61 results about "Satellite imagery" patented technology

A system for real-time certification and lifecycle automation of environmental credits. The system comprises: (i) a physical data collection layer equipped with on-land sensors, aerial LiDAR, satellite imagery, environmental DNA (eDNA) sampling, and community-contributed field data; (ii) an Ecological Oracle Engine, which uses an AI protocol to validate, classify, and interpret multi-source ecological data, supported by an adaptive reference library; and (iii) a smart contract protocol deployed on a blockchain network, configured to autonomously convert verified ecological data into financial instruments by issuing, pricing, transferring, freezing, and retiring tokenized environmental credits. The system performs continuous verification of each credit and includes freezing functionality based on ecological performance. It supports multiple credit types, including but not limited to carbon, biodiversity, and water credits. The system may be integrated with external modules for integrates with a Digital Twin of Nature and Web3 ecosystems, including decentralized marketplaces, ReFi platforms, and DAO mechanisms.

A global intelligence platform integrating satellite imagery, media analysis, and AI-driven insights. The system comprises a data integration module for processing real-time data streams, an AI analytics engine with machine learning models for each data type, a data fusion module for correlating insights, and a user interface. The AI engine includes models for analyzing satellite imagery, translating, and summarizing news, and processing social media sentiment. The data fusion module spatiotemporally aligns the heterogeneous data to identify relationships and generate a unified knowledge representation. The user interface enables querying, filtering, and customizing intelligence reports. By leveraging advanced AI techniques and diverse data sources, the invention provides comprehensive, real-time intelligence for decision-makers across various sectors, empowering informed responses to global events, trends, and public sentiment.

Boundary Verification Systems are provided. A proposed boundary of a geographic area is read. One or more boundary validation criteria is read. One or more attribute of the geographic area over time is determined from satellite imagery of the geographic area. The proposed boundary is validated against the one or more boundary validation criteria and the one or more attribute. A revised boundary is generated based on the proposed boundary and the one or more attribute.

This invention discloses an intelligent identification and positioning method for ocean internal waves based on multi-source remote sensing imagery, belonging to the field of marine remote sensing technology. The key technical solutions include the following steps: determining the geographical location and time range of the target sea area; collecting multi-temporal high-resolution remote sensing image data from synthetic aperture radar (SAR) and optical satellite imagery; labeling the internal wave features of the preprocessed multi-temporal high-resolution remote sensing image data to generate ground truth images; using a trained deep convolutional neural network model to perform pixel-level analysis on the input remote sensing images to extract ocean internal wave features and generate binarized images of the internal wave features; and converting the extracted binarized images of the internal wave features into corresponding latitude and longitude locations based on the geographical location information of the original remote sensing images. This achieves high-precision geographic positioning of internal waves, is suitable for marine research and high-precision environmental monitoring, and has broad application prospects.

A system for emergency planning and response to an incident related to the oil and gas industry comprising one or more databases that store data of oil spills and trajectories models, real-time locations of emergency vehicles, H2S gas sensors, LEL gas sensors, and corrosion pumps real-time readings, real-time weather stations, gas wells cross sections drawings and locations of daily drilling rigs. The system further comprises ERP, satellite imagery or aerial photography, and AVL; one or more servers; an ERP system; an electronic display device; and a GIS. The GIS comprises a processing system, and a GIS map displayed by the electronic display device. The GIS map is configured to display a common operating visual of the incident to incident personnel as it unfolds and to locate the emergency vehicles near to the incident.

The invention discloses an asset market dynamic valuation method and system based on deep learning, and the method achieves the dynamic updating of the asset value through adaptive multi-source heterogeneous data fusion and time sequence dependence modeling, and comprises the steps: S1, ubiquitous heterogeneous data integration; s2, performing dynamic noise cleaning and feature alignment; s3, constructing a multi-order coupling valuation framework; s4, carrying out time-varying feature topological mapping; s5, an adversarial dynamic training mechanism; s6, outputting and correcting a joint estimation state; and S7, an estimation robustness verification module. According to the method, millisecond-level multi-dimensional fusion of satellite images, logistics information and high-frequency transaction data is realized through a space-time tensor compiling technology of multi-source heterogeneous data, and the problem of synchronous lag of derivative signals in a traditional architecture is solved; through reconfigurable feature topology evolution, self-evolution of a factor interaction path when a market structure drastically changes is realized, and the problem of response delay of fixed model topology to industry rotation events is solved.

This invention relates to the field of power engineering identification technology, specifically to a method and system for reconstructing a 3D model of a high-voltage transmission line based on satellite imagery. The method includes: acquiring satellite imagery; identifying tower types; acquiring geographic information data and, in conjunction with the satellite imagery and tower types, performing tower height inversion to obtain the tower height; and reconstructing a 3D model of the high-voltage transmission line using a constructed parametric model library based on the satellite imagery, tower types, and tower height. This solution can efficiently and stably construct a 3D model of a high-voltage transmission line using satellite imagery, thus solving the problem of existing methods relying on expensive data sources and being difficult to apply on a large scale.

This invention discloses a method and system for fusing satellite imagery and UAV SLAM data, relating to the field of image processing technology. The method includes: constructing a first construction monitoring model and a second construction monitoring model; spatially registering and fusing the first and second construction monitoring models to construct a third construction monitoring model; constructing a site feature registration constraint space, and using this space to perform multimodal feature registration detection and correction on the third construction monitoring model to obtain a fourth construction monitoring model; performing multi-scale unfolding detection and anomaly tracing on the fourth construction monitoring model to obtain a model unfolding detection anomaly tracing map; and performing adaptive correction on the fourth construction monitoring model to obtain a fifth construction monitoring model. This invention solves the technical problem of low accuracy in the fusion of satellite imagery and UAV SLAM data in existing technologies, achieving the technical effect of improving the accuracy of satellite imagery and UAV SLAM data fusion.

This invention provides a geological disaster monitoring and early warning method and system based on BeiDou signals, belonging to the field of data monitoring technology. It includes: extracting spectral, texture, and shape features from monitored real-time images for analysis to identify abnormal areas and corresponding geological disaster types in the monitored region; analyzing surface deformation data of the identified abnormal areas based on BeiDou satellite data to obtain deformation data features of the abnormal areas, assessing the degree of deformation, and obtaining a deformation degree assessment value; determining the geological disaster index of the monitored region based on the deformation degree assessment value and the geological disaster type, and generating a corresponding early warning strategy. This invention achieves early automatic delineation of potential hazards by analyzing image features from BeiDou satellite imagery, and combines BeiDou deformation data to quantitatively analyze the activity status of disaster bodies, generating a tiered early warning strategy, thus improving the spatiotemporal coverage, accuracy, and timeliness of geological disaster monitoring and early warning.

This application provides a method and system for extracting water body contours from high-orbit satellite imagery, comprising the following steps: cutting the original image into a sliding window to generate an image patch sequence; inputting the image patch sequence into a heterogeneous dual-stream encoder to extract dual-branch heterogeneous features; performing deep interaction on the dual-branch heterogeneous features in the heterogeneous dual-stream encoder to obtain deep heterogeneous collaborative features and shallow heterogeneous collaborative features at multiple feature extraction stages; processing the deep heterogeneous collaborative features and the shallow heterogeneous collaborative features to obtain a probability prediction map; using a square-shaped weighted fusion strategy to reassemble the probability prediction maps of all image patches to obtain a full-image probability prediction map; and performing binary partitioning on the probability prediction map to obtain the final binary mask map. This invention solves the problems inherent in high-orbit SAR imagery, such as uneven texture within water bodies due to noise, difficulty in maintaining the topological integrity of small river networks, and gaps and misalignments left during reassembly and stitching.

The invention relates to the technical field of electronic maps, in particular to a global earth surface coverage mapping method based on a 2-meter-resolution domestic satellite image, and aims at solving the problem that an existing 30-meter and 10-meter-resolution global earth surface coverage product is low in accuracy. And a mapping strategy combining general and regional feature modeling with layered land surface coverage is adopted. According to the image prepared by the method, the land cover accuracy is high.

The invention discloses an uprush cloud top detection method and system based on a YOLOv8 algorithm, and belongs to the technical field of target recognition. The method comprises the following steps: constructing a multi-channel satellite data set and generating a three-channel RGB image through a pseudo-color synthesis technology; performing truth value labeling by combining radar vertical profile data to form a high-quality data set; a convolution attention module is embedded in the YOLOv8 model, training is carried out, and small target detection performance is optimized; and preprocessing a real-time satellite image, inputting the preprocessed real-time satellite image into the model, outputting a detection result and linking an early warning system. The method gets rid of dependence on manual setting of a fixed physical threshold value, is high in generalization capability and robustness, is completely automatic in detection process, can reduce the burden of a predictor, improves the severe convection weather monitoring and early warning efficiency, and is especially suitable for precise recognition of small-scale and untypical-form uprush clouds.

The present application relates to the field of space photogrammetry and water conservancy surveying and mapping technology, and particularly relates to a Gao Fen 7 satellite water conservancy stereoscopic mapping and three-dimensional modeling method and system, comprising: using a water conservancy facility topology characteristic quantitative evaluation model to screen the same-track stereo images, and performing same-track stereo image splicing and fusion based on river linear topology, reservoir area surface topology and canal system node topology; dividing terrain subareas and establishing a terrain slope and RPC parameter correlation correction model. The present application takes water conservancy facility topology characteristics as the core driving force, runs through the whole process of image preprocessing, space three solution, image control layout, point cloud processing and three-dimensional modeling, replaces the precision control role of traditional dense image control points through topology constraint, solves the problems of insufficient image control precision, high image control point layout cost and low large scene modeling efficiency of existing satellite stereoscopic mapping in complex water conservancy topography, and realizes efficient and high-precision application of Gao Fen 7 satellite images in water conservancy engineering surveying and mapping.

This invention discloses a method and apparatus for fine extraction of aquatic vegetation and landscape connectivity assessment in rivers and lakes based on high-resolution remote sensing, belonging to the field of environmental remote sensing monitoring and water ecology assessment technology. It includes: acquiring and preprocessing high-resolution satellite imagery to obtain a standardized remote sensing image dataset; extracting water body masks and aquatic vegetation coverage areas through hierarchical threshold segmentation; constructing a dual-threshold discrimination rule based on the normalized vegetation index and soil-regulated vegetation index to finely divide the distribution areas of floating and submerged plants; constructing a binary raster map of vegetation distribution based on the distribution areas; combining a preset connectivity distance threshold; calculating the landscape connectivity index of vegetation patches based on graph theory; determining the ratio of equivalent connected area to effective connected area; and completing a comprehensive assessment of the ecological effectiveness of the aquatic vegetation community. This invention is mainly used for remote sensing monitoring of aquatic vegetation in urban rivers and lakes and for assessing the effectiveness of water ecology restoration, effectively mitigating spectral interference from complex water body sediment backgrounds.

In an approach for efficient flood water analysis from spatio-temporal data fusion and statistics, a processor classifies regular waters by using cartographic data in a first location. A processor generates a water stream network including a watershed based on elevation data. A processor performs statistical analysis of spectral information from a multi-spectral satellite imagery over water bodies including the regular waters and flood waters. A processor correlates the spectral statistics of the multi-spectral satellite imagery to kinetic energy of the flood waters using machine learning techniques and physical modeling. A processor builds a learning model based on the correlation between the spectral statistics and the flood waters with the kinetic energy. A processor estimates kinetic energy of flood waters in a second location using the learning model. A processor evaluates a flooding risk for the second location based on the estimated flood waters kinetic energy.

This invention belongs to the field of UAV autonomous positioning and navigation technology, and relates to a UAV map construction method for environments where satellite imagery is unavailable. Specifically, it provides a method for constructing a UAV work area map based on aerial imagery, addressing issues such as image distortion and edge misalignment in existing area map construction methods. This invention orthogonally divides the UAV's aerial photography work area into a grid according to the same geographic coordinate system. The UAV performs a coverage flight mission within the work area according to a planned flight path, maintaining an interval of 50% of the field of view width between adjacent flight paths. During aerial photography, the UAV simultaneously acquires image and geographic positioning information (GPS information and yaw angle information of the center point) as image location points. Based on UAV sensor data and the UAV's flight trajectory and pose, keyframes of geographic location information are extracted from the aerial video, and then used to fill the corresponding grid content to obtain a global map of the work area.

The application provides a source-sink landscape-based soil heavy metal remediation method and system, and relates to the field of ecological environment; the method generates target region small watershed space data by analyzing digital elevation model through GIS; on the other hand, the source-sink landscape type of the target region is determined according to satellite images and remote sensing images, and the pollution source intensity is determined by combining rainfall runoff and soil environment sampling monitoring to establish a source-sink landscape geographic information database; soil heavy metal monitoring and spatial evaluation are carried out by selecting typical landscape types, and a self-regression model is established by spatial autocorrelation analysis of soil heavy metal spatial distribution information and source-sink landscape types, and the best scheme of landscape space optimization is proposed based on the model; based on the optimal scheme, the landscape space pattern most conducive to the soil heavy metal self-repairing process is obtained; finally, physical, chemical and biological treatments are further carried out for different landscape types, and the source-sink landscape-based soil heavy metal remediation application is completed.

The invention discloses a remote sensing image multi-granularity collaborative recognition method and system based on satellite edge calculation, and the method comprises the steps: obtaining a high-resolution remote sensing satellite image, carrying out the in-orbit coarse granularity screening through maximizing an efficiency score, and obtaining an interested target region; based on a distributed task unloading method, an interested target area is sent to an optimal intelligent satellite through a remote sensing satellite; and the optimal intelligent satellite performs fine-grained target recognition processing on the interested target area to obtain a ground target recognition result. According to the invention, decision making can be carried out under the condition of meeting performance and time delay constraints, and the precision of a ground target identification result is improved. The remote sensing image multi-granularity collaborative recognition method and system based on satellite edge calculation can be widely applied to the technical field of satellite earth observation.

Implementations are disclosed for training and / or applying a single machine learning model to generate mid-cycle inferences based on variable length time series inputs. In some implementations, a time series of satellite imagery samples that depict an agricultural area over all or part of a crop cycle may be obtained. During training, ground truth agricultural classifications may be obtained for geographic units of the agricultural area represented by individual pixels of the satellite imagery samples. During training, sample(s) of the time series may be masked to generate a partially masked satellite imagery samples, which in turn may be used to generate input embedding(s). The input embedding(s) may be applied across a machine learning model to generate output(s) representing in-season agricultural prediction(s). During training, the in-season agricultural prediction(s) may be compared with the ground truth agricultural classifications to train the machine learning model.

The present invention relates to a method and system for providing an interactive artificial intelligence response service. More specifically, the method for providing an interactive artificial intelligence response service according to the present invention may include the steps of: receiving a user query via a service page; identifying location information from the user query; searching for satellite imagery related to the user query from at least one database using the user query and the location information; performing analysis related to the user query using the satellite imagery; generating a prompt using at least one of the user query, the location information, the analysis results from the analysis, and the satellite imagery; processing the prompt as input to a large-scale language model (LLM); obtaining an answer to the user query from the large-scale language model; and providing the answer to the user query to the service page.

The invention belongs to the technical field of land resource monitoring, and particularly relates to a land utilization change monitoring method based on deep learning. The monitoring method comprises the following steps: S1, deep learning network training data set selection; S2, deep learning environment configuration; S3, deep learning network training; S4, image prediction; according to the method, a U-Net and DeepLabV3 + semantic segmentation model is selected, a Sentinel-2 satellite image is utilized, a deep learning technology is combined, a land utilization intelligent classification and dynamic monitoring framework is constructed, the land utilization change condition of a research area is monitored, driving factors are analyzed, and the dynamic classification and dynamic monitoring of land utilization are realized. Urban expansion and ecological element space-time evolution rules are revealed by finding out reasons for change of ground feature type proportions, and a scientific basis is provided for territorial space governance; according to the method, the technical advantages of deep learning in high-resolution remote sensing interpretation are verified, and a generalizable normal form is provided for middle city land utilization monitoring.

The invention provides a method and system for optimizing unmanned aerial vehicle aerial photography scene reconstruction based on a GAN model, and belongs to the technical field of scene reconstruction, and the method comprises the steps of feature vector acquisition, discriminator updating, generator updating, iteration and scene reconstruction. According to the method, satellite image data Isat, point cloud data Plidar, an unmanned aerial vehicle aerial image Iphoto and a multi-view rendering image Irend are subjected to multi-source data fusion, and the reduction capability of a 3D Gaussian sputtering model constructed by a 3DGS algorithm is improved; an optimization mechanism is formed through the GAN model, the 3D Gaussian sputtering model is continuously guided and optimized, the scene reconstruction capability of the 3D Gaussian sputtering model is improved, and thus the fidelity of an aerial scene of the unmanned aerial vehicle is improved.

The invention discloses a comprehensive prospecting prediction method for pegmatite type lithium ore, and relates to the technical field of pegmatite type lithium ore detection. The method comprises the following steps: delineating a large-scale geochemical exploration working area according to small-scale geochemical exploration anomalies; sampling 1: 50,000 water-based sediments; testing the water-based sediment sample, processing data and mapping; interpreting and evaluating geochemical exploration anomaly; geological survey of the prospecting target area; acquiring and processing hyperspectral remote sensing data; rock ground collection and feature analysis; extracting spodumene pegmatite from a hyperspectral remote sensing satellite image; predicting spodumene pegmatite vein in a regional range; and verifying the prediction effect. According to the comprehensive prospecting prediction method for the pegmatite type lithium ore, prospecting prediction is carried out by integrating multiple methods such as geology, geochemical exploration, remote sensing and hyperspectrum, the prediction precision and exploration efficiency of the pegmatite type lithium ore can be remarkably improved, and the increasing lithium requirement is met.

The present disclosure relates to an apparatus and a method for generating standardized 3D objects based on satellite imagery. An apparatus for generating standardized 3D objects based on satellite imagery according to one embodiment of the present disclosure may include a preprocessing unit configured to preprocess the satellite imagery by upscaling the satellite imagery using a generative adversarial network (GAN), and then resizing and dividing the upscaled satellite imagery, a digital surface model generation unit configured to generate a digital surface model for the preprocessed satellite imagery by applying a vision transformer to the preprocessed satellite imagery, an object boundary information generation unit configured to generate boundary information of individual objects included in the preprocessed satellite imagery using a convolutional neural network (CNN)

A method of modeling three-dimensional objects from two-dimensional imagery includes receiving, from an imaging device with a sensor, one or more digital images of an object and image metadata representing a focal length of the sensor, a pixel size of the sensor, and a range of each of the one or more digital images; adding, to the one or more digital images, digital markings representing perpendicular axes of the object; computing at least one sensor model of the imaging device based on the image metadata, the digital markings, and the perpendicular axes of the object; computing object-space coordinates of points corresponding to different features of the object based on the at least one sensor model; and generating a three-dimensional model of the object based on the object-space coordinates of the points.

This disclosure presents a system and method for aligning diverse map types and transforming coordinates between them. The method involves receiving a map, selecting an alignment map, and choosing an alignment method. The system then performs the alignment process, generating a transformation matrix that defines the spatial relationship between the maps. The alignment quality is evaluated, and if satisfactory, the transformation matrix is integrated into a transformation tree data structure. This tree represents spatial relationships between multiple maps, enabling complex coordinate transformations. The method supports various map types, including floor plans, satellite imagery, and geographic data, allowing for their integration into a unified spatial framework. By addressing the challenges of aligning maps from different sources and formats, this system facilitates accurate positioning, navigation, and spatial analysis across heterogeneous map environments.

Implementations are described herein for using one or more transformer networks to generate inferred image data based on processing image data capturing a particular geographic area during a particular time period, including first image data captured in a first spectral band and at a first spatial (and / or temporal) resolution and second image data captured in a second spectral band and at a second spatial (and / or temporal) resolution. The inferred image data can include second spectral information at the first spatial (and / or temporal) resolution, or vice versa. Thus, the spatial and / or temporal resolution of image data of a certain spectral band can be improved, allowing for more effective usage of satellite imagery in agricultural settings.

This invention discloses a method, apparatus, device, and medium for dynamic updating of 3D geographic scenes based on spatiotemporal interpolation. The method includes acquiring multi-source heterogeneous data, including satellite imagery, UAV remote sensing imagery, and ground sensor data; calculating the weight of each data source based on its spatial and temporal resolution; performing spatial and temporal interpolation calculations on spatial points of the 3D scene, combining the weights of the multi-source data; performing nonlinear compensation on the spatial and temporal interpolations to obtain the updated spatial point values ​​of the compensated 3D scene; comparing the interpolation results at different time points, detecting local changes, and updating the changed areas of the 3D scene. This invention ensures fusion quality through reasonable weighting by comprehensively configuring weights based on spatial resolution, temporal resolution, data quality, and similarity to reference data, making the fusion of basic 3D scene data more closely resemble real geographic features.

This invention discloses a system for segmenting minimal fire points in satellite remote sensing imagery, relating to the fields of image processing and remote sensing technology. The system includes an encoder, a fire-oriented directional context modulation module, a decoder, and a multi-scale Mamba attention module. The encoder suppresses background redundancy responses and enhances minimal fire point features through a cross-layer gating mechanism; the fire-oriented directional context modulation module enhances the response of directional expansion features of fire points through multi-directional context modeling; the decoder fuses encoder features through skip connections; and the multi-scale Mamba attention module utilizes a state-space model to model long-range spatial dependencies, enhancing the globally consistent representation of multiple minimal fire points. This invention solves the problems of extremely small fire point target scale, complex background, difficulty in capturing directional diffusion, and poor global consistency in satellite imagery, achieving high-precision and robust segmentation of minimal fire points and improving the accuracy and efficiency of fire monitoring.

The invention relates to the technical field of remote sensing image processing, in particular to a satellite image RPC parameter elevation optimization method fused with a gravity field model. Comprising the following steps: S1, aerial triangulation encryption and key point acquisition; s2, coordinate transformation; s3, primarily calculating geodetic coordinates by the model; s4, carrying out elevation conversion on the EGM2008 model; s5, constructing a block adjustment model considering the gravity field; s6, iterative solution and output are carried out; according to the method, the earth height (H) directly output by the RPC model is accurately converted into the normal height (h) by introducing the EGM2008 earth gravitational field model, so that the elevation system error caused by the fluctuation of the geoid in a complex region with abnormal and remarkable gravity is effectively eliminated. The normal height is used as a hard constraint to be fused into block adjustment, and the RPC parameters are optimized in a targeted manner, so that the final elevation positioning precision is improved by more than 30% compared with that of a traditional method, and the elevation precision requirement of topographic map surveying and mapping with the measuring scale of 1: 10000 and even 1: 2000 can be met.