164 results about "Urban land" patented technology

The invention relates to the technical field of geographic information systems, in particular to a GIS-based urban land planning analysis method and system, and aims to construct a multi-scale space-time cube model by integrating remote sensing, historical planning, mobile terminals and social media data, generate standardized urban temporary land data and improve the urban land planning analysis efficiency. Adaptive feature extraction is adopted to identify temporary land, a hierarchical classification system is established, abnormal changes are monitored through a time sequence model, an association diagram of the temporary land and an urban function network is constructed, an interaction relation is analyzed, finally, differentiated urban planning optimization suggestions are proposed, efficient configuration and management of urban land resources are assisted, and through fusion of multi-source data, the urban planning optimization suggestions are optimized. Multi-dimensional cognition of space, time, functions and relations is achieved, and the understanding depth of temporary land is fundamentally improved.

Provided is a system for simulating urban spatial growth by coupling urban development with water resources environmental carrying capacity, including a dynamic evaluation module for a water resources carrying capacity configured to evaluate and predict a maximum scale of urban space, a identification module for a water ecological sensitive area configured to identify water ecological security patterns and determine spatial regions that need to be avoided during urban spatial growth, and a simulation module for urban land use change configured to predict urban spatial layout features under different water ecological sensitive area protection modes and urban spatial growth scales. The system, as a whole, can predict the trends in urban population, industry, and construction land changes by simulating coupling between water resources environmental carrying capacity as well as water ecological sensitive area protection characteristics and factors such as urban socio-economic development and urban land expansion.

The invention provides a multi-modal data urban land utilization scene classification method based on street scene image matching remote sensing image, designs a cross-view image matching method based on geometric transformation, and realizes spatial position matching of a multi-view street scene image and a remote sensing image; the method comprises the following steps: constructing an urban land utilization scene classification model based on a high-resolution remote sensing image and a streetscape image by utilizing a pre-trained large model with relatively strong generalization ability, and extracting depth features of different scales and different visual angles; designing a global-local feature fusion module, and aggregating features of different levels through self-attention; a gate control cycle fusion module is designed to realize interaction of streetscape images at different shooting time and shooting view angles; through a channel attention mechanism, remote sensing image features and streetscape image features are distributed according to different weights, contribution proportions of the features in different classification tasks are optimized, a dynamic gradient modulation strategy is designed, and different modal information is adaptively fused, so that accurate classification of urban land utilization remote sensing images is realized.

The invention discloses an urban land subsidence analysis method and system based on multi-source data fusion, relates to the technical field of subsidence analysis, and constructs a multi-source three-dimensional monitoring network, performs air-space-ground multi-source data fusion analysis and atmospheric delay error correction, constructs a land subsidence prediction model, and provides an urban land subsidence analysis method and system based on multi-source data fusion. According to the method, a ground surface and underground three-dimensional monitoring network is constructed, the level, layered settlement and underground water data are integrated, minute-level global monitoring is achieved in combination with the Internet of Things, and the method is high in practicability and high in practicability. Through air-sky-ground data fusion and atmospheric correction, monitoring errors are reduced to a millimeter level, an evaluation system combining subjective and objective weights generates a five-level risk map, a visual platform supports dynamic simulation and factor traceability, a whole-process technical closed loop is formed, and the intelligent level of urban settlement prevention and control is remarkably improved.

The invention is suitable for the technical field of land subsidence prediction, and provides an urban land subsidence space-time fusion depth map model prediction method comprising the following steps: S1, data processing based on space-time characteristic analysis; and step S2, land subsidence prediction of the STF-GCLSTM model based on the grouping strategy. Multi-source data input is adopted, according to spatio-temporal feature extraction of the GCN spatial feature extraction model and the LSTM time feature extraction model, a more accurate settlement prediction value is finally obtained through the spatio-temporal feature fusion prediction model, and settlement feature differences of large and small regions are further considered. According to the method, a research area is divided into a plurality of sub-areas through a grouping strategy, so that the settlement trends of the sub-areas are similar, and then different sub-areas are modeled respectively, so that the fitting error of the model is reduced, and the prediction precision of the model is improved.

The invention discloses an urban street land fine division method and system based on deep learning, and belongs to the field of geographic information processing. The method comprises the following steps: firstly, collecting remote sensing image data, building space form data, business form POI data and urban land data of a case city district, extracting various kinds of information, converting the information into two-dimensional plane image format data, and carrying out rasterization and data enhancement processing; then training the land use division convolutional neural network deep learning model through a grid translation window method and a grid land use function judgment rule, performing rasterization function judgment on urban land use in a target area, dividing grid boundaries through a watershed method, and finally checking and correcting a result to obtain a target urban land use function judgment result. And obtaining a final result of land division of the target district. According to the method, the internal land of the urban land street can be finely divided, automatic division and function judgment of the urban land are realized, and digital and intelligent application conversion of urban planning work is promoted.

The invention provides a rail transit station classification method and device based on multi-source data, and the method comprises the steps: integrating urban land utilization and planning data, rail transit operation data, geographic information data, mobile phone signaling data and other multi-source information; a characteristic index system including five dimensions of passenger flow scale, resident demand, district development, station connection and peripheral facilities is constructed, and 17 characteristic indexes are extracted to comprehensively calibrate rail station characteristics. Then, analyzing the characteristic indexes by using a K-means clustering method, and accurately classifying the urban rail transit stations into seven categories, namely a core area comprehensive station, a living function leading station, a business office leading station, a transfer hub station, a suburban comprehensive station, a leisure tourism function station and a general station; therefore, fine division and scientific evaluation of functions of urban rail stations are realized.

The invention discloses an ai-based urban land function identification method and system, and belongs to the technical field of artificial intelligence data processing, and the method comprises the steps: obtaining a high-resolution remote sensing image, a dynamic population thermodynamic diagram and POI data of a target region, and constructing a multi-modal original data set; performing space-time alignment and characterization processing on the data set to generate standardized multi-modal data; weighting the remote sensing image based on the standardized data in combination with population thermodynamic and POI data to generate a plot function feature vector; classifying the feature vectors and outputting a preliminary land use function classification result; generating a credibility score based on the classification result; when the credibility is lower than a threshold value, analyzing the feature vector to generate land use function mixing proportion data; and integrating the classification result, the credibility score and the mixing proportion data to generate a land use function identification report. According to the method, multi-modal data fusion is adopted, and a function mixing degree dynamic analysis strategy is combined, so that high-precision identification of urban functional areas and quantitative analysis of mixed functional plots can be realized.

The invention discloses an urban land identification method based on a remote sensing image, relates to the technical field of urban land planning, solves the problem of insufficient refined features of an original identification mode, and locks core boundary pixels from a complex gradient pixel point set through pixel interval quantization, feature circle construction and refined point screening. The contour precision is obviously improved; compared with a traditional single-line contour, the refined contour can more completely represent the surface feature form, is especially suitable for irregular terrains, and reduces classification errors. According to the method, the pixel mean value and variance features are taken as measurement, the number of combination areas and the similarity of internal features are considered, the optimal combination is screened through weighted calculation, scattered division or excessive combination is avoided, the generated optimal combination areas highly meet the requirements of function aggregation and land cooperation in urban planning, and the planning efficiency and scientificity are remarkably improved.

The embodiment of the invention provides a traffic travel carbon emission space-time prediction method based on land function evolution modeling, and belongs to the technical field of data processing, and the method specifically comprises the steps: 1, constructing a multi-scenario land layout scenario constraint based on historical land function change data in a target region; step 2, utilizing the target model to predict urban land function space distribution of the target area under the constraint of the multi-scenario land layout scenario; 3, according to the urban land function space distribution, predicting the travel times of the passenger vehicles in each traffic zone, and according to the travel times, calculating the traffic travel distribution; and step 4, calculating the urban traffic travel carbon emission under the constraint of the multi-scenario land layout according to the traffic travel distribution. Through the scheme disclosed by the invention, the prediction efficiency, accuracy and adaptability are improved.

The invention relates to the technical field of big data, and provides a big-data-based urban planning and construction land zoning management and control system, which comprises a data acquisition module used for acquiring multi-dimensional data of urban planning and construction lands in real time; the feature extraction module is used for extracting key features from the multi-dimensional data; the dynamic partitioning module is used for constructing a dynamic land use partitioning model according to the key features and determining a dynamic partitioning index of each region based on the dynamic land use partitioning model; the conflict potential module is used for determining a conflict potential index between every two regions according to the dynamic partition indexes corresponding to every two regions of different land use types; the application optimization module is used for obtaining the optimized urban land; the zoning management and control module is used for zoning and managing and controlling the urban land again according to the new purpose corresponding to the optimized urban land; according to the invention, the accuracy, adaptability and sustainability of urban planning construction land zoning management and control can be improved.

The invention provides an efficient drainage strategy optimization method for a radially-distributed water system in a coastal shallow hill region. The method comprises the following steps: performing drainage layout on a target region by adopting different modes; carrying out flood drainage strategy selection; acquiring key data of a radially-distributed water system area in a coastal shallow hill area; performing real-time analysis, processing and evaluation on the key data; based on the key data analysis result, the drainage strategy is optimized and adjusted, and the optimal drainage scheme is compared and selected; and evaluating the optimized drainage strategy, dynamically adjusting a joint operation scheme under different boundary conditions, and applying the joint operation scheme to an equipment scheduling operation management system so as to improve the adaptability and efficiency of the drainage system to different scenes and external boundaries. The efficient flood drainage strategy and system capable of adapting to the coastal shallow hill area can provide effective support for improving the comprehensive benefit of flood control and drainage of the area, reducing the flood disaster risk, improving the utilization rate of urban land and reasonably carrying out flood drainage project layout and dispatching operation.

The present invention provides a GIS-based urban land planning analysis method, including: determining a temporary building land block and a temporary building land type of a target city; determining the geospatial change degree of the temporary building land block according to first historical geospatial data and second historical geospatial data of the temporary building land block; determining whether the temporary building land block is an abnormal land block according to a historical remote sensing image and a current remote sensing image of the target city at a second historical time node; and performing an association analysis on the temporary building land block and a land block corresponding to a land type having the same temporary building land type. By analyzing the current remote sensing image of the target city, the temporary building land block and the corresponding temporary building land type of the target city can be determined, and the attribute information of the temporary building land block marked as an abnormal land block can be used as an analysis reference for the same land type.

The invention relates to an urban land surface environment dynamic monitoring method and system fused with a remote sensing technology. The method comprises the following steps: acquiring an original data set of a target city; performing data cleaning and standardization processing on the original data set to generate a standardized urban space data set; segmenting a city and town development boundary according to the data set, dividing evaluation units and designing a multi-dimensional evaluation index system; on the basis of the evaluation index system, measuring and calculating each index and determining the weight, and obtaining a three-level evaluation grade for evaluating the spatial quality; and finally, on the basis of the three-level evaluation level, targeted quality improvement countermeasures are formulated according to the spatial quality characteristics of different areas. By adopting the method, the urban space quality difference can be accurately identified, a quantitative basis is provided for urban planning, resource allocation optimization, facility layout improvement, heat island effect alleviation and the like are assisted, refined treatment and sustainable development of urban space are promoted, and the life welfare of residents is improved.

The invention discloses a cellular automaton-based land utilization and urban element collaborative simulation method. The method comprises the following steps of: calculating the development probability of each element; the method comprises the following steps of: constructing a cellular automata (CA) model of each element; space collaborative simulation calculation; and evaluating the result precision. According to the method, the urban land use change simulation precision can be effectively improved, the land use change in reality can be better simulated, the simulation precision of population and economic elements can be improved, and the interaction and synergistic effect between different urban elements are effectively revealed. By means of multi-element collaborative simulation, small changes of urban development elements can be caught more finely, and the method has good perception ability for residential land, industrial land, commercial land, public management land and the like and has important significance for simulating urban collaborative development and reasonably planning urban layout.

The embodiment of the invention discloses a city shrinkage identification method based on time sequence population data change point detection, and relates to the technical field of geographic information, and the method comprises the steps: obtaining land utilization space raster data and population space raster data; respectively converting the land utilization space raster data and the population space raster data into matrixes; determining urban land use grids based on the land utilization space matrix, extracting population data from the population space matrix, constructing a one-dimensional population array, and performing smoothing processing to obtain smoothed time series data; identifying change trend abrupt change nodes of the population data based on the smoothed time sequence data, and segmenting each one-dimensional population array; and identifying a population data change trend of the segmented array, and determining a city contraction grid. According to the method, time periods are automatically divided by using change point detection, the starting and ending time of urban contraction can be more objectively identified, and data support can be provided for future urban land evolution simulation and regional development policy formulation.

The invention discloses a land utilization type fine-grained remote sensing interpretation method based on a self-supervised learning framework, and relates to the technical field of hyperspectral remote sensing image processing and geographic information analysis. The advantages of satellite and unmanned aerial vehicle images are combined, the spectral characteristic difference is dynamically balanced by using a double attention mechanism, the relevance between wave bands and labels is mined based on a self-supervised learning framework, and land utilization first-level global and second-level fine-grained interpretation is realized. According to the method, the feasibility of the multi-mode hyperspectral remote sensing image in urban land utilization monitoring is provided, and the defects of the traditional method in the aspects of strong and insufficient marking dependence, obvious same-spectrum foreign matter phenomenon, inaccurate secondary class interpretation and the like are overcome.

The invention relates to the technical field of land subsidence monitoring, and discloses an urban land subsidence monitoring device which comprises a water storage tank, a static force level gauge and a data collector, a water inlet pipe and a water return pipe are arranged on the two sides of the static force level gauge respectively, the water return pipe is communicated with the water storage tank, the water inlet pipe is communicated with a dynamic water tank, and the dynamic water tank is communicated with the static force level gauge. The water inlet pipe and the dynamic water tank are directly provided with one-way valves, liquid flows into the dynamic water tank from the water inlet pipe in a one-way mode, and the dynamic water tank is communicated with the water storage tank. According to the urban land subsidence monitoring device, through the dynamic water tank and the driving plug, liquid in the detection device flows in a one-way mode, bubbles newly generated in a pipeline or a static leveling instrument can flow out in time, the influence of the bubbles is reduced, and data are more reliable. Secondly, the dynamic water tank regularly injects liquid with a characteristic volume into the water storage tank, so that the liquid level of the water storage tank is regularly and specifically changed, pressure values in all the static force level gauges are conveniently compared, and abnormity is found out.

The invention discloses a low-efficiency land use redevelopment function prediction method based on a graph neural network, and the method comprises the steps: S1, obtaining the land use benefit of each space unit in a learning region, dividing each space unit into a high land use benefit type, a middle land use benefit type and a low land use benefit type, selecting a space unit with a high land utilization benefit type as an urban land use configuration case library; s2, taking the urban land use configuration case library obtained in the step S1 as a sample set, and constructing and training a functional feature learning model based on a graph neural network according to the sample set; and S3, obtaining each space unit in the prediction area, inputting the space units into the functional feature learning model obtained in the step S2, and performing low-efficiency land use redevelopment function prediction. By simulating urban space function interaction and mechanism innovation, a traditional land utilization prediction normal form based on static rules and spatio-temporal evolution is broken through, and scientific support is provided for land benefit improvement and function replacement decision in urban updating.

The invention provides a simulation design method of an urban ecological corridor, which comprises the following steps: S1, collecting a multi-temporal thermal infrared remote sensing image of a city to be simulated and designed and preprocessing to obtain a core heat island node; s2, heat resistance surface construction is carried out based on different surface types, a heat transfer resistance surface is obtained, and the different surface types comprise a digital elevation model, an impervious surface index, a normalized vegetation index, a normalized building index, a normalized water body index and a gradient; s3, taking the core heat island node as a network end point, searching a communication path with the lowest resistance on a heat transmission resistance surface, and obtaining an urban heat island corridor; and S4, on the basis of the urban land utilization data, ecological lands of forests, wetlands, large parks and water bodies serve as candidate ecological source lands, ecological connection paths are constructed from the angle of ecological connectivity by adopting the S2 and the S3, and the urban ecological corridor is obtained.

The invention discloses an urban territorial space dynamic monitoring and intelligent analysis system and method, and relates to the technical field of territorial space dynamic monitoring. The system comprises an initialization module used for constructing a lightweight learning framework, a dynamic modeling module used for establishing a dynamic network of geographical environment factors and a monitoring target based on parameters and a characteristic mode set output by the initialization module, and an optimization feedback module used for establishing a monitoring result feedback channel, and comparing the actual monitoring data set with a model prediction data set output based on the dynamic modeling module to calculate a prediction error. Through cooperative work of the three, the problem of weak model generalization ability caused by spatial heterogeneity of geographic phenomena is solved, and efficient and accurate monitoring and analysis of urban territorial spaces in different regions are realized.

The invention provides a carbon neutralization zoning simulation method, system and device based on a smart city, and relates to the technical field of carbon neutralization simulation, and the method mainly comprises the steps: carrying out the planning zoning of a map of a tested city; calculating the carbon emission of each partition and carrying out visualization processing to obtain geographic space data; calculating the carbon sink amount of each partition; collecting output value basic data of each industry of the detected partition and marking the output value basic data in geographic space data; carbon emission basic data, carbon sink basic data and output value basic data which are regularly acquired by a smart city system are stored in a smart city database based on a time sequence; predicting the future carbon emission, carbon sink and output value of the measured subarea; constructing a time sequence dynamic optimization model, and solving an industrial area layout scheme through a parallelization genetic algorithm; and generating suggestion information. According to the scheme, a quantifiable and adjustable carbon management tool is provided for overall planning of urban territorial space, and the scheme is particularly suitable for low-carbon planning and treatment of high-density coastal cities.

The present invention provides a method and device for determining urban land parcel evacuation identification information, which can be applied to the fields of artificial intelligence and emergency evacuation technology. The method includes: an identification agent obtains an action for laying out evacuation identification based on land parcel information and initial evacuation parameters; the action is executed in an environment to determine the layout and evacuation status of the evacuation identification; an initial strategy network obtains sample actions, sample evacuation identification layout results, sample evacuation status results, and sample distribution parameters based on sample land parcel information and sample initial evacuation parameters; an initial value network evaluates the sample actions, sample evacuation identification layout results, and sample evacuation status results based on an evaluation strategy to obtain an evaluation result; and based on the evaluation result, sample distribution parameters, and a loss function, the initial parameters of the initial strategy network and the initial value network are updated to obtain an identification agent. This method can reduce reliance on mathematical models and algorithms and reduce computing resource consumption.

The present application relates to a kind of carbon emission space refinement simulation and driving mechanism analysis method based on multi-source data and machine learning, including obtaining night light remote sensing, population density data, multi-source data such as interest point density, nonlinear spatial fitting model is constructed and trained by using information entropy weight method to carry out multi-source data fusion and construct economic energy comprehensive index, use city land use classification data as industry space restriction, combined with the total carbon emission of each industry calculated to build carbon emission space simulation base map, based on extreme gradient boosting tree, introduce SHAP framework for post-analysis, from global, local and spatial three scales, the nonlinear driving mechanism of carbon emission of multi-source city form element is analyzed, overcome the over-saturation defect of single night light data in city core area, realize the leap from pure space fitting to mechanism explanation, provide scientific basis and method support for high-density city accurate locking emission hotspot and making low-carbon policy.

The application discloses a kind of urban land remote sensing image classification methods based on evolution multitask optimization, comprising: 1: obtaining urban land cover remote sensing image sample and pre-processing, to build sample dataset;2: the sample dataset is decomposed into multiple training subsets, and the corresponding sub-population is constructed for each training subset;3: the state of sub-population is evaluated to adjust the probability of executing knowledge transfer operation online;4: different source populations are selected according to the state of sub-population to provide knowledge for it;5: more optimal classifier is obtained by using the model representing global information;6: select a part of poor performance classifier to carry out unbalanced adjustment;7: finally, the best individual is selected from the sub-population to form a multi-classifier, to identify different types of urban land use and cover situation.The application can quickly classify different urban land types by constructing and training the optimal multi-classifier, so as to improve the accuracy and precision of land type classification result.

The invention discloses an urban ventilation environment simulation and prediction method and system based on different planning objectives, and particularly relates to the field of urban ventilation environment simulation and prediction, and the method comprises the steps: obtaining ventilation characteristic data, land utilization data and regulation information data for many years as data sources; unifying the data sources to the same coordinate system and spatial resolution, and performing rasterization and numerical normalization processing; constructing a land utilization prediction model based on the PLUS based on the land utilization data and the regulation information data; and rasterizing the driving factors of the land utilization change. Regulation information is introduced in a land utilization prediction process, a dynamic land utilization conversion matrix is constructed, and a space-time diagram neural network and an entropy weight method are combined for calculation, so that joint modeling and quantitative evaluation of an urban land utilization pattern and a ventilation environment under a multi-level management and control condition are realized; the problem of systematic deviation between a simulation result and actual execution in the prior art is solved.

The present application relates to the technical field of geographic information system, and more particularly to a kind of city land planning analysis method and system based on GIS, by integrating remote sensing, historical planning, mobile terminal and social media data, construct multi-scale spatiotemporal cube model, generate standardized city temporary land data, adopt adaptive feature extraction to identify temporary land, establish hierarchical classification system, and monitor abnormal change through time series model, construct the association diagram of temporary land and city function network, analyze interactive relationship, finally propose differentiated city planning optimization suggestion, help city land resource efficient allocation and management, by fusing multi-source data, realize the multidimensional cognition of space, time, function and relationship, fundamentally improve the understanding depth of temporary land.

This invention discloses an urban land use classification method based on the fusion of multi-source data and deep learning, comprising: Step 1: Constructing a spatiotemporal map using Poisson disk sampling and graph neural networks, integrating the spatiotemporal heterogeneity of multi-source data, and dynamically adjusting weights using an attention mechanism; Step 2: Extracting static features from remote sensing and DEM using Swin Transformer, processing dynamic features from social media and mobile phone signaling using LSTM, fusing static and dynamic features through cross-modal attention, and optimizing data matching degree based on Grad-CAM++; Step 3: Combining the person-to-land flow of mobile phone signaling with the spatial correlation of social media activities, predicting trends using LSTM and embedding ecological indicator constraints; Step 4: Constructing a cross-departmental data sharing platform, collaboratively optimizing planning through a unified indicator system, and dynamically adjusting parameters using social media to mine public demands and POI data; Step 5: Introducing ecological footprint and carbon emission indicators to assess sustainability, establishing a monthly update mechanism, and dynamically calibrating model parameters through real-time data streams.

The present invention discloses a method for calculating pond water storage capacity based on dynamic grid sampling, which relates to the field of water resource monitoring technology. The method comprises: extracting pond water surface based on optical images and an improved normalized water index, calculating the pond area and number within a mission area; and using a grid sampling method to classify and sample the ponds; calculating the pond density and grid width based on the land area, pond area, and number within the mission area, dynamically generating a grid, and batch sampling the ponds using programming software; using big data analysis based on urban land space monitoring road network data, evaluating the quality of pond samples and replacing them, encoding the pond samples, and constructing a sample database using geographic information software; and constructing a water depth inversion model using programming software and a machine learning algorithm to obtain water depth inversion results. The water storage capacity of the ponds is calculated based on the pond area. The present invention can optimize the spatial coverage balance of samples.

The invention relates to an urban land utilization recognition method based on a BERT model text classification algorithm, and the method is characterized in that the method comprises the following steps: 1, determining an urban land utilization recognition type, determining an urban land utilization recognition region range, obtaining POI data in the region range, and carrying out the data screening; step 2, establishing an urban land utilization identification unit in an urban land utilization identification model based on a BERT model text classification algorithm, and associating the screened POI data with the urban land utilization identification unit; step 3, carrying out geographic text mining on the POI data; and step 4, performing urban land utilization identification of the urban land utilization identification model based on the BERT model text classification algorithm to obtain a high-precision urban land utilization identification result. According to the method, high-precision urban land utilization identification can be timely and accurately carried out by utilizing the available POI data.