59 results about "Land type" patented technology

The invention discloses an urban land function identification and change detection method fusing remote sensing image and POI data. The method comprises the following steps of: extracting the land type based on a remote sensing image; identifying an urban functional areas based on the POI Data; fusing different scales of functional region recognition results; detecting the changes of the urban functional areas and analyzing the evolutionary trend. The method of the invention comprehensively considers the characteristics of the data from different sources, identifies the urban functional areasat different scales by fusing remote sensing images and the POI data, fuses the identification results, and realizes the accurate identification of the urban functional areas; and fully considers thePOI area weight, so that the mixed functional areas can be effectively identified. And through the multi-time series data, an evolution analysis model of the urban functional area is established, andthe spatial and temporal change law and trend of the urban land functions are excavated.

The invention provides a method of para position allocation and ,mixing cultivation of planting grass for afforestation on purple hills, which comprises the steps of: (1) division of land types: purple hilly lands are divided according to the acidity, basicity and lithology of the lands, soil erosion, soil thickness and terrain features; (2) selection of species of trees and grasses: species of trees and grasses which are applicable to grow in varied purple lands are selected according to the selection experiments of the different types of species of trees and grasses used for afforesting of purple hilly lands, and fitting degrees of the species of trees and grasses to each kind of land are specified; (3) para position allocation of the species of trees and grasses with the kinds of the lands: corresponding species of trees and grasses are allocated on the most suitable or relatively suitable lands; (4) mixed disposition of trees: different optimized disposition modes for purple hilly mixed forests are selected according to the mixed experiments of the trees for different kinds of lands of hills and hilly areas with purple soil, and trees and bushes, legumes and non-legumes, species of trees of deep root system and shallow root system, horizontally growing type and vertically growing type, soil fixing type and soil improved type are mixed. The method of the invention adopts trees, brushes and grasses to be planted on the most suitable and relatively suitable hills with purple soil, thus realizing precise para position allocation, high survival rate being up to over 90 percent and greatly accelerating the growing speed and forest establishment speed.

The invention discloses a method for estimating terrain by the polarization interference of the data of a synthetic aperture radar and a software thereof, which relate to synthetic aperture radars. The method comprises the following steps of: (A) filtering polarization interference data; (B) distinguishing different land types by utilizing a polarization classification technique; (C) dividing the data into the land types to form a plurality of simple subblocks; (D) estimating the terrain interference phase of each subblock respectively; (E) processing the terrain interference phase of each subblock in parallel and estimating the terrain of each subblock; and (F) combining the terrain of each subblock and calculating an integral terrain result. The method and the software introduce the polarization classification technique, divide the data into the land types to form a plurality of simple subblocks for processing, adopt different polarization interference methods according to the difference of the land types contained in the subblocks to estimate the terrain interference phases and then calculate the terrain of each subblock. The method not only lowers the difficulty of estimating the terrain of the complicated areas formed by the land types but also enhances the estimation efficiency of the terrain of this class of areas.

The invention provides a forest land classification method based on a remote sensing image. The forest land classification method is characterized by specifically comprising the steps of: selecting aforest land region to be classified to obtain data; preprocessing the remote sensing image; acquiring all forest land types in the remote sensing image; establishing a forest land second-level classification system suitable for the forest land region; matching coordinates of an on-the-spot investigation point positions of forest land types with the remote sensing image; performing computer automatic extraction on forest land classification information, and generating an automatically classified forest land classification map; performing manual visual correction on the forest land classification map; and determining precision of a classification result. The forest land classification method comprehensively utilizes the geometrical morphology and structural information of surface features, such as texture, shape, structure, spatial combination relationship and the like, gives consideration to more information such as structures and features, and improves the classification precision; andthe forest land classification method establishes the remote sensing oriented forest land second-level classification system, and ensures the classification consistency and result comparability of the forest land remote sensing and monitoring data; and the forest land classification method provided by the invention is convenient, precise and efficient, and realizes the rapid and precise monitoring of regional forest land resources.

The invention relates to a land type classification method based on multi-source territorial resource data. According to the method, multi-source data such as second national soil general survey, landutilization status survey, geographic national condition general survey and internet data are used, attribute indexes of landform types, soil types, land utilization types, land utilization intensityand the like reflecting land resource comprehensive characteristics are fused, a mesoscale land type classification system is constructed, and a classification result verification method system integrating typical verification and layered verification is provided. The invention aims to improve the efficiency and practicability of land type research, realize accurate classification of land resource comprehensive information, and meet the important strategic application requirements of national land resource investigation, geographic national condition general investigation and the like.

The invention provides a desertificated land remote sensing rapid identification method and belongs to the technical field of remote sensing monitoring. The method comprises the following steps: obtaining data and carrying out data pretreatment; extracting non-vegetation type information from the pre-processed data; carrying out processing on the extracted non-vegetation type information, including extracting water body and building land type information; extracting naked land type information; and analyzing and identifying desertificated land. The method is high in data source time series andhigh in time resolution, is short in monitoring period with one year being a cycle, and can master the development or reversal of the desertificated land every year.

The invention discloses a land occupation early warning method. The method comprises the following steps: acquiring a land remote sensing image in a target longitude and latitude range; inputting theremote sensing image into an image classification model to obtain a current land type corresponding to the remote sensing image; searching a basic land database by taking the target boundary longitudeand latitude information as an index to obtain a basic land type corresponding to the target boundary longitude and latitude information in the basic land database; comparing the current land type with a basic land type; and if the current land type is inconsistent with the basic land type, generating land occupation early warning information. The invention further discloses a land occupation early warning device and apparatus and a storage medium. According to the invention, the situation that the land is occupied and developed privately can be found in time, and the land resource occupationsituation can be conveniently supervised.

The invention relates to the technical field of map shrinking, in particular to an automatic land utilization data shrinking method, which comprises the following steps of: extracting a house building area and a water system from original data to perform independent shrinking, performing scale transformation through resampling and reclassification on the basis of a grid mode, performing pattern spot fragmentation processing, and obtaining a land utilization data matrix; according to the method, the data calculation amount can be remarkably reduced, the map shrinking efficiency can be remarkably improved, the semantic information of the land type pattern spots can be considered, the regional geographic features and the element relation of the pattern spots are kept correct, and the accuracy of the map spots is improved. And the high-quality land utilization map compilation requirement is met.

The invention provides a greenhouse gas accounting method and device, first electronic equipment and a storage medium, and the method comprises the steps: obtaining a plurality of land units in a preset range, and determining the land type of each land unit; receiving a data set which is sent by second electronic equipment and is required for accounting and corresponds to each land unit; and determining the greenhouse gas emission of the land unit according to the data set required for accounting corresponding to the land unit. According to the method, the urban greenhouse gas emission sourceand the absorption sink are located on the land units in the urban area, various land types respectively corresponding to each land unit are determined, then the types of the greenhouse gas emission source and the absorption sink of the land unit are determined, the greenhouse gas emission amount and the absorption amount of each land unit are calculated, and integration and management of information and data related to the greenhouse gas in a city range are realized.

The invention discloses a shared vehicle standard parking area division method which comprises the following steps of S1, determining a square unit window, and obtaining the vehicle suitable capacityin a square unit window; S2, dividing a city map to form a multi-level map; S3, obtaining the number of vehicles per day in each square unit window in the three-level map; S4, screening each square unit window in the three-level map according to the number of vehicles per day in each square unit window in the three-level map and a land type tolerance coefficient, and determining an optimal parkingwindow; and S5, determining a parking area according to the optimal parking window. According to the method, through the qualitative classification and the quantitative calculation of the three-levelmap, the space elements are divided according to the urban land standard, different tolerance coefficients are determined, the optimal parking area in each area can be better determined, the parkingarea is better scheduled and standardized, and a foundation is laid for the benign development of the shared vehicles.

The invention provides a spatial load distribution rule research method based on feature extraction, and the method comprises the steps: S1, generating a plurality of power utilization function cellsin a certain region according to the actual land use condition, and studying a power load, and obtaining a load sample; S2, extracting geographic feature information of each power utilization functioncell, giving a land type, and solving the load density of each power utilization function cell through the land type and the power load data; S3, taking the geographic feature information as input information, and clustering the power utilization function communities into N categories; S4, extracting load density distribution characteristics of the power utilization function communities of each category and each land type; and S5, obtaining a spatial load distribution rule of the region according to the load density distribution characteristics. The method has the advantages that the geographical characteristic difference of the power load and the characteristics of partitioning and classifying the power load are considered, the distribution of the space load is effectively described, a reliable basis can be provided for the planning and scheduling of the power distribution network, and the practicability is relatively high.

The invention relates to a reconstruction method of a wetland landscape pattern in a period without remote sensing data, and belongs to the field of wetland protection and recovery, solving the problem that historical wetland landscape spatial distribution data is difficult to obtain. The reconstruction method comprises the following steps: obtaining a land type statistical data set in the periodwithout remote sensing data in a to-be-reconstructed region; obtaining a suitability probability graph layer of a to-be-reconstructed region; reconstructing a wetland landscape pattern in a period without remote sensing data by utilizing topographic map data; comparing the reconstruction result with the record of the corresponding period in the acquired land type statistical data set, verifying the reconstruction result by using the suitability probability graph layer, and correcting the reconstruction result; determining peat in a to-be-reconstructed region as a research object; and obtainingthe deposition rate and flux record of each chemical element and pollutant in the deposition layer of the peat on a hundred-year scale, inverting the land utilization condition of the to-be-reconstructed region according to the record, comparing the land utilization condition with the corrected land utilization condition in the reconstruction result, and verifying whether the reconstruction result is accurate or not.

The invention discloses a land type pattern spot analysis method based on an intersection solution algorithm of two convex polygons, and relates to the technical field of land type pattern spot analysis. According to the land type analysis method, convex polygons are obtained through a method for converting boundary points into graphs or a method for analyzing shp files, intersection solving is conducted on the convex polygons in sequence, a common intersection of all the convex polygons is obtained, the obtained common intersection and the convex polygons in a database are overlapped, land type pattern spots and linear ground feature information intersected with the common intersection are obtained, and the land type pattern spots and the linear ground feature information are analyzed to obtain pattern spot areas under different land types. The land type pattern spot analysis method is simple and low in space and time complexity, land type pattern spot analysis can be achieved only by establishing one-time connection with the database, the operation speed is high, and robustness is high.

The invention provides a generalized load space prediction method and system, and the method comprises the steps: analyzing historical data of land type conversion, and obtaining the area change dataof each type of land in a prediction time period; performing prediction based on historical data of a distributed power supply, an electric vehicle and an energy storage device to obtain data of the distributed power supply, the electric vehicle and the energy storage device in a prediction time period; and based on the area change data of each type of land in the prediction time period and the data of the distributed power supply, the electric vehicle and the energy storage device, predicting by adopting a pre-established generalized load space prediction model to obtain a generalized load prediction value of each type of land. The influence of the distributed power supply, the electric vehicle and the energy storage in the urban intelligent power grid on the load density increase condition is considered, the accuracy of load space distribution prediction is improved, and effective data is provided for generalized load prediction of the intelligent power grid.

The invention relates to a method for ecological restoration of marginal land of the loess plateau by using an energy crop triarrhena lutarioriparia, which comprises the following steps: S1, collecting and collating a spatial distribution map of meteorological data in the loess plateau region in recent years and an existing land type map of the region in combination with a regional map meeting thegrowth conditions of the triarrhena lutarioriparia, using geographic information system software for map layers piling, map layers piling the mentioned three maps, and determining the region suitablefor ecological restoration by using energy crop triarrhena lutarioriparia; S2, dividing the suitable restoration region determined in the step S1 into three types of marginal land to be restored according to land types; and respectively carrying out ecological restoration on the three types of marginal land to be restored by adopting different triarrhena lutarioriparia planting methods. The method for ecological restoration of marginal land of the loess plateau by using energy crop triarrhena lutarioriparia has the advantages that, a suitable method for planting and restoring the triarrhena lutarioriparia is configured according to different types of marginal land of the loess plateau region and local conditions, and economic benefits are generated by the harvest of biological refining raw materials and biomass power plant raw materials on the ground every year, so that the economic benefits and the ecological benefits are both achieved.

The invention discloses a DS self-adaptive selection method in an InSAR time sequence analysis based on a land type. The DS self-adaptive selection method comprises the steps that in the process of the DS self-adaptive selection method in the InSAR time sequence analysis, the recognition of homogeneous pixels on a time sequence SAR image is carried out based on the land type, and then DS candidatepoints are selected; the land type is a land coverage type or a land utilization type; aiming at different land types, DS selection thresholds under different land types are self-adaptively calculated; and DS self-adaptive selection based on the land type is carried out. By adopting the self-adaptive selection technical scheme for performing the InSAR time sequence analysis on a DS target based on the land type provided by the invention, DS quality of self-adaptive selection can be improved; and an inspection search range is reduced, identification efficiency of homogeneous pixels is improved, and DS selection efficiency is further improved.

The invention discloses a method for converting surveying and mapping completion drawings into surveying delimitation drawings in batches, which comprises the following steps of: (1) acquiring all entity objects in a completion drawing working space, and analyzing completion drawing data; (2) for the text object, respectively replacing and adjusting according to the text content and the spatial position of the corresponding text in the survey delimitation graph; for a geometric object, extracting boundary lines of a project area and a fixed work area by utilizing a text matching and space logic judgment method, searching and deleting all land type elements in the project area, and performing symbolic expression on the boundary lines and an internal area by adopting a symbolic expression method; for the table object, creating an area statistical table by adopting a method of first positioning and then in-situ replacement; (3) storing the processed completion drawing file to the same path through a text replacement method; according to the method, the manual intervention degree of traditional surveying and mapping completion drawing conversion is reduced, and the working efficiency and quality of land reclamation are improved.

The embodiment of the invention provides a land area acquisition method and device and a storage medium, and relates to the technical field of map data; the method includes superposing the spatial analysis layer of the target area with the land type pattern spot layer corresponding to the target area to obtain an area superposing result; obtaining a corresponding ground class pattern spot superposition result and a linear ground object superposition result according to the region superposition result; obtaining an updated deduction relationship according to the linear ground object superposition result; and calculating and obtaining the actual area of the target area according to the ground class pattern spot superposition result, the linear ground object superposition result and the updated deduction relationship. According to the invention, the actual range of the linear ground features and the actual area of the ground pattern spots can be restored on the graph surface through the overlapping operation between the graph layers and the mode of updating the linear ground feature deduction relation, and the accuracy of area analysis is improved.

The invention relates to a land resource analysis model training method and analysis method based on image recognition, and the method comprises the steps: extracting the convolution features of to-be-detected aerial pictures with different downsampling multiples according to a feature extraction model after obtaining the to-be-detected aerial pictures of a to-be-analyzed region; converting each convolution feature into a corresponding multi-dimensional vector according to a vector conversion model; splicing the multi-dimensional vectors to obtain a to-be-tested feature vector, and obtaining a land type label corresponding to a to-be-analyzed area for analyzing land resources based on the land resource analysis model according to the matching degree of the to-be-tested feature vector and the reference feature vector. Based on this, through the convolution features of different downsampling multiples, the fine difference of the aerial photo to be detected can be distinguished, the anti-interference capability of image recognition is improved, and the accuracy of subsequent land type label output is improved.

The invention discloses a land development intensity control method and device, computer equipment and a storage medium. The method comprises the following steps: constructing a traffic demand prediction model for predicting a traffic demand state in a region; determining the plot ratio of the development plot according to the land type of the development plot, and calculating the bearing level of a preset dynamic and static comprehensive traffic mode under the current plot ratio in combination with the traffic demand prediction model; and when it is determined that the bearing level does not exceed the standard, progressively increasing and updating the current plot ratio according to the preset step length, returning the current plot ratio to the combined traffic demand prediction model, continuing to execute the step of calculating the bearing level of the preset dynamic and static comprehensive traffic mode under the current plot ratio, not stopping circulation unless it is determined that the bearing level exceeds the standard, and outputting the plot ratio of the development plot in the current cycle as the land development intensity index extreme value. By implementing the method, the land development intensity control efficiency can be improved.

The invention provides a deep learning technology-based field investigation method and system. The method comprises the steps of S101, obtaining a deep learning model and a proof photo obtained by training a deep learning platform based on a sample set; S102, obtaining an identification type result of the proof photograph through a deep learning model, judging whether the proof photograph of the same target pattern spot has feature consistency or not according to the identification type result of the proof photograph, if yes, executing S103, and if not, executing S104; S103, analyzing and judging by using a rule engine according to the identification type result of the proof photograph in combination with the current land utilization situation so as to obtain an investigation result of thetarget pattern spot; and S104, determining that the proof photograph does not have feature consistency. According to the method, the land type corresponding to the proof photo can be automatically recognized through the deep learning model, recognition is more accurate, repeated work is avoided by reminding the abnormal photo which does not meet the requirement, the survey result is obtained through the rule engine, the speed is high, and errors are not likely to happen.

The invention aims to provide a land utilization planning method considering multiple uncertainties, which comprises the following steps of: constructing a multi-target land utilization space optimization model under the multiple uncertainties, and solving the multi-target land utilization structure optimization model by utilizing an improved multi-target ant colony algorithm, obtaining land quantity structure prediction result elastic intervals of the research area in different planning periods and different land types under multiple uncertainty conditions, and then obtaining the distributionsuitability probability of each land type and setting an adaptive inertia coefficient, a neighborhood influence factor and conversion cost in combination with driving factor data and by utilizing a BP neural network; and combining the land quantity structure prediction result elastic interval of the research area with the distribution suitability probability, the adaptive inertia coefficient, theneighborhood influence factor and the conversion cost of each land class, and finally obtaining a future land utilization spatial pattern optimization result under multiple uncertain conditions. Anda new thought is provided for land utilization planning.

The invention relates to a real-time monitoring method and system for a personnel movement track in a land type natural conservation area. The method comprises the following steps: collecting the overall image data of an initial natural reserve domain, and setting the image data as the static image data of different functional regions according to a preset condition; collecting trajectory data ofa user entering the natural reserve at present in real time to form real-time natural reserve domain dynamic image data based on the trajectory data; and carrying out superposition analysis on the obtained dynamic image data and the static graphic data, determining real-time position information of the current user in a natural conservation region, and sending a corresponding region entry strategyto the user according to admission rule conditions of different functional regions. According to the method and the system, real-time pedestrian flow monitoring and control can be carried out on an accessible area and a forbidden area in a natural reserve, and the influence of excessive pedestrian flow on the natural reserve can be effectively prevented.