49 results about "Land change" patented technology

The invention relates to the field of geographic information technology and natural resource management, and discloses an automatic analysis system for land utilization flow change in land change investigation. The system comprises an object-level spatiotemporal spectrum tree reconstruction and data initialization module, a topological potential energy field construction module based on evolution entropy, a multi-scale geometric difference extraction and morphological analysis module, a variable stiffness topological adsorption and decoupling module based on a stiffness ratio, and a flow damping filtering and event serialization module driven by semantic rules. The system constructs a topological potential energy field by using a historical state vector and semantic stiffness, performs one-way adsorption, adjustment or retention on a difference polygon according to relative stiffness, and cleans invalid traffic based on a damping matrix. According to the method, through physical field simulation and semantic logic verification, high-frequency disturbance misjudgment is effectively eliminated, pattern spot boundary correction is ensured to conform to the legal weight, and the authenticity and logic self-consistency of change survey data are improved.

The invention discloses an intelligent fusion processing method and system for forest and grass wet resource pattern spots and annual land change survey data, and belongs to the field of space-time big data in the natural resource industry, and the method comprises the steps: completing the pattern layer fusion based on the annual land change survey data in combination with forest and grass wet pattern spot survey results; superposing county and rural boundaries to form an administrative fusion layer; performing subcompartment division based on the remote sensing image; merging broken classes according to a principle of adjacent positions and similar types; eliminating a pattern spot sharp angle; land classes are refined, and attribute information is perfected; the public welfare forest range and the protection level are optimized; automatically checking and summarizing to form a result layer; and finally performing statistics to generate various thematic reports. According to the method, technologies such as database storage, spatial index construction and a fusion algorithm are adopted, the data processing efficiency is improved by more than 50%, and the sharp angle processing time is shortened; and the labor cost and the time investment are obviously reduced.

The invention discloses an intelligent redevelopment research method for low-efficiency land use, which comprises the following steps of: acquiring multi-source data including land change survey data, topographic data, urban planning data, remote sensing data, point-of-interest data, mobile signaling data and social economic data in a research area, and inputting the multi-source data into a land use identification model; obtaining a spatial distribution diagram of low-efficiency land in the research area and corresponding low-efficiency factors; inputting the spatial distribution diagram and the low-efficiency factors into a redevelopment potential calculation model to obtain a redevelopment comprehensive potential value of the low-efficiency land; a plurality of redevelopment schemes are obtained based on the redevelopment comprehensive potential value, the rigid constraint of the low-efficiency land during redevelopment and the redevelopment mode library; and obtaining respective corresponding benefit index difference values after the low-efficiency land is redeveloped by using the plurality of redevelopment schemes, and obtaining an optimal redevelopment scheme from the plurality of redevelopment schemes by using a multi-attribute decision algorithm and the benefit index difference values.

The invention relates to an intelligent monitoring method and system for unused land based on a full-process closed-loop supervision mechanism, and relates to the technical field of land resource management.The method comprises the steps that a task database is established by means of obtained land related data, image spot extraction is conducted on all images in the database through a change detection model, and image spots are obtained; the method comprises the following steps: extracting pattern spots, performing a series of processing such as optimization, checking and superposition on the extracted pattern spots to obtain data fusion pattern spots, processing the data fusion pattern spots through grading and time identification to obtain graded pattern spots, finally inputting the graded pattern spots into an established system of a full-process closed-loop supervision mechanism, and performing interpretation in the system through each monitoring module. And result backflow updating and intelligent monitoring processing are performed based on the interpretation result, so that the processing precision and efficiency of the monitored pattern spots are effectively improved, the land change condition is comprehensively tracked, and the problem of insufficient timeliness and accuracy of land supervision in the prior art is solved by combining active interpretation supervision.

The invention discloses an element quantitative fusion-based urban coastal zone landscape evaluation and prediction method, which comprises the following steps of delimiting a vector boundary of a target urban coastal zone based on land change data, and dividing an area covered by the vector boundary into a plurality of grid units; associating the preprocessed spatial element data to the corresponding grid units, and constructing a structured spatial database; establishing a multi-element quantitative index system, calculating a combined weight, and outputting a comprehensive evaluation score of each grid unit through weighted summation; calculating global and local Moran's I indexes based on the comprehensive evaluation score, and identifying a spatial aggregation region and an abnormal region with statistical significance; obtaining dynamic monitoring data in a coastal zone range of a target urban area, and updating the structured spatial database; and based on the updated structured spatial database, comprehensively evaluating the score and the recognition results of the spatial accumulation area and the abnormal area, and generating an evaluation and prediction result.

The invention discloses a multi-spectrum and SAR cooperative sensing sea reclamation increment remote sensing detection method, and belongs to the technical field of land change remote sensing monitoring. According to the scheme, multi-spectrum and SAR satellite data are fused, the data advantages of optical images and synthetic aperture radar images are effectively combined, and through the wave band operation and threshold segmentation technology, the real-time performance of the sea reclamation increment is improved. According to the method, more detailed and reliable sea reclamation pattern spot information can be obtained, the spatial distribution and dynamic change of suspected sea reclamation pattern spots can be accurately identified, and the limitation that traditional optical remote sensing is interfered by cloudy and rainy weather is effectively overcome. The method is used for a computer program to execute and complete multi-source data fusion, change detection and sea reclamation suspected pattern spot detection and analysis, and can be widely applied to the fields of coastal zone ecological environment protection, coastal city planning management, ocean resource sustainable utilization, relevant policy making and the like. And a core technical support is provided for full-process monitoring and scientific decision making of the reclamation activity.

The invention relates to the technical field of geographic information processing, in particular to a field proof file information encryption tamper-proof method of a land change investigation technology, which comprises the following steps: using authorization authentication, and initializing a camera; acquiring attitude information of the equipment when the investigator takes a picture, and calculating a direction angle of the picture by combining with geographic position information of the equipment; the positioning map is integrated into the shot picture, and watermarking is carried out on the shot picture; writing the required geographic metadata into an image file of a shot picture, and encoding and packaging according to a standard format; according to the method, the technical requirements of all links are packaged into the SDK, it is guaranteed that the proof file meets the requirements, it is guaranteed that the data of the proof file is encrypted and safely transmitted, operation is convenient and fast, quality is controllable, and the method is easy to implement and popularize. And the land change investigation business field work proof efficiency is improved by more than 40%.

The application discloses an AI-based land investigation evidence photograph identification model construction method, an identification method and equipment, and the method comprises the following steps: acquiring a training data set; using the preprocessed historical evidence photographs to train the constructed initial identification model, obtaining the target identification model corresponding to each region and each period, and the target identification model is used for deploying on each terminal equipment. Through the quality detection and classification identification of the collected evidence photographs by the target identification model deployed on the terminal equipment, the problem that the business level of field personnel is uneven in the land investigation evidence work, which leads to inaccurate land class identification in the evidence photograph shooting process, and the real-time judgment of the land class identification and the evidence photograph shooting quality in the evidence shooting process by using the AI technology is solved, so that the shooting evidence photograph quality is improved, the overall land change investigation result is improved, and the subsequent land investigation evidence work is effectively guided.

The application discloses a real-time land change statistical analysis method based on a table strategy, comprising the following steps: obtaining original image patches from different themes in an image, classifying the union of the original image patches according to a preset rule to obtain minimum unit image patches, and associating the minimum unit image patches with a space unit table, a version table and a change patch table; each minimum unit image patch is associated with a unique child node, the original image patch is associated with a unique parent node, and the child node is associated with the parent node with the existence of image patch overlap source; the change patch is superimposed with the union, the space unit table, the version table and the change patch table are updated according to the intersection relationship between the change patch and the minimum unit image patch. The application has the beneficial effects that only the statistical analysis logic needs to be maintained, the original vector data does not need to be sorted, the maintenance cost is reduced, and when the analysis demand is proposed, the statistical report can be directly output from the existing vector data layer, and the timeliness is high.

The application discloses a method for intelligent verification of land change evidence photos based on multi-source information fusion, and has the characteristics that a to-be-verified image set, a travel database and a land type database are acquired respectively; travel features are determined according to the to-be-verified image set and the travel database; whether the to-be-verified image set is abnormal is judged according to the travel features and the travel database; when the to-be-verified image set is abnormal, the to-be-verified image set is marked as wrong; when the to-be-verified image set is not abnormal, first land feature types in a to-be-verified area are determined according to the land type database; second land feature types in each photo in the to-be-verified image set are determined according to the photos; and qualified to-be-verified image sets are determined according to the first land feature types in the to-be-verified area and the second land feature types in each photo in the to-be-verified image set. The application constructs a multi-level automatic verification framework, reduces the problem of false evidence from the technical level, and effectively improves the verification efficiency of evidence photos.

The invention provides a territorial change investigation database quality inspection data processing method and system, and the method comprises the steps: obtaining physical feature information of a mobile storage medium storing territorial change investigation data, and carrying out the comparison verification of the physical feature information based on an authorized medium white list in a pre-stored security database, when the verification is passed, generating a medium authentication pass signal; in response to the medium authentication passing signal, performing automatic quality inspection on the land change survey original database in the mobile storage medium based on the auxiliary database, and generating a daily change survey data quality inspection report; correcting error data in the land change survey original database based on the daily change survey data quality inspection report to obtain a daily change survey result database; and performing data consistency comparative analysis on the obtained annual proof data and the daily change survey result database to obtain a territorial change survey data quality inspection result report. By adopting the method, the territorial change working efficiency can be improved.

The application provides a kind of weak supervision detection method of bulldozing area based on high-resolution remote sensing image, the method comprises: the original remote sensing image is preprocessed and feature extraction, obtain tile set and corresponding multi-scale feature map;Seed point mining is carried out to multi-scale feature map, and the seed point set of bulldozing area is obtained;Based on the seed point set of bulldozing area, the seed point corresponding to the multi-scale feature map is determined, and the complete region activation map is generated by expanding from the seed point;Based on the original remote sensing image and multi-scale feature map, the complete region activation map is processed by multi-feature boundary perception algorithm, and the pseudo label corresponding to the boundary position is obtained;Based on tile set and pseudo label, the new remote sensing image is processed by semantic segmentation network, and the detection result corresponding to the new remote sensing image of bulldozing area is obtained.The application realizes land use survey, illegal land use law enforcement inspection and land change dynamic monitoring by only relying on pixel level bulldozing area accurate detection of image level label.

This invention relates to the field of land resource survey technology, specifically to a method for automated verification and evidence generation of land change map features. Compared with existing technologies, this solution has the following advantages: 1. Significantly improved verification efficiency: Through automated map feature matching and task distribution, the response efficiency of evidence collection tasks is improved, which can meet the timeliness requirements of large-scale land change verification; 2. Enhanced support for on-site verification and evidence collection: Mobile terminals can upload evidence data during the evidence collection process, including evidence images, evidence videos, evidence locations, and verification opinions, reducing verification omissions and misjudgments; 3. Improved traceability: A closed-loop process of "task distribution - on-site evidence collection - data feedback - result archiving" is constructed. Based on the evidence data, a traceable land change survey result data package is finally formed. The data package is associated with the map feature area, avoiding data loss or matching errors. The entire chain is traceable, reducing the cost of cross-departmental collaborative communication.

The application relates to the technical field of value accounting, and discloses an ecosystem gross domestic product accounting method based on GIS spatial analysis, which comprises the following steps: accounting for regional basic data and satellite image data, constructing a two-way dynamic semantic mapping library corresponding to land change survey block classification-ecosystem type; obtaining Internet of Things monitoring data and statistical report data, performing spatial interpolation-boundary correction and sliding window smoothing space-time fusion to obtain a standardized data set; constructing a double-level accounting index system of urban areas and typical areas, and synchronously calculating physical quantity and value quantity; based on GIS, a model correction factor is called to quantize the index to a grid unit, neighborhood analysis is performed to obtain a result, and after the parameters are calibrated, a multi-year ecological value trend graph is generated in combination with GIS; customized results are output according to scenes such as ecological compensation, and after three-level quality control verification, the final results are output. The application can meet the needs of accounting accuracy and practicability of ecological compensation, EOD projects and the like.

The application provides a land greening space marking method, system, terminal and storage medium, relates to the technical field of land surveying and mapping, and comprises the following steps: constructing a resource status database according to the latest land change survey results and the forest and grass wet survey monitoring results; converting the resource status database into a grid image, setting a seed point, merging similar pixel regions, and segmenting the grid image into land map patches; taking site factors as growth constraints of the land map patches to obtain initial plot data; performing vectorization processing on the initial plot data to obtain land greening land map patches; constructing a control boundary database and superimposing the control boundary database on the land greening land map patches, removing overlapping map patches in the superimposed image, and obtaining initial greening map patches; performing erosion and expansion processing on the initial greening map patches, and adaptively adjusting the size of a structural element of the initial greening map patches after the erosion and expansion processing to obtain vector achievement data; and the application has the advantages of improving the accuracy of land greening space marking.

The invention discloses an AI-based territorial change investigation proof system and method, and belongs to the technical field of territorial investigation. The method specifically comprises the following steps: S1, multi-source data acquisition: synchronously acquiring a satellite remote sensing image, an unmanned aerial vehicle aerial image, GIS basic geographic data and mobile terminal field acquisition data of a land investigation area; s2, historical data preprocessing: taking land investigation historical reference data, and sequentially performing denoising, coordinate unification, format standardization, data enhancement and sample labeling processing to generate a training data set and a reference comparison data set; by unifying a coordinate system, a data format and de-noising enhancement processing, the problem of inconsistency of multi-source data is solved, manual operation errors are reduced, and data consistency of large-scale investigation is guaranteed. A hybrid model of CNN, Transform and U-Net is fused, an attention mechanism and a feature pyramid structure are matched, the change area recognition precision and the boundary segmentation effect are improved, and the manual intervention cost is greatly reduced through the automatic process.

This invention provides a method and apparatus for carbon sink calculation based on multidimensional carbon stratification. The method includes: acquiring vector data of ecological protection and restoration projects in the area to be calculated and determining its completeness; for complete ecological protection and restoration project vector data, performing carbon stratification on different land categories based on different stratification factors to obtain carbon stratification results; for incomplete ecological protection and restoration project vector data, acquiring corresponding land change data, and spatially and attributely fusing the land change data with the incomplete ecological protection and restoration project vector data to obtain carbon stratification baseline data; determining fused and non-fused areas based on the carbon stratification baseline data, performing carbon stratification on different land categories in the fused areas based on different stratification factors, and performing carbon stratification on non-fused areas based on land change data to obtain carbon stratification results; and calculating carbon sinks based on the carbon stratification results. This method can improve the accuracy of carbon sink calculation.

The invention relates to the technical field of territorial monitoring data processing, in particular to a method and system based on territorial monitoring data integration. The method comprises the following steps: acquiring heterogeneous territorial monitoring data; performing heterogeneous feature dimension interaction data analysis based on the heterogeneous territorial monitoring data to generate heterogeneous feature multi-dimensional interaction data; performing territorial integrated three-dimensional model analysis according to the heterogeneous feature multi-dimensional interaction data and the heterogeneous territorial monitoring data to generate a territorial integrated three-dimensional model; performing self-adaptive territorial space recursive partitioning processing on the territorial integrated three-dimensional model to generate self-adaptive partitioned territorial space data; marking a landmark index of the self-adaptive partitioned territorial space data to obtain index partitioned territorial space data; and analyzing index partition adjacency characteristic land change transfer trend data through index partition territorial space data, and executing territorial change monitoring intelligent feedback operation. According to the invention, through integration of heterogeneous territorial monitoring, efficient territorial monitoring data feedback is realized.

The invention relates to the technical field of resource investigation, and discloses a territorial resource investigation achievement checking system based on artificial intelligence, comprising a multi-source data acquisition module used for acquiring a screening achievement from an optical remote sensing interpretation module, 3D scanning model data of an aerial photography module, and territorial change investigation data; and the data consistency comparison module is used for comparing the screening result of the optical remote sensing interpretation module and the 3D scanning model data of the aerial photographing module with the land change survey data so as to obtain the remote sensing interpretation difference degree and the three-dimensional model difference degree. By integrating multi-source data and performing consistency comparison, the accuracy and comprehensiveness of territorial resource survey result verification can be remarkably improved, and the difference degree is calculated by comparing optical remote sensing interpretation and aerial photography 3D scanning data with territorial change survey data, so that a change area is identified more accurately, personal errors and omission are reduced, and the accuracy and comprehensiveness of territorial resource survey result verification are improved. And the verification result is ensured to be more reliable.

The invention discloses an annual land change intelligent checking method and system based on space-time big data fusion. The method belongs to the technical field of big data verification. The method comprises the following steps: collecting multi-source spatio-temporal data; eliminating space-time differences and semantic conflicts among the multi-source space-time data by applying a feature matching and data alignment method, and constructing a unified space-time big data set; an intelligent detection model is constructed based on deep learning, and land utilization classification rules are combined to automatically identify land change areas and types; and according to the identification result, a checking task is automatically generated according to a preset rule for checking, and a checking feedback result is obtained. According to the invention, the problems of insufficient data fusion, low intelligent degree of change identification, split checking process and the like in the prior art are effectively solved, and the efficiency, precision and automation level of annual land change investigation are remarkably improved.

The application relates to the technical field of remote sensing image recognition, in particular to a land resource dynamic monitoring and evaluation system based on remote sensing image recognition. Time-series remote sensing images and multi-source behavior event data are synchronously collected through a data acquisition module; land change features and behavior intention features are respectively extracted by using an image coding module and a behavior coding module; through an image-behavior isogenic representation space and a training method for embedding causal time sequence constraints and geographical proximity loss which are innovatively constructed, the two types of heterogeneous feature vectors are semantically aligned; finally, the causal correlation strength between behaviors and changes is calculated by a causal reasoning module, and a monitoring and evaluation result containing an interpretable evidence package is generated. The application breaks through the limitation that traditional remote sensing monitoring can only describe surface changes, realizes automatic attribution analysis of land change driving factors, and significantly improves the causal credibility and decision support value of the monitoring result.

This invention relates to the field of land use change survey technology and discloses a method for generating evidence of land use change based on tower-based UAVs and their nests. The method includes: acquiring data of the land parcels to be evidenced and filtering them to generate a target land parcel set; allocating the target land parcels to corresponding stations based on the coverage area of ​​tower stations to generate an evidence task set; planning UAV evidence flight routes to generate flight path schemes; scheduling UAVs to perform fixed-point and directional shooting to collect evidence images; and performing quality screening, land use identification, and spatial processing on the images to generate evidence results. This invention utilizes the synergy of tower infrastructure resources and the automated operation capabilities of UAVs to achieve full-process automation of land parcel selection, flight path planning, image acquisition, and intelligent analysis.

The application relates to the technical field of land monitoring, in particular to an intelligent urban land space change monitoring system based on data processing, which comprises a processor and a memory, the processor executes a computer program stored in the memory to realize the following steps: acquiring a suspected land change region, screening the suspected land change region according to pixel values of pixel points in the suspected land change region and each edge section of the suspected land change region, obtaining a shadow region and a land change region on a current shadow correction image, performing shadow correction on the shadow region according to a non-shadow region adjacent to the shadow region, obtaining a current target image, and re-performing region change judgment on the current target image to obtain a land non-change region and a land change region on the current target image. The application can improve the effect of monitoring and identifying urban land space changes.

The invention discloses an annual land change land type accurate identification method and system based on remote sensing monitoring, and relates to the technical field of land change identification. Multi-source remote sensing data of different land types are collected, a land type identification model is constructed, and multi-source remote sensing data of a year before a to-be-monitored year are selected as reference period data; the multi-source remote sensing data of a to-be-monitored year is used as monitoring period data; respectively inputting the fused remote sensing data of the reference period and the monitoring period into the trained land class recognition model to obtain land class classification diagrams of the reference period and the monitoring period; and comparing and identifying the land category classification maps of the reference period and the monitoring period pixel by pixel through a pixel-level difference comparison algorithm to form a complete change pattern spot, and recording the change type of each change pattern spot from the land category of the reference period to the land category of the monitoring period. According to the invention, by fusing the multi-source remote sensing data, an efficient and accurate land type identification model is constructed, and rapid and accurate identification of annual land change land types is realized.

The application is suitable for the technical field of sponge city rainwater resource management, and provides a sponge city rainfall analysis method and system based on super-long duration data, which comprises the following steps: obtaining real-time predicted precipitation of a target city expansion area in a predetermined time period, a preliminary rainwater management efficiency formulated for the target city expansion area, and simultaneously obtaining underground water level fluctuation records, historical land cover change data and soil permeability historical data table of the target city expansion area; the rainwater management efficiency is optimized by combining multi-dimensional data such as underground water level fluctuation, soil permeability historical data and historical land cover change information; firstly, the primary correction factor of the rainwater management efficiency is accurately adjusted by analyzing the underground water level change rate and combining the soil permeability rate deviation; and secondly, the average slope of the historical land change trend is used as a secondary correction factor to further optimize the rainwater management efficiency.

The application relates to the technical field of land change monitoring, and discloses a land change monitoring method and system based on unmanned measurement, which comprises the following steps: dividing a target land area into a plurality of mutually non-overlapping grid units, generating a land change monitoring index of the grid units based on multispectral remote sensing data; adopting a multi-parameter trend identification algorithm to generate land change intensity of the grid units; combining the spatial structure of the grid units, adopting a spatial filtering mode to perform smoothing processing on the land change intensity of the grid units to form a visual change intensity monitoring graph. The target land area is divided into grid units, a land change monitoring index is calculated and a time sequence is constructed, a multi-parameter trend identification algorithm is used to evaluate the change intensity, a spatial filtering mode is combined to realize the visual change intensity monitoring graph, dominant change parameters are identified, and classification identification of land change types is performed, so that the spatial precision and time sequence stability of land change identification are improved.

The invention relates to a remote sensing forest land change detection method based on time sequence consistency judgment, and relates to the technical field of high-resolution remote sensing image processing technology and remote sensing image data mining based on deep learning, and the method comprises the following steps: making a forest land change detection data set based on a three-phase time sequence remote sensing image; constructing a three-temporal remote sensing forest land change detection model based on time sequence attention fusion; setting a loss function and an optimizer; training the model by using the constructed data set; prediction is performed based on enhancement during testing, and the result quality is improved by adopting post-processing. According to the method, the suppression capability on seasonal interference is enhanced by inputting three-period time sequence remote sensing images: early-period, current-period and synchronous-period high-resolution remote sensing images are adopted as input, and the historical state of the same region and the change trend of previous and later time nodes are comprehensively considered; and the seasonal pseudo change caused by the phenological cycle difference is effectively inhibited.

The invention discloses a land change tracking method, device and equipment, and aims to improve the efficiency and accuracy of obtaining land change tracking related information. The method comprises the following steps: in response to an input target range, acquiring pattern spot information of each target pattern spot in the target range of a target year from a territorial change survey map service of the target year, and performing grouping according to a land category name of each target pattern spot to obtain a land category attribute ordered dictionary over the years, the over-the-year land class attribute ordered dictionary comprises pattern spot information of target pattern spots of land class names corresponding to each year; the territorial change survey map service is generated according to the territorial change survey data; acquiring pattern spot information of target pattern spots of each land class name in the target range in the latest year from the land class attribute ordered dictionary over the years; the target pattern spot of the target land class name in the latest year is determined as a root pattern spot, the land circulation process of the root pattern spot is determined according to the pattern spot information of the target pattern spot in the target year, and the target land class name is any land class name.

This invention relates to the field of UAV remote sensing technology and provides a method for dynamic monitoring of land use change based on UAV remote sensing. The invention acquires multiple remote sensing images through UAV remote sensing monitoring; performs basic segmentation; calculates the homogeneity and heterogeneity between neighboring images; determines whether adjacent images can be merged; and, if merging is not possible, performs classification labeling and land change analysis. After basic image segmentation, the invention performs adjacent comparison analysis on multiple image targets, calculates the homogeneity and heterogeneity between neighboring images, and then judges and processes adjacent image merging until merging is no longer possible. Finally, it performs classification labeling and land change analysis to obtain the land use change monitoring results. This merging process automatically classifies different land use types, solving the problems of insufficient accuracy in type classification and computational complexity, ensuring the accuracy and real-time nature of the dynamic monitoring results of land use change.

The invention relates to the technical field of natural resource investigation and monitoring, in particular to a field work proof achievement check code method based on land change investigation technical requirements. An API (Application Program Interface) realizes verification of a proof user at a webpage end, completes proof data encryption and completes addition of a check code to proof data; therefore, the authenticity and the safety of the proof achievement are improved, the work of manually copying proof data in the middle is reduced, a unit or a company without a private key only needs to call an API interface to enable the achievement to have a check code, and verification of a national DB legal verification tool is completed through the check code. Encryption tampering prevention of the proof achievement is achieved, the proof achievement is prevented from being counterfeited, and it is guaranteed that the proof achievement meets the territorial change investigation service requirement.