate left and right tree lines and a lane centerline relative to the vehicle. This sensor-derived centerline is compared with georeferenced lane and row geometry stored in a digital map. A particle filter maintains multiple pose hypotheses, propagates them using vehicle odometry, and weights them according to alignment between the lane centerline relative to the vehicle and the mapped lane geometry for the particle. Resampling and averaging of high-weight hypotheses produces an adjusted pose that corrects for lateral and heading errors. The adjusted pose serves as input to a navigation controller that issues steering and speed commands, enabling accurate autonomous operation.

The invention discloses an unmanned aerial vehicle road disease detection and analysis system and method based on a YOLO algorithm, and the method comprises the following steps: synchronously collecting a road image and geographic coordinates through an unmanned aerial vehicle, and constructing an image data set with geographic reference; constructing an initial YOLO target detection model based on the data set; introducing an additional detection layer for detecting a pixel area into the model, and embedding a channel attention mechanism into the feature extraction network to obtain an improved YOLO target detection model; reasoning a data set by using the model, and outputting a disease category, a bounding box coordinate and a confidence coefficient; pixel coordinates are converted into geographic coordinates through a coordinate conversion algorithm, and disease records with accurate geographic coordinates are generated; and optimizing and updating the model based on the record as an incremental training sample. According to the method, the disease detection precision and the small target identification capability are effectively improved, and the long-term adaptability and generalization performance of the model are enhanced through a closed-loop incremental learning mechanism.

The invention discloses a man-machine collaborative remote sensing image intelligent labeling method and device based on a visual large model. The method comprises the steps of obtaining remote sensing image data to be labeled, and performing image enhancement processing to obtain enhanced remote sensing image data; inputting the enhanced remote sensing image data into a visual large model for mask segmentation processing, and outputting a plurality of target segmentation masks and corresponding confidence coefficients; marking layer graphs with different colors on the to-be-marked remote sensing image data to form marked remote sensing image data, and displaying the marked remote sensing image data to a user; and the user performs confirmation operation, and stores the labeled remote sensing image data as a GeoTIFF file format image with geographic reference information. In the embodiment of the invention, the image labeling precision of complex ground objects, multiple regions and the like is obviously improved.

The application discloses a kind of unmanned aerial vehicle image and multi-modal map area cutting method and system based on attitude information, first obtain the unmanned aerial vehicle image containing EXIF metadata, extract GPS information and attitude parameter, calculate image ground real projection center point longitude and latitude in combination with sensor parameter graph;Again, according to pixel size, sensor physical size etc., calculate ground resolution and geographical coverage, correct the projection angle to get four real longitude and latitude, then four corner coordinates are converted to map projection coordinate system, generate cutting window in combination with base map geographic reference information, high-precision cutting is carried out in infrared, visible light map base map respectively, corresponding result is carried out after spatial alignment and consistency verification, and relevant data is saved in JSON format structure.The method realizes accurate area matching and cutting, improves the spatial accuracy and processing efficiency of multi-source remote sensing data fusion and target identification, and is suitable for remote sensing surveying and mapping, resource investigation and emergency monitoring and other scenes.

A computer-implemented georeferencing method (100), the method (100) comprising: receiving (102) sensor data representing a portion of the surface of the earth, wherein the sensor data is obtainable or obtained by a sensor included in or attached to a vehicle, preferably an aerial vehicle; segmenting (106) the sensor data to generate a sensor data segmentation map comprising one or more segments and a set of labels, wherein each of the labels relates to a segment and is indicative of a type of the segment; determining (118) a labelled reference segmentation map obtainable by segmenting georeferenced reference data; and comparing (126) the sensor data segmentation map to the reference segmentation map to determine a georeference of the sensor data segmentation map.

The invention relates to a ground station system (38a-c) for georeferencing aerial images, in particular for calibrating and / or surveying drone photogrammetry data, of an area (10a-c), such as, for example, a construction site, an archaeological find and / or excavation site or an infrastructure facility, with a plurality of control point units (12a-c, 14a-c, 16a-c) that can be arranged distributed over the area (10a-c), wherein each of the control point units (12a-c, 14a-c, 16a-c) has an optical marker (18a-c) on at least one surface that is uniquely identifiable from the air. It is proposed that each of the control point units (12a-c, 14a-c, 16a-c) is associated with a Global Navigation Satellite System (GNSS) module (20) for acquiring its instantaneous geographic position and a Wide Area Network (WWAN) module (28a-c) for at least wirelessly transmitting the acquired instantaneous geographic position, wherein at least one of the control point units (12a-c, 14a-c, 16a-c) is integrated into a functional object (22a-c, 24a-c, 26a-c) that is at least temporarily assigned to the area (10a-c) and performs a main function within the area (10a-c) that is different from georeferencing.

Methods and systems for improved generation and georeferencing of floor plans are presented. In one embodiment, a method is presented that includes receiving images that depict sheets of a blueprint of a structure. Subsets of the images depicting floor sheets and elevation sheets may be identified. Exterior contours may be extracted from the images depicting floor sheets and elevation contours may be extracted from the images depicting elevation sheets. A corresponding structure within a three-dimensional map may be identified based on the exterior contours and the elevation contours. A three-dimensional contour of the exterior of the structure may be extracted from the three-dimensional map.

Described are systems and techniques for visualization and manipulation of non-composite oblique aerial imagery. Information can be obtained for an annotation feature for overlay on a displayed oblique image with an oblique viewpoint of a scene within a geographic area. Three-dimensional (3D) model data can be obtained for a portion of the geographic area and including 3D information of object surfaces within the scene. A 3D representation of the annotation feature can be generated using the 3D model data and georeferenced coordinates determined for the annotation feature. An oblique projection of the 3D representation of the annotation feature into the oblique viewpoint of the scene can be generated to include portions of the annotation feature unobstructed by the object surfaces within the scene. The oblique projection of the annotation feature can be output as a two-dimensional (2D) overlay on the displayed oblique image.

The application discloses a kind of based on Gaussian distribution's remote sensing image pixel and sampling point matching method and device, it is related to remote sensing data processing and environmental monitoring technical field.The method first obtains ground sampling point longitude and latitude coordinate and the remote sensing image data of coverage area, and extracts image spatial resolution, the geographical reference information of pixel;Then according to image resolution to determine initial weighted window range, in combination with preset boundary weight threshold and maximum coverage distance, calculate Gaussian kernel standard deviation;With sampling point as center, construct two-dimensional Gaussian weight field, according to the Euclidean distance of sampling point and the center of pixel in image to calculate weight, and according to the pixel set of dynamic determination involved in calculation according to Gaussian kernel coverage range;Finally, the reflectivity of each pixel is multiplied by corresponding weight and is normalized, and obtains the estimated reflectivity at sampling point.The method can effectively fuse neighborhood pixel information when sampling point and pixel center do not coincide, improve reflectivity extraction precision.

The invention discloses an unmanned aerial vehicle image and multi-modal map area cutting method and system based on attitude information, and the method comprises the steps: firstly obtaining an unmanned aerial vehicle image containing EXIF metadata, extracting GPS information and attitude parameters, and calculating the latitude and longitude of a real projection center point of the image ground in combination with sensor parameters; calculating ground resolution and geographical coverage according to pixel size, sensor physical size and the like, correcting projection angles to obtain real longitudes and latitudes of four corners, transferring coordinates of the four corners to a map projection coordinate system, generating a cutting window by combining geographical reference information of the base map, and performing high-precision cutting in the base map of the infrared and visible light map respectively to obtain a three-dimensional image; after a corresponding result is obtained, space alignment and consistency verification are carried out, and related data are stored in a JSON format in a structured mode. The method realizes accurate region matching and cutting, improves spatial precision and processing efficiency of multi-source remote sensing data fusion and target identification, and is suitable for various scenes such as remote sensing surveying and mapping, resource investigation, emergency monitoring and the like.

This invention belongs to the field of image reconstruction technology using 3D Gaussian sputtering. It discloses a reconstruction method, system, and storage medium that directly converts color point clouds into 3D Gaussian sputtering. By using the color point cloud as the sole input, an initial color image and corresponding depth map are generated using the intrinsic and extrinsic parameters of a virtual camera. The virtual camera's intrinsic and extrinsic parameters are set to seamlessly incorporate absolute spatial coordinate information into subsequent image enhancement and 3D Gaussian sputtering training processes, ensuring that the final reconstructed 3D Gaussian sputtering model has a clear geographic reference. The initial color image and corresponding depth map are then processed using a diffusion model for style enhancement and hole completion, outputting an enhanced image. Finally, the enhanced image and depth map are used together for 3D Gaussian sputtering training. Through joint optimization of photometric, depth, and multi-view consistency, the final scene representation is obtained, achieving direct generation from point clouds to 3D Gaussian sputtering and improving the realism, texture, and multi-view consistency of the reconstructed image.

The invention relates to a method for controlling a drone (100) provided with a surface treatment device and with position sensors, the method comprising: - flying the drone (100) according to the flight plan (400) at a distance from a surface to be treated; - the flight plan (400) of the drone (100) being defined in a georeferenced 3D point cloud (300) representing the surface to be treated; - the flight plan (400) comprising a flight path in which a set of successive positions that the drone (100) must adopt are defined, the flight path being such that the drone (100) passes at a treatment distance from the surface to be treated, the coordinates of the points of the surface being known; - defining the position of the drone (100) during flight and selectively activating the surface treatment device depending on the position of the drone (100).

A method for controlling the trajectory of a motor vehicle (MV) comprising a vehicle localization system including a single-antenna satellite geolocation unit (ANTE) and an inertial geolocation unit cooperating with a georeferenced high-definition map; said method, implemented by said localization system, consisting of exploiting the rear axle drift angle (RAR) of said vehicle to estimate, in real time, the yaw angle, or heading, of the vehicle throughout the determined trajectory of the vehicle, particularly during turns, using a dynamic model representing the vehicle, of the bicycle model type (BM), in an x, y reference frame, taking into account the position of the satellite geolocation unit's antenna (ANTE) in the vehicle's x, y reference frame and the antenna drift angle (ANTE). (Figure 1)

A data gathering module receives georeferenced data from a UAV over a target area. A processing module receives the captured georeferenced sensor data and analyzes the data to create detailed landscape condition maps using techniques such as machine learning and vegetation indices (NDVI, NDRE). In some embodiments the module automatically identifies and segments different landscape features such as turf, plants, weeds and hardscapes. The data processing module pinpoints specific problem areas or polygonal zones that require remediation for issues such as water stress, nutrient deficiency, weed infestation or pest damage and generates a specific remediation recommendation. An action module generates a set of targeted commands to accomplish the remediation recommendation, and a verification module compares the pre- and post-mediated areas to generate a verification report.

The application relates to a land survey area measurement method, device, equipment and medium. The method comprises the following steps: pre-processing and scene complexity evaluation are performed on surveying and mapping images, a comprehensive state vector is generated in combination with a computing resource state and an aircraft state; the comprehensive state vector is input into a deep reinforcement learning decision model to obtain an inference configuration action; the surveying and mapping images are cropped and / or scaled according to the inference configuration action to generate adaptive input images, and a semantic segmentation model is used to perform forward inference on the adaptive input images to obtain a semantic segmentation result image; area results and geographical boundary information are generated and cached based on geographical reference information and the semantic segmentation result image; spatial fusion and deduplication processing are performed on all the surveying and mapping images based on the geographical boundary information and the area results, a target land class continuous distribution map is generated, and the total area of the target land class in the surveying and mapping area is calculated based on the target land class continuous distribution map. The method can improve the task completion rate and the overall surveying and mapping efficiency.

The invention discloses an offset image registration method based on a side window Gaussian edge condition diffusion model, and belongs to the technical field of remote sensing image processing. The method comprises the following steps: firstly, acquiring a reference image and a to-be-registered offset image, and performing coarse registration to obtain an initial transformation parameter and a coarse registration image; then extracting a side window Gaussian edge graph of the reference image as a conditional constraint, and performing iterative refining on the coarse registration image by using a diffusion model to obtain a refined image; and determining a residual transformation parameter according to the refined image and the reference image, obtaining a final transformation parameter in combination with the initial transformation parameter, and finally transforming the offset image to obtain a registered image. In the aspect of registration precision, stable sub-pixel registration is realized through side window Gaussian edge strong constraint, and large-scale training data and extra geographic reference information are not needed; and non-linear radiation difference and large-scale geometric offset interference can be effectively resisted.

The application discloses a kind of based on side window Gauss edge condition diffusion model offset image registration method, belong to remote sensing image processing technical field.The method first obtains reference image and the offset image to be registered, and carries out coarse registration and obtains initial transformation parameter and coarse registration image;Then extract the side window Gauss edge graph of reference image as conditional constraint, utilize diffusion model to the coarse registration image is iterated and refined, and obtains refined image;According to refined image and reference image, determine residual error transformation parameter, combined with initial transformation parameter to obtain final transformation parameter, finally to offset image is transformed to obtain after registration image.The application is in registration precision aspect, through side window Gauss edge strong constraint, realizes stable subpixel level registration, without large-scale training data and additional geographic reference information;Effectively resist nonlinear radiation difference and large-scale geometric offset interference.

This application provides a map generation method and system based on multi-source heterogeneous geographic information processing. It acquires three types of heterogeneous data: a geographic element attribute database, a geographic coordinate semantic database, and a geographic reference image database. Spatial spectrum reconstruction processing is then performed to generate a spatial spectrum cube. Belief propagation fusion processing is then used to construct a belief network from the spatial spectrum data. Combined with spatially constrained probability field optimization and D-S evidence synthesis, a geographic evidence chain is generated. Next, spatiotemporal field reconstruction processing is performed to separate the static and dynamic states of the geographic evidence chain and couple them into fields, constructing a hyperdimensional spatiotemporal map. Finally, holographic projection conversion processing is used to perform phase field compression and coherent diffraction reconstruction on the hyperdimensional spatiotemporal map, generating a holographic geographic projection map that supports 3D interaction. This method achieves the automatic generation of a structurally unified, highly reliable geographic map that supports dynamic behavioral reasoning and true 3D interaction from multi-source heterogeneous geographic data, significantly improving the decision-making value and cognitive efficiency of geographic information.

A system for determining locations of blast fragments may include: a three-dimensional (3D) imaging system mounted on an excavation device and configured for viewing a portion of a pile of blast fragments and configured for capturing at least one image of the viewed portion of the pile of blast fragments; a positioning system configured for providing a location and heading of the excavation device; and a processor. The processor may be configured to execute instructions to perform image processing operations upon the images of the viewed portion of the pile of blast fragments, to: identify a reference location in the viewed portion of the pile of blast fragments; determine the reference location in a geographic coordinate system; identify a plurality of individual blast fragments in the viewed portion of the pile of blast fragments; and determine a distinct geographic coordinate corresponding to each of the plurality of individual blast fragments.

The invention concerns a method (1) to detect deformations of, and / or damages to, structures permanently arranged on the earth's surface. In particular, said method (1) comprises: acquiring (11) georeferencing data indicative of geographical reference positions of predefined points of interest of a given structure to be monitored permanently arranged on the earth's surface, wherein said predefined points of interest are representative of a 3D geometry of the given structure without deformations and damages; acquiring (12) a SAR image of an area of the earth's surface where the given structure is arranged, wherein said SAR image is associated with a given reference coordinate system; transforming (13) the geographical reference positions of the predefined points of interest into corresponding expected positions in the given reference coordinate system associated with the acquired SAR image so as to carry out a reprojection of the 3D geometry of the given structure without deformations and damages on the acquired SAR image; identifying (14) in the acquired SAR image the predefined points of interest of the given structure; determining (15) actual positions in the given reference coordinate system associated with the acquired SAR image of the predefined points of interest identified in said SAR image; making a comparison (16) between the expected positions of the predefined points of interest and the corresponding actual positions in the acquired SAR image; and detecting (17) one or more deformations of, and / or one or more damages to, said given structure on the basis of the comparison made.

The present invention relates to a method for calculating one or more isovists of a physical or extended reality environment, comprising the following steps: providing at least one electronic acquisition device configured to obtain a plurality of georeferenced data, such as time, position, orientation, and configured to take frames of the physical or extended reality environment; calculating a positioning parameter for each user, each positioning parameter being representative of a positioning point of a respective user in the physical or extended reality environment and being determined from the georeferenced position data acquired when said respective user uses the electronic acquisition device to take a frame of the physical or extended reality environment; calculating an aim parameter for each user, each aim parameter being representative of the direction of a respective frame of the physical or extended reality environment taken by a user at the respective positioning point and being calculated from the georeferenced orientation data acquired when such respective user uses the electronic acquisition device to take the frame; associating each positioning parameter with a respective aim parameter to define respective data units; grouping into respective positioning sets data units having respective positioning parameters differing from each other by a value lower than a positioning threshold value and respective aim parameters differing from each other by a value lower than an aim threshold value; calculating at least one isovist for each positioning set as a function of the respective positioning and / or aim parameters; representing each calculated isovist in a graphical user interface of an electronic display device.

The invention relates to a computer-implemented-method for detecting underground structures through analysis of time-series optical remote-sensing data. Unlike approaches that rely on single images, vegetation indices, or pairwise change detection, the method analyzes temporal spectral behavior of the ground surface as an information-bearing signal indicative of subsurface anomalies. The method comprises obtaining a plurality of georeferenced optical images acquired over an extended time-period, spatially aligning the images, extracting spectral values at defined spatial sampling loci, and converting the extracted spectral values into standardized single-channel values. A multi-stage normalization process is applied to mitigate natural soil heterogeneity and transient environmental effects while amplifying persistent temporal patterns associated with underlying subsurface structures. The method generates an output spatial dataset, optionally visualized as a synthetic map, to highlight locations indicative of underground anomalies. The approach provides a deterministic, training-independent, and computationally-efficient-solution suitable for applications including archaeology, underground infrastructure monitoring, and related fields.

In an embodiment, an apparatus for processing of high-altitude images is presented. An apparatus for processing high-altitude images may include a processor and a memory communicatively coupled to the processor. A memory may contain instructions configuring a processor to obtain image data. A processor may be configured to perform image processing on image data specific to an atmosphere associated with the image data. A processor may be configured to remove, through application of a machine learning model, obscure image data from processed image data to generate viable images. A processor may be configured to geolocate viable images based on obtained georeference data to generate geolocated image data. A processor may be configured to mosaic geolocated image data to produce a georeferenced map.

The object of this present Patent of Invention application idealizes an autonomous payment system for amounts for use in transport infrastructures such as barrier or free-flow technologies, georeferencing, geolocation and license plate reading for information about any vehicle that crosses vehicle passage points (4), for monitoring or payment without user intervention (6) which will have the amounts debited from their wallet or through credit card.

The application relates to a land survey area measurement method, device, equipment and medium. The method comprises the following steps: pre-processing and scene complexity evaluation are performed on surveying and mapping images, a comprehensive state vector is generated in combination with a computing resource state and an aircraft state; the comprehensive state vector is input into a deep reinforcement learning decision model to obtain an inference configuration action; the surveying and mapping images are cropped and / or scaled according to the inference configuration action to generate adaptive input images, and a semantic segmentation model is used to perform forward inference on the adaptive input images to obtain a semantic segmentation result image; area results and geographical boundary information are generated and cached based on geographical reference information and the semantic segmentation result image; spatial fusion and deduplication processing are performed on all the surveying and mapping images based on the geographical boundary information and the area results, a target land class continuous distribution map is generated, and the total area of the target land class in the surveying and mapping area is calculated based on the target land class continuous distribution map. The method can improve the task completion rate and the overall surveying and mapping efficiency.

A system for generating time encodings. The system comprises a timestamp generating circuit configured to periodically generate a timestamp; an internal clock system operationally connected to the timestamp generating circuit, whereby the timestamp generating circuit is further configured to periodically generate the timestamp based on the internal clock; at least one LED arranged in at least one array of the LED or LEDs, obtaining respectively for each array a LED clock. The timestamp generating circuit is further configured to convert the timestamp into a binary form, and control the LEDs in the at least one array to visually code in accordance with the binary form, whereby each one of the LEDs represents a bit in the binary form. The system further comprises at least one camera, each of the at least one camera having a corresponding field of view, whereby the at least one LED clocks are placed within a respective corresponding field of view of the at least one camera. The system further comprises a processing system configured to record image data from images output from the at least one camera, comprising for each image at least a representation of the LED clock visually coding the binary form physically, encoded with the image for retrieval of information.