97 results about "Satellite imagery" patented technology

The invention relates to the technical field of remote sensing data processing and glacier area calculation, in particular to an unmanned aerial vehicle and satellite remote sensing data fused glacier area calculation method, which comprises the following steps of: cooperatively acquiring a satellite multispectral image and unmanned aerial vehicle high-resolution optical and LiDAR data, performing time synchronization, high-precision space registration and data enhancement processing, and calculating the glacier area through the unmanned aerial vehicle and satellite remote sensing data fusion. A satellite image glacier macroscopic feature and an initial mask are extracted by using a convolutional neural network and an NDSI / NDWI algorithm, and unmanned aerial vehicle image microscopic texture, edge and topographic features are acquired through a local binary pattern, edge detection and LiDAR point cloud; based on pyramid layering and a conditional random field, adopting a variance weighting algorithm to realize multi-scale feature level fusion; and after segmentation through an Otsu algorithm, calculating the area through a pixel counting method and introducing gradient correction, and evaluating the reliability through three types of precision. The method breaks through the limitation of a single data source, fuses macroscopic and microscopic features, improves the boundary positioning precision and calculation efficiency, and is suitable for glacier dynamic monitoring in a complex environment.

The invention discloses a lake terrain prediction method based on machine learning fused multi-source satellite data, and relates to the technical field of machine learning, and the method comprises the steps: extracting the optical features of a target lake from an optical satellite image; extracting water features of each water body pixel in the target lake from the SWOT satellite data; performing space-time alignment and fusion on the optical features and the water features to obtain a fusion feature vector; the actually measured water depth point of the real water depth is matched to the geographic position of the corresponding pixel, and then the real water depth and the fusion feature vector of the corresponding pixel form a training sample; selecting a machine learning model and initializing the structure or parameters of the machine learning model; training the initialized machine learning model by using the training sample; utilizing the trained machine learning model to predict the water depth value of the corresponding pixel according to the fusion characteristic vector of each water body pixel; and determining the terrain of the target lake according to each water depth value. According to the method, the terrain of a large-scale lake can be efficiently predicted at low cost.

The invention discloses a water quality monitoring method and system based on artificial intelligence. The method comprises the following steps: acquiring a comprehensive data set composed of sensor data, satellite images and meteorological parameters; according to the water flow velocity and pollution concentration gradient in the comprehensive data set, adopting a dynamic sampling algorithm to adjust the sampling frequency and position, and outputting adjustment data; performing space-time interpolation processing on the adjusted data to obtain a preprocessed data set with high-density space-time coverage; key features of sensor values, image textures and meteorological parameters are extracted from the preprocessed data set by adopting a principal component analysis method, a weighted feature matrix is constructed, and a fusion feature set is obtained; and judging whether the dimension of the fusion feature set exceeds a preset threshold value, if the dimension of the fusion feature set exceeds the preset threshold value, performing dimension reduction and classification on the fusion features by adopting a random forest algorithm, and optimizing model parameters through cross validation to obtain a pollution concentration prediction result. Effective technical support is provided for water environment protection, and important ecological and social benefits are achieved.

The invention provides a construction method and system of a settlement landscape evolution database based on multivariate spatial-temporal characteristics. According to the method, historical literatures, aerial photography, satellite images and landscape data are integrated to form a multivariate data set; constructing a dynamically updated database by using MySQL, and realizing data source associated storage by using a relational model; establishing a settlement landscape state machine model, and extracting spatio-temporal data to identify typical states and conversion conditions; constructing a state transition matrix, and analyzing the influence of social economic and environmental changes on evolution; the database is updated through field investigation, and a visual tool is developed to display the evolution process. According to the method, full-process dynamic analysis of settlement landscape evolution is realized, and efficient and accurate support is provided for decision making.

A satellite for software-defined satellite imagery includes an optical assembly. The satellite further includes an attitude control system. The attitude control system is configured to control an orientation of the satellite such that the optical assembly slews across a ground track of the satellite. The satellite further includes a set of image sensors configured to capture an image using light directed to the set of image sensors at least in part by the optical assembly.

A solar radiation forecasting method based on satellite data and a multi-temporal-spatial-scale super-large model comprises the following steps: acquiring satellite remote sensing data, and cutting a satellite image into a prediction area size; constructing a daily variation feature set and a seasonal variation feature set, recombining the data into a four-dimensional tensor, and dividing the four-dimensional tensor into a training set, a verification set and a test set; performing linear mapping on the daily period and monthly period characteristics through a learnable parameter matrix to generate comprehensive time embedding; a layered parameter control strategy is adopted, and model parameters and unfreezing parameters are frozen; generating a low-resolution preliminary prediction result through a regression convolution layer; performing up-sampling by using a residual super-resolution module, reconstructing a high-resolution image in combination with jump connection and residual scaling, outputting single-channel solar radiation intensity data, and de-standardizing the data to a physical quantity line; and configuring an optimizer. The method can realize regional-level high-precision solar radiation prediction in 0-4 hours in the future, and solves the problems of spatio-temporal dependency splitting, poor adaptability to few samples and insufficient spatial resolution in solar radiation prediction.

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.

The invention discloses a lake water volume monitoring method based on a satellite image and a CatBoost-XGBoost model, and relates to the technical field of remote sensing application and hydrological monitoring, and the method comprises the steps: S1, obtaining and processing multi-source data: obtaining a Sentinel-2A satellite multispectral image, carrying out atmospheric correction, synchronously collecting actually measured water depth data, carrying out space-time matching, and constructing a training data set; s2, a CatBoost-XGBoost combination model is constructed and optimized, the CatBoost-XGBoost combination model is constructed, the output of the CatBoost model and the output of the XGBoost model are fused through weight distribution, and water depth inversion is achieved; s3, water depth inversion and precision verification: outputting pixel-level water depth based on the combined model, and verifying the inversion precision by adopting multiple indexes; and S4, water body range extraction and water volume calculation: extracting a water body boundary based on a water body index, and calculating the water volume of the water body through spatial integration in combination with the inversion water depth. By adopting the method of the steps, a machine learning method is introduced into water volume calculation, the lake water depth is inverted through the machine learning method, and the water volume is further calculated by utilizing double integration of the lake area and the water depth.

The invention provides a shadow perception satellite image nerve radiation field surface three-dimensional reconstruction method, which comprises the steps of optimizing an RPC model based on a multi-temporal satellite image, generating a sparse point cloud, and constructing a unified coordinate system through outer bounding sphere normalization; three-dimensional points are sampled in the pixel line-of-sight direction, and a training set is constructed; the SDF network predicts that spatial points have symbolic distance values, the color network integrates SDF features and sight line direction output colors, and the shadow color network combines the sun direction to generate shadow vectors; the light color is predicted through volume rendering integration, the shadow probability is calculated based on secondary ray casting in the sun direction, and a loss function is constructed in combination with an irradiance error to jointly optimize network parameters; and generating a three-dimensional grid by using sampling points of the trained SDF network in a unified coordinate system and adopting a Marching Cubes algorithm. According to the invention, the convergence stability of the neural radiation field model on satellite data and the success rate and precision of scene three-dimensional surface reconstruction can be improved.

The invention discloses an unmanned aerial vehicle cross-view-angle geographic positioning method based on a state space model, and relates to the technical field of geographic positioning methods, and the method is characterized in that the method comprises the following steps: S1, constructing a state space feature extraction module, and extracting features irrelevant to view angles from an unmanned aerial vehicle and a satellite image; s2, constructing a state space feature cross fusion module to obtain fused cross-view features; s3, building an unmanned aerial vehicle positioning network, and regressing the geographic coordinates of the unmanned aerial vehicle through a stacking feature extraction and feature cross fusion module; and S4, expanding satellite and unmanned aerial vehicle data, and updating unmanned aerial vehicle positioning network parameters by minimizing joint loss. The technical problem to be solved by the invention is to provide the unmanned aerial vehicle cross-view geographic positioning method based on the state space model, global features in an unmanned aerial vehicle and a satellite image are extracted through the state space model, cross-view features are fused by using a local attention mechanism, and geographic coordinates of the unmanned aerial vehicle are regressed on the satellite image.

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.

A device for providing a spatial information convergence solution comprising: a data collection module configured to receive a plurality of spatial information data and user requirement data; a selection module configured to select needed data required for generating the requirement data from among the plurality of spatial information data; a fusion module configured to generate a spatial information convergence solution by fusing the needed data; and an output module configured to provide the spatial information convergence solution to a user terminal for the user,wherein the plurality of spatial information data include at least one of CCTV data, drone data, GPS data, satellite imagery data, and aerial data.

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 invention discloses a fishery sea area monitoring system based on satellite remote sensing, which belongs to the field of fishery monitoring technology and specifically comprises: a remote sensing data acquisition module that acquires and pre-processes abnormal data on sea surface temperature, chlorophyll concentration and sea level height; a buoy data acquisition module that synchronously acquires temperature and salinity profile data of an Argo buoy array, and whose deployment density is dynamically adjusted according to historical fish catch and ocean front strength; a spatiotemporal alignment module that divides a time window by orbital period, corrects spatial offset in combination with buoy drift trajectory, sets a time buffer interval based on geostrophic velocity and satellite image resolution, and generates a spatiotemporally unified three-dimensional ocean environment data set; a fishery suitability calculation module that constructs a biophysical coupling model, associates surface chlorophyll with subsurface temperature and salinity structure, and calculates the suitability index of each water layer; a fishing ground division module that intelligently demarcates core areas and edge areas based on the three-dimensional distribution characteristics of the index; the present invention realizes accurate identification of fishing grounds based on multi-source data fusion.

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 present invention discloses a high-precision cross-view geolocation method for drone-remote sensing images. By constructing a cross-view geolocation model for drone-remote sensing images enhanced by feature space grouping, multi-scale semantic features of drones and remote sensing images are extracted based on ConvNeXt to obtain global features and local features. The global features and local features spatially enhanced by the feature space grouping enhancement module are subjected to feature fusion and quality regression. The model is trained using a hybrid function measured by triplet loss and cross-entropy loss, and drone images and satellite images are sent to the trained model for matching to achieve cross-view geolocation of drone-remote sensing. The present invention utilizes a spatial grouping enhancement module to enrich the spatial information of features, and utilizes a hybrid function measured by triplet loss and cross-entropy loss to improve the effective information utilization and feature matching effectiveness between non-aligned image pairs, thereby improving the accuracy of cross-view matching.

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 present invention provides a method for estimating the soil organic carbon content of wetlands with different restoration years, which relates to the field of wetland carbon sink accounting technology. The method includes: setting up multiple sampling points within the target study area and collecting soil samples; preprocessing satellite image data and determining a first input feature, and determining a second input feature based on the geographic coordinates of the sampling points, and inputting the above features into a regression prediction model to obtain a calculation result; generating a soil organic matter content data distribution based on the calculation result; using space instead of time, dividing sample areas with different restoration years based on land use history or restoration measures records, and within each sample area, statistically calculating the soil organic matter content and total amount based on the soil organic matter content data distribution to determine the soil organic carbon content of different restoration years. The present invention utilizes remote sensing technology and machine learning algorithms to achieve rapid and non-destructive estimation of wetland soil organic carbon content, reducing the workload and cost of field sampling.