1890 results about "Satellite image" patented technology

Disclosed in the present invention is a method for repairing an RPC of a remote-sensing image. The method comprises: S1, acquiring information such as an image to be repaired and a reference image; S2, performing feature extraction and matching on the image to be repaired and the reference image by using an SIFT algorithm, so as to obtain a matching point pair and corresponding first pixel coordinates; S3, acquiring longitude and latitude coordinates; S4, acquiring geodetic coordinates and a control point; S5, constructing an RFM model, performing RPC positive calculation on an original RPC parameter of the image to be repaired and the geodetic coordinates, so as to obtain corresponding second pixel coordinates of the geodetic coordinates on a remote-sensing image, and separately calculating a mean value of error distances between corresponding pixel coordinates; S6, introducing a constraint condition by using an L1 regularization optimization method, so as to obtain an optimized RFM model; and S7, performing determination according to the mean value of the error distances and a preset threshold value, and selecting different repair policies to recalculate or correct the RPC parameter. In the present invention, an RFM model is optimized by using an L1 regularization method, and different repair policies are selected to perform correction or estimation on an RPC parameter, thereby improving the geometric positioning precision of a satellite image.

The invention relates to the technical field of intelligent decision making, in particular to an accurate fertilization intelligent decision making method and system based on multi-source remote sensing, and the method comprises the steps: obtaining a multi-source remote sensing image which comprises an unmanned aerial vehicle image and a stationary satellite image; acquiring soil nutrient data and fertility indexes of the sample field parcels; acquiring meteorological data and soil data; carrying out image preprocessing and feature extraction; constructing a crop identification and classification model; carrying out crop type identification and block division on the multi-source remote sensing image; extracting crop growth features based on the multi-source remote sensing image and the result of the crop identification and classification model; based on the crop growth characteristics and the soil nutrient data, analyzing the relationship between the crop growth stage and the nutrient demand; constructing a nutrient utilization efficiency maximization model; based on a nutrient utilization efficiency maximization model, generating a precise fertilization decision scheme; and outputting a precise fertilization decision scheme, and carrying out precise analysis and intelligent decision making on the growth state and nutrient demand of farmland crops.

The present invention provides a method for impact analysis of port construction on a coastal ecotone based on remote sensing data, which belongs to the technical field of remote sensing application. The method quantifies landscape data and satellite image data, and then converts the data into intuitive images to observe the spatio-temporal change of the coastal ecotone after port construction. The method includes: step 1: obtaining remote sensing image data of a research region; step 2: preprocessing the remote sensing image data to obtain processed images; step 3: establishing an evaluation system of human disturbance indexes; and step 4: generating a spatio-temporal distribution map of regional disturbance indexes. The present invention provides a method capable of quantifying the impact range and the impact degree of the port construction factor on the coastal ecotone, provides an idea for reducing the impact of port construction on a surrounding environment.

The invention provides a crop monitoring method fusing a laser radar and a multispectral image, and the method comprises the steps: carrying out the extraction of laser radar point cloud data and multispectral satellite image data of a target crop, carrying out the data preprocessing, unifying the data in a same coordinate system, carrying out the data fusion, extracting the structure parameters of the canopy of the target crop, and carrying out the data fusion. Estimating the physiological parameters of the canopy of the target crop, comprehensively analyzing the structural parameters and the physiological parameters, constructing a quantitative crop growth evaluation index, simulating the ideal growth state of the target crop through a crop growth model, judging whether the growth of the target crop is abnormal or not, and generating an abnormality diagnosis result. And finally, obtaining growth early warning information and management decision suggestions of the target crops. According to the method, three-dimensional structure and spectral feature collaborative characterization is carried out on the crops, quantitative comprehensive evaluation indexes are constructed, the crop growth vigor is dynamically evaluated, and the precision, timeliness and quantifiable level of crop growth vigor monitoring can be remarkably improved.

The invention discloses a battlefield target behavior prediction method capable of being guided by micro-physical representation and a thinking chain, and the method comprises the steps: obtaining satellite images, radar / communication detection and open source text multi-source time sequence data, completing the entity recognition, relation extraction and event detection, and constructing a dynamic space-time knowledge graph; the maneuverability, sensor detection, weapon range and terrain accessibility mechanism are micronized to serve as a physical consistency constraint embedded prediction model; generating an intention-action-result causal priori chain and parameterizing the causal priori chain into a computable structure; performing multi-branch long-time-sequence situation deduction, and outputting a future target behavior track and a scene probability; and evaluating and explaining by integrating the causal confidence coefficient, the physical consistency and the data goodness of fit, and giving a key event probability and situation evolution report. According to the method, unified modeling of semantic causal and physical constraints is realized, and the method has explainable, verifiable and robust prediction capabilities, and is suitable for target behavior prediction and command information system decision support in a complex environment.

The invention relates to a method for improving satellite data inversion soil salinity by using hyperspectral data of an unmanned aerial vehicle. The method comprises the following steps: collecting a soil sample in a test area, and calculating soil salt content; acquiring and preprocessing satellite remote sensing image data of a test area; storing data, outputting a single-band satellite image, and calculating a spectral index; obtaining and preprocessing hyperspectral image data of the unmanned aerial vehicle in the test area; reconstructing and upscaling unmanned aerial vehicle image data pixels to a spatial resolution of 10m; reconstructing the narrow-band spectral data of the unmanned aerial vehicle into broadband spectral data consistent with a satellite image, storing and outputting a single band, and calculating a spectral index; screening a soil salt content characteristic variable, and constructing a soil salt content inversion model by taking the screened characteristic variable as an independent variable and the actually measured soil salt content as a dependent variable; and fitting a function relationship between the upscaling unmanned aerial vehicle soil salt content inversion model and the satellite inversion model by adopting a numerical regression function method to realize correction of the satellite inversion model.

The invention relates to the field of construction planning visualization, in particular to a construction planning visualization method and system for a refuse transfer station. The method comprises the following steps: acquiring a satellite positioning map and a refuse transfer station design drawing of a to-be-developed area; image detail reconstruction is carried out on the satellite positioning map, road space distribution fitting is carried out, and a regional road distribution network is constructed; performing surrounding building positioning calculation on the detail optimization reconstruction satellite image based on a regional road distribution network, performing an optimal site selection decision, and extracting an optimal address region image; carrying out transfer station key feature point identification on the refuse transfer station design drawing, then carrying out three-dimensional point cloud modeling, and constructing a transfer station three-dimensional structure model; and according to the optimal address area image, carrying out corresponding foundation accurate matching on the transfer station three-dimensional structure model, carrying out spatial positioning mapping, and constructing a transfer station address twinborn model. According to the method, the operation efficiency of the transfer station is improved, and the rationality of building planning is enhanced.

The invention relates to an unmanned aerial vehicle multi-modal visual positioning method and system in a GNSS denial environment, and the method comprises the steps: constructing a visual inertial mileage calculation method based on deep learning feature extraction and matching, calculating the relative positioning of an unmanned aerial vehicle, and outputting to obtain high-frequency 6-DoF pose information; performing scene matching of infrared RGB image multi-modal fusion based on the Officient-LoFTR, and obtaining absolute positioning in a world system by solving a relative pose between a current key frame picture and a matched satellite image; a factor graph optimization mode is adopted to carry out self-adaptive weight information fusion according to the confidence coefficient of absolute positioning and relative positioning under a world system, and 6-DoF pose information with high frequency and higher precision is output; based on high-frequency and higher-precision 6-DoF pose information, after the unmanned aerial vehicle is recovered, all aerial images and satellite images are fused and updated. The system comprises a calculation module and a fusion updating module. According to the invention, more accurate and high-frequency pose output can be provided.

The invention discloses a building health state detection system, and relates to the technical field of photogrammetry and image processing, the system firstly obtains four-dimensional space-time coordinate data of a plurality of monitoring points on a target building based on satellite image analysis, constructs a four-dimensional space-time network model containing spatial topology and time sequence evolution information according to the four-dimensional space-time coordinate data, and then obtains a four-dimensional space-time network model containing space topology and time sequence evolution information; the system maps network model nodes to a predefined standard reference network associated with a local gravity direction, then, the system analyzes dynamic relevance in the four-dimensional network model by adopting a graph self-attention network, and meanwhile, calculates relative relation changes of monitoring points relative to the predefined standard reference network through geometric analysis; the deformation interpretation module distinguishes and determines internal relative deformation, overall displacement and absolute posture change (including settlement and inclination) of the building according to the two types of analysis results and by referring to surrounding environment information, and the state evaluation module integrates the information to generate a health state report containing risk levels.

The invention discloses a semi-supervised multi-temporal satellite image time-varying information extraction method, and belongs to the technical field of remote sensing image processing. The semantic change detection performance of the model and the detection precision of complex shape change ground objects are improved. The method comprises the following steps: constructing a semantic change detection model, carrying out full-supervised training on the semantic change detection model by using a binary change detection supervised loss function and a semantic segmentation supervised loss function by using a small amount of labeled dual-temporal remote sensing images to obtain an initial model, and obtaining a semantic change detection prediction result of each pair of images; using a pseudo label optimization strategy to optimize the semantic change detection prediction result of each pair of images; combining the obtained pseudo label data with high confidence and a small amount of labeled dual-temporal remote sensing images into a new training set, using the new training set to perform semi-supervised training on the initial model in a semantic change detection model, and using a consistency regularization combination loss function to perform supervised training to obtain a new model; and until a preset number of iterations is reached.

The invention provides a multi-modal multi-scale feature fusion-based power load prediction method and system, and relates to the technical field of power load prediction. According to the method, a cross-modal attention mechanism is adopted to fuse multi-scale load time sequence features and satellite image features; decoding the fusion features through a multi-step prediction decoder to obtain a multi-step power load prediction result; the acquisition mode of the satellite image features comprises the following steps: performing feature extraction on a satellite image based on a GRVT model to obtain the satellite image features; the GRVT model includes a number of stacked gated residual encoder network elements. According to the method, satellite image feature extraction is carried out based on the GRVT model, the global information processing advantage of Transform is utilized, the single-layer information retaining capability of a residual connection mechanism is also utilized, the two are combined to efficiently extract satellite image features, the satellite image features are fused during power load prediction, and the situation that the data type is single, and the prediction efficiency is high is avoided. And the precision of power load prediction is improved.

The invention relates to the technical field of space governance, and discloses a multi-agent space collaborative governance method which comprises the following steps: processing multi-source heterogeneous data such as satellite images and sensor readings, establishing cross-type semantic association through a geographic space data embedding technology, generating a unified structured text after optimizing the satellite images through vLLM, and synchronizing the unified structured text to a central database; an agent role portrait is dynamically generated by the central server large language model based on a preset Prompt template, and generation does not depend on a fixed rule; then, selecting a target node in the edge-center architecture, disassembling a total task into sub-tasks, establishing semantic mapping, calculating a matching probability, and performing optimal distribution by a reward borrowing function; generating a governance scheme in a perception layer-decision layer-execution layer framework, and outputting a coded operation instruction; based on an execution feedback updating strategy, a multi-level mechanism is set, roles are automatically redistributed, and the governance continuity is guaranteed. According to the invention, the overall efficiency and reliability of space governance can be improved in the face of dynamic scenes or emergency situations.

The invention discloses an intelligent geographic surveying and mapping data processing analysis method and system, and the method comprises the steps: obtaining satellite image data, LiDAR point cloud data and unmanned aerial vehicle aerial photography data, obtaining multi-source heterogeneous surveying and mapping data, building a DC-CNN dual-channel convolutional neural network model, adding a cross-channel feature fusion module on the basis of a model spectrum channel and a geometric channel, and carrying out the multi-source heterogeneous surveying and mapping data fusion. And utilizing a convolutional layer in a depth separable convolution substitution model, inputting the fused multi-source heterogeneous surveying and mapping data into the target DC-CNN network model for identification, and performing association modeling on the surveying and mapping feature data, topographic parameters and environmental data to obtain a multi-scale geographic knowledge map. The extraction efficiency of the model on key features is improved, so that the model has better real-time performance and expandability while keeping high precision.

The invention discloses a Yangtze estuary terrain evolution analysis and dynamic simulation method based on multi-source data fusion, and relates to the technical field of terrain evolution analysis, and the method comprises the following steps: setting a preliminary expected monitoring period by a satellite remote sensing platform according to the monitoring demands of a mud flat region, so as to regularly obtain the image data of the mud flat region, the real-time monitoring of the mud flat area is realized; and after a new satellite image is acquired each time, registering the new satellite image with the satellite image acquired in the previous time. According to the method, the mud flat area is divided into a rapid evolution type and a slow evolution type, and the initial period is maintained for the stable change area, so that resources are saved; the period of a violent change area is shortened, the data acquisition frequency and the response speed are improved, the problem of traditional monitoring lag is effectively solved, rapid terrain change can be captured in time, the monitoring efficiency is optimized, the ecological damage risk is reduced, and accurate decision support is provided for mud flat protection and management.

The invention provides an optical imaging satellite task scheduling method based on a hybrid heuristic memetic evolution algorithm, solves the problem of how to accurately and efficiently obtain satellite images, belongs to the engineering field, and is based on a constructed satellite data set, a task data set, a satellite dynamics model, a task decision model and constraint conditions, takes maximizing the satellite task completion benefit value as an objective function, and constructs a satellite scheduling decision model; constructs a hybrid heuristic memetic evolution algorithm: obtains an initial observation path solution set through clustering processing and a constraint check model; inputs the initial solution into the satellite scheduling decision model to obtain a total benefit value of the initial solution; adopts a neighborhood search algorithm to obtain a feasible solution and a total benefit value of the initial solution, and obtains the quality of the feasible solution from the total benefit value difference; combines a simulated annealing algorithm and an ant colony evolution algorithm to search, and obtains an optimal solution for optical imaging satellite task scheduling after a preset stop condition is met; the invention realizes accurate and efficient acquisition of satellite images.

The invention discloses a method for evaluating the influence of black land ecological barrier pattern evolution on agricultural climate and yield, and relates to the technical field of agricultural ecological environment monitoring and remote sensing model coupling. Comprising the following steps: S1, acquiring a high-resolution satellite image, microwave remote sensing earth surface humidity data, optical remote sensing vegetation coverage data and thermal infrared remote sensing earth surface temperature data of a target area; according to the method for evaluating the influence of the black land ecological barrier pattern evolution on the agricultural climate and the yield, the problem of lack of ecological-climate-agricultural system collaborative modeling in the traditional technology is solved. A high-resolution satellite image is used for analyzing a network topology structure of the protection forest, microwave and thermal infrared remote sensing are fused to invert'water, soil and gas generation 'multi-element parameters, and dynamic quantitative evaluation of ecological barrier spatial heterogeneity evolution on local climate and crop growth is realized in combination with a dynamic weight distribution and two-way nesting technology.

The invention discloses a lightweight-based satellite landslide image intelligent detection method, device and equipment and a medium, and relates to the technical field of disaster detection, and the method comprises the steps: obtaining a whole-scene optical satellite image containing a landslide and a non-landslide region and landform auxiliary data; a dynamic segmentation strategy is adopted to carry out differential segmentation and standardized preprocessing on an image based on topographic data, and a standardized image is obtained. A target landslide image is screened through a double-layer machine learning model, the target image is input into an improved lightweight convolutional neural network for processing, an initial detection result is obtained, finally edge optimization and coordinate calibration are performed on the result, and an accurate landslide area detection result is output. The identification precision of the landslide image is effectively improved through dynamic segmentation and double-layer screening, the improved lightweight convolutional neural network realizes efficient detection in a low-resource environment, and the accuracy of a landslide detection result is further improved.

The invention provides a desertification monitoring grading and vegetation extraction method and system, and relates to the field of desertification remote sensing monitoring and ecological assessment, and the method comprises the steps: selecting a multi-temporal satellite image and an unmanned aerial vehicle image which cover a full research region according to a preset condition, and carrying out the data preprocessing of the multi-temporal satellite image and the unmanned aerial vehicle image; the method comprises the following steps: constructing a desertification difference index by fitting a feature space of a vegetation index and a surface albedo by using a multi-temporal satellite image, grading the desertification degree of a research area to obtain a grading result, and locking a key monitoring area in the research area according to the grading result; a vegetation sample image of a key monitoring area is obtained from an unmanned aerial vehicle image, HSL color space conversion and hue optimization processing are carried out on the vegetation sample image, and a normalized vegetation index based on HSL is constructed, so that vegetation information of the key monitoring area is finely extracted, a key desertification disaster area is effectively positioned, and the accuracy of the desertification disaster area is improved. And vegetation information in the region is finely extracted.

The invention provides a high-precision positioning method and system based on multi-source information fusion. The high-precision positioning method comprises the following steps: acquiring the omm positioning information of the pose of an unmanned aerial vehicle in real time based on a visual inertial navigation unit; a global feature extraction algorithm is combined with a similarity algorithm, and a target satellite image matched with the airborne image of the unmanned aerial vehicle is selected from a satellite image library; using a local feature matching algorithm to combine with the geographic coordinates of the target satellite image to obtain GPS predicted positioning coordinates; fusing the odom positioning information and the GPS predicted positioning coordinates by using a global fusion technology to obtain preliminarily fused odom positioning information; according to the method, secondary fusion is carried out on the basis of GPS positioning data obtained by a real GPS sensor in combination with the preliminarily fused odom positioning information to obtain final odom positioning information, and through a double fusion strategy, the advantages of a multi-source positioning technology are fully integrated, and the positioning accuracy is improved.

The invention discloses a roof photovoltaic identification and potential evaluation method based on improved TransUNet semantic segmentation. The method comprises the following steps: obtaining a building roof satellite image data set; cleaning and classifying the images in the data set according to whether roof features exist or not, and dividing out images with roofs; according to the roof image, roof features are extracted through the constructed and trained TransUNet semantic segmentation model, and a building roof area is identified and area division is carried out; and matching the processed image with the subject name, and positioning the subject to which the building belongs. According to the method, an innovative solution of a remote sensing technology and a machine learning method is integrated, the generalization ability of roof recognition semantic segmentation can be effectively improved, the roofs are accurately recognized and divided, and the method aims at achieving rapid and accurate evaluation of large-scale urban building roofs.

The invention discloses a phenological self-adaptive corn automatic mapping method, which comprises the following steps of: 1, screening to obtain Sentinel-2 time sequence satellite image data of a target area, preprocessing, and synthesizing a time sequence clear sky image data set with one scene in a half month; 2, filtering the time sequence clear sky image data set to obtain a denoised and smoothed time sequence satellite image data set; step 3, based on peak and valley values of the NDVI index of the filtered image, adaptively acquiring a corn identification key phenological period of the target area; 4, analyzing typical spectrum differences of the corn and other main background ground objects, providing a corrected red edge normalized vegetation index, and forming a typical index data set of the corn identification key phenological period with an NDVI index and an LSWI index; and step 5, automatically realizing rapid mapping of the corn in the target area by utilizing the division rule of the typical index data of the key phenological period of the corn and the main background ground feature, and realizing a corn planting distribution monitoring effect with higher precision and efficiency.

The invention discloses a forest carbon reserve estimation method based on artificial intelligence and multi-modal remote sensing data, and relates to the technical field of forest carbon reserve estimation, and the method integrates optical satellite images, laser radar data and hyperspectral image remote sensing data, improves the comprehensiveness and estimation precision of the data through multi-source data fusion, and improves the estimation precision of the forest carbon reserve. The method can more accurately reflect the coverage condition, the vertical structure and the biological diversity of the forest, overcomes the limitation of a single data source, divides the whole forest region into a plurality of sub-regions, independently constructs a carbon reserve estimation model based on the forest type of each sub-region, refines the carbon reserve estimation of different regions in a region-by-region estimation mode, and improves the estimation accuracy. According to the method, characteristics of different forest types are accurately reflected, a dynamic carbon reserve prediction model based on a differential equation is introduced, historical and real-time remote sensing data are combined, changes of carbon reserves in the time dimension are simulated, and influences of factors such as forest growth, climate changes, natural disasters and felling on the carbon reserves are captured.

The invention relates to the technical field of cross-view image positioning, in particular to a cross-view image geographic positioning method and system for an unmanned aerial vehicle and a satellite image, and the method comprises the steps: employing a Dinov2 large model after Conv-LoRA fine adjustment as a feature encoder, and extracting an unmanned aerial vehicle image feature vector and a satellite image feature vector; designing a spatial relation perception feature aggregator MSRA based on a Mama module for aggregating image features, and embedding spatial configuration features into a global descriptor; an InfoNCE loss function is adopted to train the model; and after feature vectors of a to-be-queried image and each image in the reference image library are extracted by using the trained model, a similarity score is calculated by using cosine similarity, and the unmanned aerial vehicle-satellite image pair with a high matching degree is selected to realize geographic positioning of the to-be-queried image. According to the method, Conv-LoRA is adopted to carry out fine tuning on a Dinov2 large model, so that the quantity of parameters needing to be trained is greatly reduced; a spatial configuration relationship between features is fully mined through a spatial relationship perception feature aggregator based on a Mama module.

The invention discloses a self-adaptive 4D Gaussian splashing high-precision three-dimensional reconstruction system and method, and belongs to the technical field of computer graphic processing. The invention aims to realize high-precision automatic registration of multi-source heterogeneous data and improve the calculation efficiency. The method comprises the following steps: collecting multi-source heterogeneous data; constructing a multi-modal fusion registration method, which comprises the following steps: combining satellite image data and low-altitude oblique photography data to realize spatial distribution geometric coarse registration, fusing low-altitude laser radar point cloud data and ground acquisition vehicle laser radar point cloud data to realize luminosity fine registration, establishing semantic features to assist registration, and obtaining registered multi-source data; initializing a 4D Gaussian primitive and executing adaptive splashing reconstruction to obtain an optimized 4D Gaussian splashing model; designing a cloud edge cooperative computing architecture oriented to 4D Gaussian splash reconstruction, and performing distributed parallel processing on the obtained optimized 4D Gaussian splash model; and executing quality evaluation and adaptive optimization, and outputting a final adaptive 4D Gaussian splash model.

The invention discloses a fishery sea area monitoring system based on satellite remote sensing, and belongs to the technical field of fishery monitoring, and the system specifically comprises a remote sensing data acquisition module which obtains and preprocesses sea surface temperature, chlorophyll concentration and sea surface height abnormal data; the buoy data acquisition module synchronously acquires temperature-salinity profile data of the Argo buoy array, and the layout density of the Argo buoy array is dynamically adjusted along with historical fish catch and ocean frontal surface intensity; the space-time alignment module divides a time window through an orbital period, corrects space offset in combination with a buoy drift trajectory, sets a time buffer area based on a georotation flow rate and a satellite image resolution, and generates a space-time unified three-dimensional marine environment data set; the fishery suitability calculation module constructs a biophysical coupling model, correlates surface chlorophyll and subsurface thermohaline structures, and calculates the suitability index of each water layer; the fishery division module intelligently defines a core area and an edge area according to the index three-dimensional distribution characteristics; according to the invention, accurate fishery identification based on multi-source data fusion is realized.

The invention discloses an attention-based deep learning urban flood affected population evaluation method and system, and the method comprises the following steps: collecting rainfall, flood water depth, terrain, pipe network and satellite image data of a research region, carrying out the preprocessing, and constructing a first data set and a second data set; and training and verifying the first deep learning model and the second deep learning model respectively. Inputting the first data set test set into the first model, and outputting a flood water depth prediction map; and inputting the second data set test set into the second model, and outputting a land utilization segmentation map. The two maps are superposed to obtain matching data of the flood water depth and the land utilization category, the number of exposed people is calculated by adopting an exposed population formula after land utilization reassignment, the population disaster rate is calculated by utilizing a preset flood disaster loss curve in the flood water depth, the number of exposed people and the population disaster rate are multiplied to obtain the number of affected people, and the number of affected people is calculated. And completing urban flood disaster population evaluation. According to the scheme, the accuracy, the reliability and the effectiveness of urban flood disaster population evaluation can be remarkably improved.

The invention relates to the technical field of value accounting, and discloses an ecological system total production value accounting method based on GIS spatial analysis, and the method comprises the steps: carrying out the accounting of regional basic data and satellite image data, and constructing a bidirectional dynamic semantic mapping library corresponding to land change investigation land parcel classification-ecological system type; internet of Things monitoring data and statistical report data are obtained, and a standardized data set is obtained through space-time fusion of spatial interpolation-boundary correction and a sliding window smoothing method; constructing an urban area-typical district double-level accounting index system, and synchronously calculating a real object quantity and a value quantity; a model correction factor is called based on a GIS, indexes are quantified to grid units, a result is obtained through neighborhood analysis, and after parameters are calibrated, a multi-year ecological value trend map is generated in combination with the GIS; and outputting a customized result according to scenes such as ecological compensation, and outputting a final result after the customized result is qualified through three-level quality control verification. According to the method, the requirements of practical affairs such as ecological compensation and EOD projects on accounting precision and practicability can be met.

A processor-implemented method for calculating emissions and measuring carbon sequestration levels is provided. The method includes (i) registering, by a user device, a first entity associated with an amount of carbon emission and a second entity associated with an amount of carbon absorption to create an account in a blockchain-based system using user details, (ii) obtaining satellite images and the drone images of the land cover of the second entity from a satellite source database, (iii) monitoring the plurality of satellite images and the plurality of drone images of the land cover of the second entity by creating land coordinates to calculate a carbon sequestration level associated with the second entity using an AI model, and (iv) training the AI model using an algorithm by categorizing the carbon sequestration level with known species of the second entity and the known tree species includes a drone image and the date when the image is captured.

The invention discloses a farmland precise fertilization control method based on multi-source data fusion and deep learning, and the method comprises the steps: obtaining the multi-dimensional heterogeneous data of a target farmland in real time, including the crop spectrum time series data of a satellite image, the ion concentration data collected by a soil sensor network, and the microenvironment time series monitored by a meteorological station; and constructing a Delaune triangulation network spatial index, and re-sampling satellite image data to be matched with a soil sensor network space. Calculating a time-varying mutual information entropy, extracting related microenvironment parameters as coupling factors, combining the coupling factors with soil basic parameters to form a three-dimensional feature matrix, and converting the three-dimensional feature matrix into a growth period feature vector fused with time-space correlation; the collaborative decision model is input, the main branch predicts the basic fertilization amount, the auxiliary branch detects the ion concentration space mutation area and corrects the fertilization amount, a fertilization control instruction is output, and the fertilization device is controlled to conduct precise fertilization. The problem that fertilization control cannot be accurately and efficiently performed on farmland in the prior art is effectively solved.

The invention relates to the technical field of image extraction, and provides a remote sensing image processing system, which comprises a server, an unmanned aerial vehicle, a data acquisition module, a feature extraction module, an image fusion module, an evaluation module and a result output module, the image fusion module preprocesses the satellite image and the image data to generate a multispectral image and a high-resolution image respectively, and performs fusion processing on the multispectral image and the high-resolution image to form a fused image, and the feature extraction module extracts spectral features, texture features and shape features from the fused image; the evaluation module evaluates the abnormal position according to the multi-dimensional feature description data to form an evaluation result, and the result output module generates an early warning prompt according to the evaluation result.