20 results about "Geo referencing" patented technology

A system and method for closed-loop tracking of advertising events through offer redemption with artificial intelligence feedback. A session identifier links device, pass, content, and contextual data throughout a content playback session. Advertisements dynamically inserted into the content stream inherit the session identifier. An offer platform generates offers linked to the session identifier and pass identifier, enabling tracking through distinct lifecycle states from push to redemption. A database captures timestamped and geo-referenced data for each event. An artificial intelligence engine uses redemption and avail events as verified ground-truth outcomes for training machine learning models, enabling supervised learning that establishes causal relationships between ad exposure and purchasing activity. An inventory gateway receives merchant product inventory data. An optimization engine employs demand forecasting and reinforcement learning to generate inventory-driven triggers and offer recommendations. A merchant interface presents analytics for inventory optimization.

A method detects a spatial arrangement of a physical object by assigning an attribute to a selection of uniquely identifiable global grid elements of a reference grid network. The method includes providing a global reference grid network with a geographical reference point defined vector-object-independently and global grid elements, which are adjacent to one another without overlap and are uniquely identifiable within the global reference grid network and are georeferenced with regard to a provided reference coordinate system, providing a vector object with at least one vertex, to which a geographical coordinate of the geographical reference coordinate system is assigned, determining and selecting those global grid elements that overlap with the vector object, and assigning an object attribute linked with the vector object or a further attribute to the grid elements selected and storing the selected grid elements with assigned attributes.

The invention discloses a robust measurable modeling method fusing three-dimensional reconstruction and geographical registration, and belongs to the technical field of three-dimensional modeling. The method comprises the following steps: acquiring source point cloud data generated by three-dimensional reconstruction and target point cloud data with geographic coordinate reference; initial registration is completed based on a Umeyama algorithm; performing iterative optimization on the initial transformation parameters by adopting a Meishan-iterative reweighted least square-Huber weight algorithm, realizing geographic registration through robust standard deviation estimation, Huber weight distribution and convergence judgment, and generating a measurable geographic space three-dimensional model; volume calculation, distance measurement and spatial analysis can be performed based on the model. According to the method, noise and abnormal point interference are suppressed through a robust iterative optimization mechanism, the registration precision is high, the generated model has unified geographic coordinate reference and high-precision measurement functions, the problems that a traditional three-dimensional model lacks geographic reference and is poor in noise immunity are solved, and the method can be widely applied to scenes such as engineering surveying and mapping and space planning.

Method for supporting data communication between a computing unit (2) of a vehicle (1) and at least one vehicle-external service server (10) using a support system (8) separate from the vehicle (1) and the service server (10), which communicates with the computing unit (2) of the vehicle (1) and the at least one service server (10), comprising the steps: - Receiving a georeference (GR) by the support system (8) from the service server (10) and determining a map-specific map reference (KR) associated with the received georeference (GR) in a digital map of the support system (8) by the support system (8) using a georeferencing procedure associated with the received georeference (GR), - Generating a unique code (X1 to XX) for the determined map reference (KR) by the support system (8) and storing the code (X1 to XX) assigned to the determined map reference (KR) and / or the received georeference (GR) in a memory of the support system (8), wherein the code (X1 to XX) is formed by a smaller amount of data compared to the received georeference (GR), - Receiving the code (X1 to XX) by the computing unit (2) of the vehicle (1), - Transmission of a request message with the code (X1 to XX) from the computing unit (2) of the vehicle (1) to the support system (8), wherein the request message instructs the support system (8) to transmit to the computing unit (2) of the vehicle (1) a georeference (GR) associated with the code (X1 to XX) and generated according to a georeferencing procedure defined by the request message and / or a map reference (KR) associated with the code (X1 to XX) and suitable for a digital map format defined by the request message, and - Transferring the georeference (GR) and / or the appropriate map reference (KR) defined by the request message from the support system (8) to the computing unit (2) of the vehicle (1) based on the received request message.

The invention discloses a farmland boundary construction and operation area calculation method and device, a system and a storage medium. The method comprises the following steps: acquiring a farmland image and GNSS / IMU metadata thereof; according to the farmland image and the GNSS / IMU metadata thereof, extracting semantic features for distinguishing crops, ridges and non-cultivated land and capturing detail features for fine trend of the ridges; simulating a surveying and mapping person to walk along the ridge for dotting according to the semantic features and the detail features to obtain ridge polygon vertexes; obtaining an offset field from each pixel to a geographic coordinate according to the GNSS / IMU metadata; according to the ridge polygon vertexes and the offset field, obtaining a geographical polygon; obtaining a prediction area according to the geographical polygon; and outputting a georeferenced vector boundary and a credible surface based on end-to-end training optimization. By adopting the technical scheme of the invention, the precision of the operation area of the agricultural machine is greatly improved, and the field map distribution is accurately obtained.

The application discloses a kind of based on multi-source spatial resolution's remote sensing building target segmentation measurement method, belong to remote sensing image processing technical field.Processing remote sensing image to be obtained, and receive the model type selected by user, weight path and optional manual input resolution;Execute spatial resolution analysis;Call building target segmentation model to infer image, output building candidate binary mask or probability graph, and obtain building area mask according to threshold strategy;Building area mask is executed target level measurement analysis;Based on multi-source spatial reference information, execute real area measurement;According to target level analysis result, generate superimposed rendering graph, black background color mask, statistical table and structured export file, realize the closed-loop output of segmentation, measurement, review and export.The present application is used to solve the problems that real area output is not reliable in existing segmentation system, target level measurement results are inconsistent, and no geographic reference image misoutput real area.

This invention provides a method and system for precise source tracing of dust pollution sources in open-pit mines based on AI vision. The method includes: constructing a unified spatiotemporal reference and performing time synchronization and spatial mapping on multi-view videos; obtaining dust plume regions and pixel-level confidence scores based on a dust plume detection model, and generating purification weight parameters by combining imitation learning to construct a cross-view consistency constraint set; establishing a three-dimensional reconstruction mesh in a geographic reference coordinate system and performing multi-view fusion optimization to form a three-dimensional dust plume concentration field; obtaining wind field parameters and generating voxel wind speed vectors, constructing a source term inversion model to solve for candidate pollution source locations and candidate release intensities; calculating source tracing confidence scores based on candidate release intensities and inversion consistency indices, screening the final pollution source location, and generating a structured evidence set bound to the final pollution source location, which can achieve precise three-dimensional positioning of dust pollution sources under multi-source superposition and dynamic wind field conditions.

A priori georeferenced vegetative index data is obtained for a worksite, along with field data that is collected by a sensor on a work machine that is performing an operation at the worksite. A predictive model is generated, while the machine is performing the operation, based on the georeferenced vegetative index data and the field data. A model quality metric is generated for the predictive model and is used to determine whether the predictive model is a qualified predicative model. If so, a control system controls a subsystem of the work machine, using the qualified predictive model, and a position of the work machine, to perform the operation.

A measurement system for determining geo-reference data for a measurement area includes a first system receiving a video stream and selecting images therefrom to correlate with geographic x, y coordinates of the measurement area. The system includes a second system (i.e., aerial platform) to hover over the measurement area and a vertical camera scoring unit (VCSU). The aerial platform and VCSU further include an inertial measurement unit (IMU) to measure orientation and movement, a global navigation satellite system (GNSS) configured to determine geo-reference coordinates of a reference point, and a camera / mounting system configured to orient the camera towards the measurement area underneath the first system. The camera is oriented directly plumb downward to obtain plan view video across the measurement area and the second system computes a single absolute geodetic point position and communicates the video and data to the first system for point, distance, and velocity computation.

Provided is a radar detector that aids in management of unimportant sources, dynamically improving handling of such sources based upon previously-stored geographically-referenced information on such sources. The detector determines location of the detector, and compares it to locations of known sources, to improve handling of such detections. The detector may ignore detections received in an area known to contain a stationary source, or may only ignore specific frequencies or handle frequencies differently based upon historic trends of spurious police radar signals at each frequency, or based on geofenced locations. GPS is used to establish current physical coordinates. The detector maintains a list of the coordinates of known stationary source “offenders” and geofence coordinates or boundaries in nonvolatile memory. When a microwave or laser source is detected, it may compare current coordinates to geofence boundaries and / or sources in this list. Notification varies depending on the stored information and current operating modes.

A soil sampling and fertility assessment system consisting of a field division module for subdividing an agricultural field, a GPS-based sampling module for capturing georeferenced soil samples, a laboratory interface for processing soil analysis results, and a geodata processing module for geostatistical modeling to delineate homogeneous fertility zones for nutrient management.

The invention discloses a multi-modal neural network-based cross-institution soil particle size dynamic mapping method, which comprises the following steps of: obtaining international soil particle size grid data and corresponding geographic reference information, primarily screening multiple function interpolation methods to construct a cubic spline model, screening an optimal interpolation method of each grid element, and constructing a multi-modal neural network-based cross-institution soil particle size dynamic mapping model; and a multi-modal neural network model is trained by taking the grid elements as independent variables and the optimal interpolation method elements as dependent variables, so that the generalization ability of the model is remarkably improved, and the method can adapt to data of different regions. Compared with a'one-cutting 'mode of a traditional single interpolation method, the scheme can dynamically match the optimal interpolation method for each element of the to-be-processed raster data, fit data features, avoid regional data deviation and improve interpolation accuracy. The finally output grid result is real and accurate and can be used as reliable basic data of soil secondary calculation, secondary calculation distortion is avoided, high-quality data support is provided for soil research, agricultural planning and the like, and scientific application decision is guaranteed.

A priori georeferenced vegetative index data is obtained for a worksite, along with field data that is collected by a sensor on a work machine that is performing an operation at the worksite. A predictive model is generated, while the machine is performing the operation, based on the georeferenced vegetative index data and the field data. A model quality metric is generated for the predictive model and is used to determine whether the predictive model is a qualified predicative model. If so, a control system controls a subsystem of the work machine, using the qualified predictive model, and a position of the work machine, to perform the operation.

The invention discloses a batch image color structure analysis method and system and electronic equipment. According to the method, format recognition and screening are carried out on catalog images specified by a user, and qualified images are converted from RGB to HSV; automatically segmenting the hue channel according to a preset hue segmentation rule to generate each hue section segmentation image; and the segmentation result is converted into a single-band GeoTIFF classification grid, and the single-band GeoTIFF classification grid is written into a coordinate reference system, affine transformation and other geographic reference information, so that the output can be directly loaded and superposed in a GIS (Geographic Information System). The method comprises the following steps of: counting each hue category and the prime number and proportion of the whole image, calculating indexes such as Shannon, Simpson, Pielou, Margaleef, dominance, cold-warm hue ratio, variation coefficient and Euclidean color difference, and uniformly exporting in a table; gUI integration segmentation, GeoTIFF conversion and index calculation are carried out, and large-scale image automation, standardization and spatialization analysis is realized.

The application discloses a kind of highway road domain deformation field construction methods based on point cloud data, it is related to point cloud data processing and highway geological disaster monitoring technical field;The method comprises: according to period, collecting highway road domain laser radar original point cloud data, obtains each period digital ground model point cloud after being denoised, filtered and ground point extraction, geographic reference processing;With the first period point cloud as reference, subsequent each period point cloud is high-precision registration;With the first period registered point cloud as reference, the preliminary three-dimensional deformation field of each subsequent period is calculated;The preliminary three-dimensional deformation field is confidence evaluation and multi-scale fusion, and the final three-dimensional deformation field is obtained;The final three-dimensional deformation field is projected to generate two-dimensional deformation field atlas containing deformation variable, significance level and confidence information;The application realizes the automation of highway road domain millimeter level tiny deformation, high-precision extraction provides data support for early identification of geological disasters.

The invention relates to the technical field of forestry safety survey, and discloses a forestry survey engineering construction method, which comprises the following steps: firstly, entering a target forest region, and enabling survey personnel to complete field investigation on trees in the target forest region by observing the appearance characteristics of the trees, sampling and analyzing or using professional forestry tools; different tree species are classified and counted, at the same time, the ages of the trees are recorded, these surveys are referred to as early surveys, GPS receivers or devices are placed at key locations within a target forest region, the GPS receivers or devices are utilized to record the accurate geographic coordinates of each survey point for geographic reference for subsequent measurement and analysis, and the accuracy of the survey points is improved. The step is to ensure the geographic accuracy of subsequent data. According to the method, various sensors are arranged in the target forest area to monitor various data of the target forest area, so that the times of entering the target forest area to track data change and monitor ecological system health by reconnaissance personnel can be reduced, the workload of the reconnaissance personnel is further reduced, and the monitoring difficulty is greatly reduced.

A method for checking the integrity of the point positioning process obtained by determining the closest position, based on the adapted filtering of received GNSS signals, of a set of georeferenced reference positions for implementation on a mobile or fixed carrier, the method making it possible to check that the correlation function of the received GNSS signals is consistent with the selected reference position. The method for checking the integrity of the nearest location comprises: a first step for pre-selecting satellite signals suitable for the position integrity test, based on the minimum signal-to-noise ratio of the point correlation channel, a second step to exclude satellite signals that are not compatible with the predicted code phases, based on the absence of a Maximum At Center (MAC) of the early-punctual-late correlation points and meeting the non-integrity, non-continuity and alarm delay risks required for positioning system security, at least one maximum likelihood search step on the set of reference positions obtained by cumulating the elementary powers of the various selected satellites.

A position of an aircraft is computed, associated with the radius of a sphere centered around the estimated position of the aircraft and within which the actual position of the aircraft is located with a probability equal to or greater than a predefined threshold. For this purpose, the computation is based on distances from radio transmitters each having a fixed position in a geographical reference frame. The positions of the radio transmitters are converted into a terrestrial reference system, before solving a system of equations linking, in the terrestrial reference system, the position of the aircraft and the positions of the radio transmitters. After solving the system of equations, the position of the aircraft is converted from the terrestrial reference system into the geographical reference frame.

Method for generating georeferenced sensor data from a coordinate measuring machine (2), wherein a sensor (3) generates sensor data points (SDP), wherein a sensor data set (SDS, SDS_0, ..., SDS_14) is generated, wherein the sensor data set (SDS, SDS_0, ..., SDS_14) comprises at least one sensor data point (SDP) and at least one sensor data time point (SDZ), wherein the sensor (2) generates a trigger signal (TS) at a trigger frequency, wherein the trigger signal (TS) is transmitted to a device for determining a sensor position, wherein a position data set (PDS, PDS_0, ..., PDS_8) is determined at or after the receipt of the trigger signal (TS), wherein the position data set (PDS, PDS_0, ..., PDS_8) comprises at least one position data point (PDP) and one position data time point (PDZ), wherein, depending on the sensor data time point (SDZ) and the position data time point (PDZ), an assignment of position data points (PDP) to sensor data points (SDP) is determined, wherein a located sensor data record (vSDS_0,...,v_SDS_14) is generated, which comprises at least one sensor data point and the position data point assigned to this sensor data point, wherein for a sensor data record (SDS, SDS_0, ..., SDS_14) to which no position data record (PDS, PDS_0, ..., PDS_8) can be assigned, an estimated position data point is determined, wherein the located sensor data record (vSDS_0,..., vSDS_14) comprises at least the sensor data point(s) and the estimated position data point and / or wherein in a sequence of position data records (PDS, PDS_0, ..., PDS_8) a missing position record (PDS_6) is detected, whereby a replacement position record is created and inserted into the sequence in place of the missing position record (PDS_6).

The invention relates to a method and a system for managing agricultural processes. The method comprises creating a field management unit by defining georeferenced boundaries of said unit, creating a layout of the field management unit by defining georeferenced field management subunits within the field management unit, storing the field management unit, including its boundaries and its field management subunits, in a geospatial database provided on a server, attributing site characteristics to the field management unit, in particular to the field management subunits, attributing plant characteristics to the field management unit, in particular to the field management subunits, and exchanging data related to the field management unit with at least one sensor platform.and the performance of a task by at least one sensor platform with respect to data related to the field management unit.