82 results about "Land type" patented technology

The invention discloses a land investigation data updating method, a land investigation data updating system, a product and a medium, and relates to the field of land investigation. Superposing vector boundary data of the preset survey range on the basic geographic information layer and importing historical land information in the preset survey range to obtain a comprehensive basic information layer; acquiring actual land types, actually measured areas and ownership condition information of various lands in a preset survey range; and comparing the actual land type, the actually measured area and the ownership condition information with the comprehensive basic information layer, identifying land utilization change data, generating a land survey data updating instruction according to the land utilization change data, and sending the land survey data updating instruction and the comprehensive basic information layer to a target client. By implementing the method, the land survey data updating efficiency can be improved.

The invention relates to the technical field of geographic information processing, in particular to a map updating system based on cloud computing, which comprises a global offset correction module, a regional error division module, a local feature correction module, a residual error optimization fitting module and a computing resource scheduling module. According to the method, through offset calculation and hierarchical division of real-time geographic data, offset characteristics of multiple geographic areas are accurately analyzed, dynamic distribution optimization of data errors in a global range is achieved, hierarchical error division is combined with geographic boundary lines and multi-level data characteristics, difference values are accurately extracted, and area division is adjusted; the local feature correction optimizes error matching under a complex terrain by integrating a slope, a land type and a terrain parameter relationship, enhances terrain adaptability and detail precision, ensures high efficiency of large-scale data processing through nonlinear relationship fitting of residual values and optimization of a resource allocation proportion, and improves the accuracy of data processing. And meanwhile, the map updating response speed is increased.

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 relates to a remote sensing image segmentation method and system based on land utilization, and the method comprises the steps: collecting continuous periodic remote sensing images of the same region, marking a land type and a seasonal factor vector, and determining whether to carry out zoning or not according to a latitude span; then identifying the boundary of the miniature land parcel, calculating the boundary identification degree, delimiting a protection area, respectively calculating the seasonal factor vectors of the non-protection area and the protection area, and fusing the seasonal factor vectors into a global seasonal factor; secondly, preliminarily segmenting the image by adopting a fuzzy C-means clustering model in combination with a global seasonal factor, and finally carrying out image re-segmentation and verifying the accuracy in combination with a plot seasonal feature and boundary recognition degree correction model; according to the method, through operations such as fine seasonal factor vector construction, latitude zoning, protection area delimiting and multi-feature fusion, the shadow area land type classification accuracy is effectively improved, the influence of seasonal variation and boundary blur in a complex earth surface scene can be reduced, and the robustness and adaptability of the model are enhanced.

The invention relates to the technical field of data processing, in particular to a data display method and device based on territorial resource investigation, a medium and a product, and the method comprises the steps: carrying out the preprocessing based on each land type pattern spot, and obtaining a plurality of target land type pattern spots; then, base map drawing is carried out based on the multiple target land type pattern spots, and a land type pattern spot map is obtained; performing land utilization analysis based on the remote sensing image returned by the unmanned aerial vehicle, and determining a land utilization current situation map; meanwhile, ownership confirmation is carried out based on the land type pattern spot pattern and the land ownership confirmation information, and a land ownership confirmation result is obtained. And finally, performing map fusion based on the land type pattern map, the land utilization current situation map and the land ownership confirmation result to obtain a territorial resource panorama. The territorial resource panorama with multiple layers overlapped is used for visually displaying the spatial distribution, form and mutual relation of territorial resources, so that the data can be quickly understood and analyzed, and the readability and usability of the data are improved.

The invention discloses a multi-task remote sensing semantic change detection method based on a visual state space model. The method mainly solves the problems that in the prior art, semantic segmentation and change detection subtasks lack consistency, and global feature information is not fully utilized. According to the implementation scheme, the method comprises the following steps: introducing a visual state space model into a feature extraction encoder, obtaining multi-level semantic features of image global context information modeling, and enhancing the multi-level semantic features; respectively obtaining a semantic segmentation result and a binary change result of the image from the enhanced multi-level features through a semantic segmentation decoder and a change detection decoder; and in combination with a semantic segmentation result and a binary change result, a semantic change result with higher discrimination is generated, and multi-class semantic change detection of the remote sensing image is realized. According to the method, the specific category information in the change area can be accurately classified while the image change area is efficiently extracted, and the method can be used for land resource utilization management and monitoring of the change of the land type of a specified area.

The invention discloses a map database-based land type map spot storage and analysis method and system. The method comprises the following steps of: acquiring and processing data; storing data; monitoring pattern spot type distribution analysis; performing annual change investigation pattern spot and land type change analysis; and reading and displaying data. According to the method, spatial analysis is carried out on land type change monitoring pattern spots and annual change investigation pattern spots, and a method based on a pattern database is designed to store analysis results; the incidence relation and important attributes of the change monitoring pattern spots and the annual change pattern spots can be expressed on a visual platform, and the monitoring type distribution condition and the annual change survey of the change conditions of different land types in a certain space range are analyzed; according to the storage and analysis method, the internal relation between the change monitoring pattern spots and the annual change pattern spots is visually presented, the effectiveness of change monitoring pattern spot analysis is improved, the data value of the monitoring pattern spots is deeply mined, natural resource data analysis and application are facilitated, and planning and decision making of related departments of the natural resource industry are supported.

The invention relates to the technical field of remote sensing data analysis, in particular to a land resource management data analysis system. According to the method, the spectral reflectivity change under the earth surface multi-direction illumination is obtained and the reflection gradient response information of the spectral reflectivity change is extracted, so that the spatial difference of the earth surface reflection characteristics at different angles can be more comprehensively presented; according to the method, surface land type division is more accurate in gray continuity and spatial structure expression, so that potential fission boundaries are identified and reasonable section classification is carried out through quantitative comparison and spatial aggregation analysis of structure texture differences in boundary identification between different regions, and the identification accuracy of the boundary identification between different regions is improved. The stability and variability of the interior and boundary structures of the land areas are effectively described, and on this basis, the area boundary is reconstructed and combined with texture and reflection clustering grouping, so that the land type state and the distribution boundary of each area can be finely judged.

The invention discloses a low-efficiency land investigation and identification method. The method comprises the following steps: acquiring a low-efficiency land sample from an established comprehensive characteristic index system for low-efficiency land identification; clustering the low-efficiency land samples by using K-Means, analyzing key influence factors of different types of low-efficiency land samples by using XGBoost-SHAP, and quantifying to obtain an environmental variable feature importance weight; and by using a geographic similarity classification space speculation method, carrying out similarity evaluation on all the to-be-identified land parcels according to the obtained environmental variable feature importance weights to obtain potential low-efficiency land use types. According to the method, different low-efficiency land types are divided by utilizing interpretable machine learning, and the feature importance of the low-efficiency land types is extracted, so that the problems of various low-efficiency land influence factors, complex types and unknown influence mechanisms are solved. The problems of few low-efficiency land use samples and single-class samples are solved by utilizing a geographic similarity classification space speculation method.

The invention discloses a land ecosystem carbon reserve change prediction method based on a multi-land utilization scene, and relates to the technical field of ecosystem carbon reserve prediction. The method comprises the following steps: setting a spatial resolution of land utilization, obtaining historical land utilization data of a to-be-evaluated region, carrying out reclassification, forming a first-level land type of the to-be-evaluated region, obtaining a high-definition land utilization type graph of the to-be-evaluated region, constructing a driving factor influencing land utilization change, and obtaining a high-definition land utilization type graph of the to-be-evaluated region. Historical data of the driving factors are obtained to serve as land utilization driving samples, land utilization changes of the to-be-evaluated area are simulated through a Flus model, the land utilization scene type of the area is set according to a development planning strategy of the to-be-evaluated area, and according to the simulation result of the Flus model, the land utilization scene type of the to-be-evaluated area is set. And obtaining prediction results of the number of typical land utilization and the spatial distribution condition of the to-be-evaluated region under each scene type, and respectively calculating the total carbon reserve and carbon density of the to-be-evaluated region under each prediction result by using the InVEST model.

The invention relates to the technical field of land type classification, in particular to a land type classification method based on territorial resource data, and the method comprises the following steps: S1, data preparation; s2, determining a classification target, a classification standard and a classification range; s3, land feature extraction and analysis; s4, data preprocessing and cleaning; s5, land feature extraction and selection; s6, formulating a classification method and algorithm selection; s7, land type division and marking; s8, determining a classification method and technology; and S9, formulating a classification standard and index system. Through land feature extraction and analysis, data preprocessing and cleaning and land feature extraction and selection, land data diversity understanding and effective management can be achieved, classification precision and efficiency are improved, and it is ensured that the classification result is more accurate and reasonable through verification and precision of the classification result; the reasonable utilization and protection level of land resources is improved through result optimization, and the accuracy and applicability of land classification are improved.

The invention relates to a carbon reserve distribution prediction and evaluation method based on conflict detection and multi-objective optimization, and belongs to the technical field of geographic information. Comprising the following steps: firstly, constructing a logistic regression model, performing logistic regression analysis to obtain a corresponding regression coefficient, and then sequentially calculating an occurrence probability, a local advantage index and a conflict strength index of each land type; then, multi-target constraint condition setting is carried out, coordination of land utilization conflicts is carried out based on multi-target constraints, and an optimized land utilization distribution pattern is obtained; and finally, inputting the land utilization data and the carbon library data before and after coordination into the InVEST model to obtain corresponding carbon reserve distribution conditions before and after optimization. According to the method, a framework combining a land utilization conflict identification and coordination method and an InVEST model is designed, an existing land utilization conflict identification and coordination method and a carbon sink distribution optimization method are improved, and the influence of land conflicts on regional carbon reserves is revealed.

The invention relates to the technical field of data analysis, in particular to a land resource management data analysis method and system based on cloud computing, and the method comprises the following steps: obtaining a difference time node plot number and a purpose field, recognizing a number evolution chain and establishing mapping, extracting a land class sequence to generate a stability label, analyzing the spatial continuity to construct an attribution path, and analyzing the attribution path. And indexing a utilization rate fluctuation trend paragraph, and identifying a purpose fusion direction to generate a fusion layer set. According to the method, through joint comparison of a serial number structure, coordinate overlapping and a purpose field, a stable land class transition chain is established, it is ensured that land class time sequence features are effectively described, the contiguous direction of a space dot matrix and the consistency of land class affiliation are combined, splicing of the space affiliation relation between land parcels is achieved on the regional level, and the splicing efficiency of the space affiliation relation between land parcels is improved. The identification precision and the spatial expression definition of the land type change are improved, and the spatial analysis capability of the land type change trend and the dynamic perception capability of the land utilization efficiency are effectively enhanced.

The invention discloses a land type adjustment and simulation method based on carbon neutralization constraint, and the method comprises the following steps: obtaining land data of a target region, and calculating the carbon flux of each classification space; constructing a carbon profit and loss weight function based on the carbon flux, the ecological benefit and the economic benefit as an overall constraint condition; reversely deducing each land space area corresponding to a preset time node on the basis of a Markov chain model, and taking the land space areas as stage constraint conditions; performing NSGA-III optimization solution based on the constraint condition to obtain a corresponding land use adjustment scheme; an improved PLUS model is adopted to simulate a land use adjustment scheme, the carbon cost of land conversion is incorporated into a land utilization simulation process, and a genetic algorithm is utilized to optimize model parameters to obtain a land space simulation result of a target year. According to the method, a carbon cycle mechanism, a carbon sink optimization target and a land utilization decision full process are coupled, a closed loop of carbon constraint-land simulation-policy assessment is realized, and scientific decision support is provided for regional carbon emission reduction and ecological protection.

The invention discloses a homeland space planning auxiliary compilation method and system based on big data, and belongs to the technical field of land planning, and the method specifically comprises the steps: obtaining a remote sensing image map of a target region, and carrying out the land recognition of the remote sensing image map, and obtaining a specific position region and a region area of any land type in the target region; the method comprises the following steps: acquiring pre-planning information, calculating to obtain a to-be-planned area of any land type, screening lands of a target region, screening out a to-be-planned region corresponding to the to-be-planned area most conforming to the land type, and taking the to-be-planned region as an optimal planning region of the land type; and determining an optimal planning area of any land type, drawing a target boundary line corresponding to the optimal planning area, and outputting a planning remote sensing image map. According to the method, the land is planned by comprehensively considering the topographic features on the basis of the existing land planning of the target area, so that the accuracy of the land planning result is improved, the land resource allocation is optimized, and the planning operability is improved.

The application belongs to the technical field of space-time data analysis, and discloses a POI data-based remote sensing image semantic label extraction method, which realizes the extraction of the semantic content label of the remote sensing image by analyzing the POI data in the coverage area of the remote sensing image from the perspectives of place names and land types. Firstly, a land type label set and a place name label set as well as a density threshold and a coverage threshold of the land type label and the place name label are preset. Then, a POI data set in the coverage range of the remote sensing image is calculated and obtained, the attributes of the POI data set are subjected to Bert semantic similarity calculation with the land type label set and the place name label set, and labels with high similarity are obtained as a pre-extracted label set of the land type and the place name. Subsequently, the coverage and the density of each label in the pre-extracted label set are calculated, and labels with the coverage and the density both greater than the density threshold and the coverage threshold are screened out as the final semantic label extraction result. The method solves the problems of high cost, great difficulty and poor effect in the extraction of the semantic label in the prior art.

The invention provides a land type information surveying and mapping system based on remote sensing interpretation, and the system comprises an image processing module which is used for obtaining a high-resolution remote sensing image and carrying out the superpixel segmentation preprocessing; the land class extraction module is used for capturing texture features of land classes and identifying initial edge features, and associating the texture features and the initial edge features to obtain an initial land class region block; the land class classification module is used for dividing the initial land class region blocks into two classes of land class edge boundary integrity and land class edge boundary incompleteness, performing edge boundary reinforcement and edge boundary repair on the initial land class region blocks, then performing reinforcement, outputting land class repair region blocks and performing scene classification; and the land class integration module is used for fusing the classified land class restoration region blocks into a land class map capable of accurately reflecting land cover land class types. According to the method, different land types are processed respectively and fused into the land map capable of accurately reflecting the land cover land type, and an important practical basis is provided for subsequent land utilization.

The application provides a planning partition-land conduction effectiveness evaluation method and device, which has the following characteristics: step S1, constructing a planning partition-land function attribute conduction matrix; step S2, obtaining a plurality of planning land vector graph patches of the planning partition; step S3, obtaining a dominant land type; step S4, aggregating the planning land vector graph patches to obtain land aggregation graph patches; step S5, obtaining coordinate values of each vertex of the planning partition and the land aggregation graph patches; step S6, calculating a characteristic vector; step S7, calculating a characteristic vector; step S8, calculating a conduction deviation coefficient; and step S9, obtaining a conduction effectiveness evaluation result according to the conduction deviation coefficient. In summary, the method can obtain the conduction effectiveness evaluation result.

The invention discloses a centralized new energy power line path generation method, system and device, and a medium. The method comprises the steps of obtaining a plurality of historical line path cases and case parameters thereof in a preset area of a centralized new energy station; according to the historical line path cases and the case parameters thereof, a traversal method is adopted to quantify the land type weight and the terrain information weight of a target area corresponding to the centralized new energy station; based on the land type weight and the terrain information weight, with the shortest power line path of the target area as a target, generating an initial power line path through an A star algorithm and a reinforcement learning algorithm, and optimizing the initial power line path to obtain a power line optimized path; according to the environment data of the target area and the current voltage grade, tower arrangement is carried out on the power line optimization path, a final power line path arrangement strategy of the target area is generated, and the generation efficiency and accuracy of the centralized new energy station power transmission line are improved.

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 application discloses a land use status map data generation method based on large-scale topographic maps, and steps include: collecting and establishing basic databases such as large-scale topographic maps, high-resolution image maps and the like; constructing a generation path of a land use status map basic unit, deconstructing elements, structures, information and the like of the large-scale topographic maps, analyzing natural and artificial features of the land, and determining a utilization direction of geographic information elements in the land use status map data generation; constructing a basic element base map through element extraction and data integration, combining land feature status characteristics to demarcate boundaries of the land and identify land types, and optimizing overall expression of the present land use; and the like. The land use status map data generation method breaks the path of conversion from the topographic map to the land use status map data, has advantages such as fine land use classification, accurate land block space and detailed spatial expression, and can provide multi-level technical support for national space planning and natural resource management.

The application discloses a land management system and method for highway engineering projects, and relates to the field of land management.The system comprises a land project change module, which changes land types, pile numbers, quantities and land unit prices according to actual site construction conditions on the basis of land project information to obtain updated land project information; an audit module, which sends the corresponding updated land project information to each audit node according to a first process audit chain; a protocol generation module, which generates a land project protocol by calling a preset protocol template when the audit is passed; the audit module, which sends the corresponding land project protocol to each audit node according to a second process audit chain; a settlement generation module, which generates a land settlement protocol when the audit is passed; the audit module, which sends the corresponding land settlement protocol to each audit node according to a third process audit chain; and a settlement archiving module, which settles according to the land settlement protocol when the audit is passed.The application improves the efficiency of land management in highway engineering projects.

The invention discloses a photo land type identification method based on artificial intelligence. The method comprises the following steps: acquiring to-be-identified data; and inputting the to-be-identified data into a photo classification and interpretation model to obtain a classification result, the photo classification and interpretation model being obtained by constructing a deep convolutional neural network model and performing evaluation and optimization after training through a training set, and the training set being photo data. According to the invention, based on massive field photo samples with local vegetation geographic features, the artificial intelligence identification technology is used to rapidly carry out land type identification on the proof photo, manpower for land type use of the field photo is effectively reduced, dependence of such work on manpower is reduced by introducing the artificial intelligence technology, and the work efficiency is improved. Personnel are released from repetitive mechanical work, labor cost and time cost are reduced, and therefore the efficiency of investigation and monitoring work is improved.

The invention discloses a land recommendation service system and method based on big data, and relates to the technical field of land recommendation, and the system comprises a land type primary selection module, a user attention degree collection module, a key element analysis module, and an adaptation degree analysis and land recommendation module, which are used for receiving the type demands of user land use, the method comprises the following steps: preliminarily screening lands in a database according to type requirements, obtaining a primarily selected land set, setting an interactive weight distribution interface, obtaining the attention degree of a user to attention elements, respectively analyzing the adaptation degree of the attention elements to the user, and comprehensively analyzing the adaptation degree of the primarily selected land to the user according to the attention degree and the adaptation degree. According to the method, the primarily-selected land is regarded as a circular area with an actual influence range, the concept of land influence distance is introduced, the convenience degree of daily commuting of the user is reflected more truly, and time and energy are saved for the user through automatic evaluation and recommendation.

The invention relates to the technical field of land management, in particular to a land full-process management method and system, electronic equipment and a storage medium, and the method comprises the steps: collecting the land information of a to-be-managed land according to the land type of the to-be-managed land; based on a preset information approval process, approving the land information to obtain an approval result; and when the approval result passes, inspecting the to-be-managed land according to an inspection template corresponding to the to-be-managed land so as to realize full-process management of the to-be-managed land. According to the invention, the accuracy of land information is ensured, patrol is carried out based on the patrol template, and the pertinence of patrol work is improved, so that the accuracy and efficiency of land full-process management are integrally improved.

The invention belongs to the technical field of remote sensing image processing, and particularly provides a land resource monitoring method and device based on a remote sensing image, and electronic equipment, and the method comprises the steps: obtaining a target remote sensing image of a to-be-monitored land resource, the target remote sensing image comprises at least one mask region, the mask region is a region which is determined to be the same land type in history; framing the target remote sensing image into a plurality of framing image blocks, and segmenting at least one pattern spot in a mask area of each framing image block; aiming at each pattern spot, a plurality of target block diagrams of different scales are determined, and the target block diagram of each scale at least comprises each pattern spot; and based on the multiple target block diagrams of different scales, identifying target land types of land resources in the target block diagrams through a pre-trained land type identification model. Through the technical scheme provided by the invention, the accuracy of land resource monitoring can be improved.

The invention discloses a method for identifying rural three-life space land types based on deep learning. S1, downloading data from the Google Earth and converting the data; s2, importing the converted raster image into ArcGISPro, and exporting vector data of which the format is an shp format; s3, judging the processing format of the metadata according to the land use type, and exporting the training data after selecting the metadata processing format; s4, performing model training by using the training data exported in the S3; s5, identifying the land to obtain a land type raster image; s6, smoothing the raster image exported in the S5 by using a mode filtering tool, vectorizing the processed raster by using a raster surface conversion tool, and exporting regular data; and S7, obtaining vector data of rural ecology, production and living space. According to the invention, accurate identification of ecological, production and living spaces is realized.

The invention relates to a territorial space land use refining method, device and equipment and a storage medium, and belongs to the technical field of geographic information systems and territorial space planning, and the method comprises the following steps: obtaining multi-source spatial data; carrying out data reclassification, and establishing a mapping relation between the original category of the POI and a land use sea classification standard for the land and the land space; performing coordinate system unified conversion and standardized preprocessing on the multi-source spatial data; generating a territorial space planning basic unit based on the business data; the AOI planar evidence and the re-classified POI punctiform evidence are fused, and the comprehensive evidence intensity of different land use types in each planning basic unit is calculated; determining the land use type of each planning basic unit by combining a multi-threshold judgment mechanism with a spatial neighborhood smoothing algorithm; and outputting a land use identification result and generating a land use thematic map for land space planning. According to the method, the accuracy of land use identification, the hybrid function identification capability and the spatial rationality of the result are effectively improved.

This application discloses a method, apparatus, and electronic device for determining land types, relating to the field of data processing technology. The method includes: determining multiple first land types and corresponding first area data for each first land type in a target area based on digital elevation data; acquiring an orthophoto map of the target area; processing the orthophoto map using a target neural network model to obtain multiple second land types and corresponding second area data for each second land type in the target area; and comparing and analyzing the multiple first land types, the corresponding first area data, the multiple second land types, and the corresponding second area data to obtain multiple target land types and corresponding target area data for each target land type in the target area. This application solves the problem of low accuracy in identifying land types and area data of target areas in related technologies.

The invention relates to the technical field of rural livable evaluation, and discloses a rural livable data dynamic acquisition and management method based on multi-source data, comprising the following steps: step S101, obtaining coverage rates of different land types through an image semantic segmentation model; step S102, preprocessing to obtain a feature matrix; s103, obtaining the carbon reserve through the carbon reserve prediction model; step S104, obtaining the carbon flux through the carbon flux prediction model; s105, calculating livable scores according to the coverage rates, the carbon reserves and the carbon fluxes of different land use types of the country to be evaluated; the coverage rates of different land use types are identified through the image semantic segmentation model, time-space analysis is performed on the remote sensing data of the multiple sub-regions through the carbon reserve prediction model and the carbon flux prediction model, and the mapping relation between the remote sensing data and the carbon reserve and the carbon flux is established, so that the data acquisition difficulty is reduced, and the data acquisition efficiency is improved. And the livable score is obtained through comprehensive calculation, so that the evaluation accuracy is improved.