26 results about "Land type" patented technology

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 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 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 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.

A land use classification method based on remote sensing images, comprising the following steps: step 1: obtaining the original image of remote sensing images of urban and township land, marking the pictures after screening, and a total of four categories: cultivated land, orchard, forest and building; step 2: cutting and data enhancement are carried out on the marked pictures to make a data set; step 3: the training set in the prepared data set is put into a feature extraction network and a multi-scale fusion up-sampling structure for training to obtain a segmentation model; step 4: the remote sensing image to be classified is put into the trained segmentation model to obtain the result; the purpose of the present application is to solve the problems of low small target precision and poor edge segmentation effect when the land categories are many and the features are complex in the land classification method based on remote sensing images, and a method capable of realizing more accurate identification result of various land types in remote sensing images is proposed.

The invention relates to the technical field of carbon emission, in particular to a carbon emission evaluation method and system for land utilization change. The invention discloses a carbon emission evaluation method and system for land utilization change. The system comprises a data preprocessing module, a carbon emission evaluation module and a carbon emission evaluation correction module. According to the method, the remote sensing image of the actual area is aligned with the planning design drawing, land type labeling is carried out on the rasterized image of the actual area in combination with the land type labeling network, the land type change diagram is constructed, simulation analysis is further carried out in combination with the carbon emission variation corresponding to land type conversion in the historical record, and the real-time analysis result is obtained. Inputting a carbon emission change distribution analysis network to generate a carbon emission change distribution diagram; the carbon emission change distribution diagram is corrected by combining the spatial distribution relation on the basis of the land type transformation and aggregation area, and the carbon emission change distribution diagram is optimized by using convolution operation, so that the corrected image can more accurately reflect the carbon emission change of the planning and design area.

The invention provides a natural resource element space-time process analysis method based on land type pattern spots. According to the method, a geographic vector pattern spot is used as a minimum space unit, spatial and temporal change types (newly increased, changed and killed) of land cover plaques are defined, and a corresponding algorithm is constructed. Firstly, multi-period basic geographic information data are collected and preprocessed, pattern spots are extracted and endowed with unique codes, and a topological structure and standardized codes are checked. And then realizing a spatio-temporal variation algorithm, judging the pattern spot variation type and calculating the area variation. And storing the data into a space-time database adopting an Oracle + ArcSDE engine, and carrying out space-time process analysis and visual expression, including a plurality of modes such as time axis animation and the like. The method is high in precision, avoids precision loss, is high in space-time complexity expression capability, is good in visualization effect, can provide scientific basis and decision support for the fields of natural resource management and the like, and is wide in application prospect.

The invention discloses a differential pattern spot repairing and attribute inheriting method oriented to land class docking. The method comprises the following steps: reading a background land class pattern spot and an updated land class pattern spot from a database by using GeoPandas; a result table RES is initialized, a unified coordinate system is CGCS2000, the area and overlapping area of each pattern spot are calculated, and an overlapping sequence table BLPULP is constructed; filtering updated land class pattern spots in the BLPULP through an overlapping proportion threshold value, extracting an overlapped BLPID unique sequence from the BLPULP for subsequent iteration processing, and if the overlapping proportion of a certain background land class pattern spot and the updated land class pattern spot is greater than a preset threshold value, directly inheriting an updating attribute and storing the updating attribute into RES; if the overlapping proportion of a certain background land type pattern spot and the updated land type pattern spot is not greater than a preset threshold value, screening an internally tangent pattern spot and an edge matching pattern spot, processing the two pattern spots, and adding the processed pattern spots to RES; and transferring the final RES list into an SRI Shapefile format through the GeoPandas, so that the final RES list can be directly used. According to the method, batch repairing and updating of differential pattern spots are realized, and the efficiency and precision of land class docking are remarkably improved.

The invention relates to the technical field of land utilization optimization, and particularly discloses a land utilization multi-objective optimization method and device, equipment and a storage medium, and the method comprises the steps: obtaining target economic data, target environment data and target control constraint data; after an economic benefit function and an ecological benefit function are constructed, a multi-target genetic algorithm is utilized to determine balance benefit points of the land classes under target control constraints; inputting the target environment data and the target economic data into an FLUS model for evaluation to obtain a large-area type suitability atlas and screening out a small-area sensitive region; inputting an actual existence point corresponding to the small-area sensitive region into a MaxEnt model for evaluation to obtain a small-area type suitability atlas; and determining a land utilization space distribution result of the target area in combination with the balance benefit point. According to the scheme, the land utilization simulation precision of the small-area ecological sensitive area is improved by constructing a coordinated land utilization optimization system of control constraints, ecological benefits and economic benefits.

The invention discloses a forest land type differentiation data processing system and method based on a GIS (Geographic Information System). The method comprises the following steps: automatically setting an area threshold value according to a woodland type through a parameter mutual exclusion constraint mechanism of the woodland type and a data source; according to a global topology overlapping processing algorithm, the overlapping relation of different types of forest lands is processed according to priorities; historical data is fixed with a non-deduction rule; based on the spatial index of the grid division method, the size of the grid can be automatically adjusted within the range of 10-500 meters; detecting the adjacency relation through intersection of the buffer areas; the component analysis algorithm and the fusion tool are connected to execute element combination; and detecting rule keywords, and identifying classification errors according to Class values and Layer field contents. According to the method, the technical problems of parameter configuration conflict, incomplete topology overlapping processing strategy, low spatial index efficiency, inaccurate element combination and the like are solved, and the accuracy and efficiency of forest land data processing are improved.

The invention relates to the technical field of geographic information processing, in particular to a multi-modal space-time information fusion-based land type recognition method, which comprises the following steps of: constructing a multi-modal land type recognition model framework, and inputting photo data of a to-be-recognized ground feature and corresponding space-time metadata; performing feature extraction on the input photo data based on an image feature extraction sub-network to obtain image features; performing feature coding on the geographic position and the space-time metadata based on the space-time information coding sub-network to obtain a space-time feature vector; sending the image feature representation and the spatial-temporal feature vector into a multi-modal feature fusion module, and performing alignment fusion to obtain a multi-modal feature; processing the multi-modal features by adopting a dynamic fusion strategy to obtain a fusion feature vector; according to the method, a multi-modal fusion strategy is adopted, so that the model can give consideration to local details and global environment information, and the precision and stability of land class recognition are remarkably improved.

The land space land use refinement method, device, equipment and storage medium of the present application belong to the technical field of geographic information systems and land space planning, and the method comprises the following steps: acquiring multi-source spatial data; performing data reclassification to establish a mapping relationship between POI original categories and land space land use classification standards; performing coordinate system conversion and standardization preprocessing on the multi-source spatial data; generating land space planning basic units based on business data; fusing AOI surface evidence and reclassified POI point evidence to calculate the comprehensive evidence strength of different land types in each planning basic unit; determining the land use type of each planning basic unit through a multi-threshold determination mechanism combined with a spatial neighborhood smoothing algorithm; and outputting the land use recognition result and generating a land space planning land use thematic map. The present application effectively improves the accuracy of land use recognition, mixed function recognition ability and spatial rationality of the result.

The invention relates to the technical field of land area measurement, and discloses a land area information acquisition system based on territorial space planning, comprising: a data acquisition module for obtaining a digital elevation model; the terrace three-coupling perimeter gain calculation module is used for calculating and obtaining terrace three-coupling perimeter gains; the direction sensitivity coefficient calculation module is used for calculating to obtain a direction sensitivity coefficient; the equivalent external expansion radius calculation module is used for calculating to obtain an equivalent external expansion radius; the area correction module is used for calculating to obtain a corrected area; and the summarizing output module is used for summarizing the corrected area and uncertainty of each land type. According to the method, a terrace frequency band is extracted according to a digital elevation model, a terrace weight is generated, terrain noise of a non-terrace area can be filtered, and a three-coupling perimeter gain kernel is constructed by fusing slope projection zoom illumination visibility and an orthographic and elevation model error sensitive item; therefore, the high-precision and high-credibility requirements of terraced space planning on terraced field area information are met.

The invention provides a method and system for screening available land parcels of reserve land, and the method comprises the steps: constructing a remote sensing land database based on ArcGIS, integrating multi-source heterogeneous geographic space data, and carrying out the preprocessing of original remote sensing land data; a plot type deduction system is established, and deducted land is obtained; and constructing an artificial intelligence large model training data set and optimizing a large model system, and carrying out land type classification identification on the deducted land by adopting a large model. According to the invention, automatic and intelligent screening and identification of available land are realized, efficient utilization and dynamic supervision of land development are realized, and a method and system support is provided for promoting land utilization and development, layout optimization and system governance.

This invention relates to the field of carbon emission technology, specifically to a method and system for assessing carbon emissions from land use change. The method and system include: a data preprocessing module, a carbon emission assessment module, and a carbon emission assessment correction module. This invention aligns remote sensing images of the actual area with planning and design maps, and uses a land type labeling network to label the rasterized images of the actual area with land types, constructing a land type change map. Further, it simulates and analyzes the carbon emission changes corresponding to land type transitions in historical records, and inputs this data into a carbon emission change distribution analysis network to generate a carbon emission change distribution map. By correcting the carbon emission change distribution map based on the spatial distribution relationships of land type change clusters, and using convolution operations to optimize the carbon emission change distribution map, the corrected image can more accurately reflect the carbon emission changes in the planning and design area.

The application provides a remote sensing-based terrace mapping method, device, equipment and storage medium, wherein the method comprises the following steps: acquiring a land satellite remote sensing image and a digital elevation model of a target region; obtaining classification features of all pixels in the target region according to the land satellite remote sensing image and the digital elevation model, wherein the classification features are used to determine the land type corresponding to the pixels, and the land type comprises a terrace or a non-terrace; dividing the target region into multiple sub-regions, inputting the classification features of the pixels in each sub-region into a classifier corresponding to the sub-region, and obtaining the land type of the sub-region, wherein the classifier is obtained after training and testing using training samples and test samples corresponding to the sub-region; and comprehensively obtaining the classification results of each sub-region to obtain a terrace map of the target region, wherein the terrace map is in the form of pixels. The spatial distribution information of the terraces can be accurately and quickly obtained.

This invention relates to the field of land planning technology, specifically to a land use planning optimization method and system based on spatiotemporal big data. A land use planning optimization system based on spatiotemporal big data includes: a historical land use data sequence acquisition module, a change characteristic calculation module, and an iterative optimization module. This invention acquires multi-temporal land use remote sensing raster map sequences of the land to be planned, constructs a pixel-level land use change dataset, comprehensively characterizes the land use evolution trends and change sensitivity of each location at different historical stages, and ensures that the model can identify the spatial distribution characteristics of stable areas and high-frequency change areas. Based on land type change statistics, a global transition matrix and trend probability vector are constructed to achieve quantitative modeling of land use evolution patterns, and this is used for trend-guided construction of the initial population of a genetic algorithm, improving the directionality and convergence efficiency of the optimization.

The invention belongs to the technical field of territorial space recognition, and discloses a territorial space land classification intelligent recognition system. Comprising the following steps: acquiring multi-temporal remote sensing image data of a target area, constructing a temporal quality index fusing a phenological matching degree, a cloud cover dispersion degree and time sequence continuity, selecting a reference image and carrying out phenological normalization compensation to realize cross-temporal spectrum consistency correction and phenological period alignment; according to the method, a differential cloud and haze reconstruction strategy is introduced, cloud layer shielding and haze shielding are taken as different processing objects, shielding types are intelligently distinguished through edge gradient features, reconstruction is performed by adopting time sequence weighted interpolation and spectrum defogging enhancement, and a pixel-object-landscape three-layer multi-scale space-time fusion feature system is constructed. The classification confidence coefficient and the reconstruction quality score are quantified, and the land use type and the risk area are accurately identified; the method has the full-process quality control capability and high accuracy, and effectively solves the problem of inconsistency of cloud and mist shielding and time phases.

The invention relates to the technical field of land monitoring, in particular to a land resource dynamic monitoring analysis system and method, and the method comprises the steps: determining the space boundary and time range of a monitoring region, and obtaining the image information of the monitoring region through a satellite; a mixed strategy of'deep learning automatic classification + manual accurate verification 'is adopted, land properties of obtained image information are classified, differential monitoring periods are implemented for land types with different change characteristics, meaningless high-frequency monitoring on low-change-rate areas such as cultivated land fallow areas and mature forest land is avoided, and the land quality is improved. The redundant workload of satellite image acquisition, data processing and manual verification is reduced; meanwhile, monitoring resources are preferentially concentrated in high-change-rate areas such as a construction land development area and an ecologically fragile area, and the overall monitoring cost is reduced on the premise that monitoring full coverage is guaranteed.

The invention relates to a city surrounding land utilization optimization method based on deep reinforcement learning, and belongs to the technical field of city science. The method comprises the following steps: inputting pixel features in an original land type image into a constructed state encoder based on graph sampling to obtain feature information of a to-be-optimized point, and taking the feature information as state information of the to-be-optimized point; setting a reward item to decide the optimization type of the to-be-optimized area, calculating a dominant function according to the value of the optimization type, and adjusting an optimization strategy based on the dominant function; and after optimization is finished, performing cooling amplitude prediction by using a trained space Durbin model, so as to obtain a land utilization optimization result based on a cooling amplitude prediction result and a preset landscape index. The method aims at solving the technical problems that in the prior art, a high-dimensional continuous remote sensing feature space is difficult to process directly, and obvious defects exist in the aspect of modeling a multi-level space semantic relation.

This application discloses a method and system for evaluating the site selection of a spoil disposal site, belonging to the field of artificial intelligence technology. The method includes constructing a dataset and an improved UNet network, and training and testing the improved UNet network based on the dataset; acquiring a target remote sensing parameter image of the target spoil disposal site, and processing the target remote sensing parameter image using the improved UNet network to obtain a land use segmentation map of the target remote sensing parameter image; and determining whether the target spoil disposal site is qualified based on the land use segmentation map. This application segments the target remote sensing parameter image corresponding to the target spoil disposal site using the improved UNet network to obtain a land use segmentation map that segments various land types, rivers, and human living areas. By combining spoil disposal site selection regulations with the land use segmentation map, an accurate evaluation of the qualification of the target spoil disposal site can be achieved.

The invention relates to a land type identification method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring remote sensing image data and geographic information data of a to-be-identified area and a land type text rule of an identification target; extracting high-dimensional visual features of a ground object from the remote sensing image data; based on the high-dimensional visual features, generating a preliminary classification result graph and a visual feature vector; inputting the preliminary classification result graph, the visual feature vector, the geographic information data and the land class text rule into a multi-modal fusion model to obtain a land class recognition result; the multi-modal fusion model is obtained through training based on a real sample of a marked land class and a synthetic sample generated based on style migration and confrontation. Therefore, the values of different types of data can be deeply fused through the multi-modal fusion model, the problem that rare land class samples are insufficient is effectively solved by relying on real samples and synthetic sample training generated by style migration and confrontation, and the generalization recognition capability of the model on various land classes is improved.

The invention discloses a drainage pipe network catchment area classification method based on land utilization characteristics. The method comprises the following steps: determining a plurality of catchment areas and corresponding land types; calculating a replacement value of a zero value in the land type proportion according to the area size of the region and the land type; according to the land type proportion after replacement of each catchment area, calculating a weighted logarithmic transformation value corresponding to the land type; according to the weighted logarithm transformation values corresponding to different land types in each catchment area, a plurality of classification schemes are obtained through Ward hierarchical clustering grouping; and calculating a comprehensive score for each classification scheme, and determining an optimal classification scheme. Scientific classification is realized through adaptive zero value processing, weighted logarithmic transformation and verification optimization closed loop, and technical support is provided for accurate management of the drainage pipe network.

The invention discloses a DEM rapid updating method and system based on minimum elevation difference mosaic, and belongs to the field of DEM dynamic updating, and the method comprises the steps: obtaining and processing natural resource monitoring data and regional historical DEM data; dEM change areas are extracted based on land type change rules, and pattern spots meeting area requirements are screened to serve as to-be-updated areas; generating a point cloud through dense matching of aerial triangulation achievements of the aerial image, and making a new DEM of a change area through processing; plane matching of the new DEM and the historical DEM is achieved through a SiftGPU, Hough transform and a UMEYAMA algorithm; setting a buffer area for a change range, calculating an elevation difference weight, generating an optimal mosaic line by using a minimum cost path algorithm, and implementing two-stage DEM seamless mosaic through feathering; and verifying the updated DEM precision based on field actual measurement points. The method and the system do not need a large amount of manual editing, and can improve the DEM updating efficiency.

The application discloses a land type classification and labeling method based on AI, which comprises the following steps: collecting satellite remote sensing images, unmanned aerial vehicle aerial photographs, GIS geographic data and LiDAR point cloud data; constructing a high-dimensional feature matrix after pre-processing the collected multi-source data; training a basic model based on artificial labeling samples; generating pseudo-label samples and expanding the training set in combination with an active sampling strategy; realizing land type prediction through feature extraction, multi-scale fusion, attention enhancement and a Transformer classification unit in model training; evaluating precision based on an mIoU index; and manually reviewing low-precision areas and iteratively optimizing the model. The application trains a large model by using an artificial sample training model, automatically classifies and labels land types by combining multi-source data, improves work efficiency, reduces artificial consumption, and improves the accuracy of classification and labeling through multi-source data.