45 results about "Urban expansion" patented technology

The invention discloses an object-oriented building change detection method based on multi-feature fusion. The method includes: solving MBIs (morphological building indexes), texture features and SFA (slow feature analysis) graphs of image pixels; performing FNEA (fractal net evolution approach) splitting through the MBIs and the texture features; solving three feature values of each object, performing differencing, and solving a threshold by a K-means clustering algorithm to obtain a feature change graph; performing post-processing through AC indexes; solving weights of different feature change graphs by an entropy method, and setting thresholds according to the weights to obtain change images; performing post-processing by a voting method to obtain change detection results. The method has the advantages that building changes are detected through the MBIs, the SFA graphs and the texture features, the MBIs and the texture features are added to the FNEA splitting, the AC indexes are provided, and post-processing is performed by the voting method. A novel way for the application of high-resolution remote sensing images, land coverage and urban expansion is provided.

The invention discloses a method for simulating urban expansion based on the division of spatial functions. The method of the invention fully considers the widespread existence of obstacles in urban expansion, the existence of multi-space entities, and the different modes of action of each entity. Heterogeneity features reasonably divide urban space, define the spatial function of different morphological entities, and construct reasonable conversion rules for each region. The process of urban expansion can be more realistically simulated by adopting the method of the invention.

The invention discloses a multi-spectral remote sensing image change detection method based on a spectral reflectivity neighborhood difference chart and neighborhood probability fusion. The method mainly comprises the steps that 1, a registered two-time-phase multi-spectral image set at the same area is input and is subjected to Wiener filtering noise reduction and normalization processing; 2, the processed image set is converted into a relative ground object spectral reflectivity image set; 3, the spectral reflectivity change module value is calculated, and a spectral reflectivity change module value chart is obtained; 4, the spectral reflectivity angle value is calculated, and a spectral reflectivity angle value chart is obtained; 5, a neighborhood difference value of the spectral reflectivity is calculated and the neighborhood difference chart is obtained; 6, clustering is carried out on the spectral reflectivity change module value chart, the spectral reflectivity angle chart and the neighborhood difference chart, and a binary image is obtained; 7, the obtained binary image is fused based on the neighborhood probability, and a change detection result chart is obtained. Manual participation is not needed, the detection precision is high, and the method can be used for the fields such as urban expansion monitoring, forest and vegetation change monitoring and the like.

The invention relates to a city expansion multi-scenario simulation cellular automaton method based on a cross entropy optimizer, which comprises the following steps: 1) supervising and classifying satellite remote sensing images to obtain a land utilization classification map, and establishing spatial variable factor data; 2) acquiring effective sample points in the research area based on the spatial variable factor data; 3) establishing a CA city expansion simulation prototype model, and acquiring CA parameters based on the effective sample point data; 4) establishing a related objective function for optimizing CA parameters, and optimizing the CA parameters by using a cross entropy optimizer; 5) establishing a CA conversion rule, and obtaining a conversion probability graph; 6) establishing an urban expansion simulation CACEO model, and simulating and predicting urban expansion dynamics and future possible scenes; and 7) performing precision evaluation on the CACEO model and the simulation prediction result thereof, and outputting and storing the simulation result. Compared with the prior art, the method provided by the invention effectively optimizes the CA model and realizes multi-target city expansion scene prediction through objective weight determination.

The invention discloses a simulation method for urban expansion through modified cellular automaton. The method comprises the following steps of building an index system to evaluate urbanization development level and land utilization potential, and obtaining a land development potential map; based on the development potential map, sorting potential values of non-city picture elements from large to small, and recording the sorted values and corresponding picture element positions through an array; traversing each item of the array until finding the maximum value picture element meeting neighborhood influence, converting the picture element into an urban land type, and meanwhile, increasing the urban cellular quantity by one; updating the influences of all neighborhoods of the cellular, deleting the item from the array and breaking out of the loop, judging whether the cellular increase quantity meets the requirement or not, if yes, obtaining a simulation result, and if not, repeating the loop until the urban land increase quantity meets the requirement. The simulation method for urban expansion through the modified cellular automaton solves the difficult problem that the simulation result is not accurate due to nondeterminacy of a conversion threshold value of the traditional cellular automaton.

The invention discloses a polycentric city development boundary demarcating method. The method includes the following steps: calculating the economic output value, the ecological services value and the social security value of agricultural land resources of a city, establishing scarcity correction coefficients and a polycentric city attraction inertia index model to improve the three values of theagricultural land resources, conducting automatic interpolation and balanced judgment on the more scientific and reasonable economic output value, ecological services value and social security valueof the agricultural land resources as well as the price of construction land of the city through a computer, and finally demarcating an ideal boundary, a moderate boundary and a limit boundary for development of the polycentric city. Through comprehensive utilization of scarcity and attraction inertia model, scientific demarcation of common polycentric city development boundaries in the process ofcity expansion can be carried out in a more reasonable manner, breaking limitations of demarcation of monocentric city development boundaries in the prior art.

The invention relates to an urban expansion simulation prediction method integrating a firefly algorithm and a cellular automaton. The method comprises the steps: carrying out the supervision and classification of a remote sensing image, and obtaining an urban land utilization classification map; obtaining urban land utilization change driving factor data, and performing preprocessing; obtaining effective sample points of the land utilization map and the driving factor through a random stratified sampling method; determining boundaries of the parameters based on logistic regression, and training the effective sample points by using a firefly algorithm to obtain a conversion rule of the cellular automaton; obtaining an urban land utilization conversion probability according to a CA conversion rule; establishing a model based on the CAFFA; carrying out simulation application and verification analysis on urban land utilization by utilizing a CAFA model, and evaluating the precision; oUTPUTTING AND STORING SIMULATION RESULTS. Compared with the prior art, the method has higher simulation precision and better urban land utilization change simulation capability. Compared with the prior art, the method has the advantages of high simulation precision, high efficiency, good simulation effect, good universality and the like.

The invention relates to a method of identifying an urban function area integrated with population density and landscape structure. For the method of identifying an urban function area integrated with population density and landscape structure, the used data is mainly the village-level population statistical data and the land utilization data. The processing process of the method of identifying an urban function area integrated with population density and landscape structure includes the steps: a, selecting a typical road as an urban expansion axis and setting a sampling band and a quadrat; b, extracting the population density and land utilization type information of the quadrat; and c, integrating population density with landscape structure to identify an urban function area, such as an urban center area, a peripheral area and an outer suburban district. Based on the relation between urban land utilization and the landscape function, the method of identifying an urban function area integrated with population density and landscape structure can identify the urban function area by calculating the population density and an urban landscape index, thus being helpful for scientifically learning about the internal structure of a city, and promoting reasonable planning and functional configuration of the city.

The invention relates to an MCR city expansion simulation method with optimized ecosystem service value, an MCR minimum cumulative resistance model is applied to city expansion simulation, and city expansion simulation oriented by ecosystem service value optimization is realized through optimization of a traditional weight definite value and a scenario simulation method. The expansion model can simulate the urban expansion simulation scene under the optimization of the service value of the ecosystem according to objective data and a statistical method, so that the simulation result of the model can reappear, and meanwhile, the quantitative evaluation of the value optimization level can be carried out.

The invention discloses an urban built-up area intelligent extraction method supporting multi-source data. Low-cost data of different sources associated with city built-up area information is searched, normalization of point, line and surface data sources is realized through a kernel density estimation method, and unified classifier construction and evaluation are realized through a decision treealgorithm so that an effective classifier is obtained; the data can be used independently or in a combined mode, city built-up area information is extracted through a classifier, and therefore the space distribution of the city built-up area is estimated. According to the extraction method, the change information of the urban built-up area can be provided for urban planners and decision departments at an extremely low cost, so that reference information is provided for the urban planners and the decision departments to detect urban expansion and formulate urban planning.

The invention discloses a multi-agent urban expansion simulation method based on a land acquisition process competition result. The multi-agent urban expansion simulation method comprises the steps of firstly calculating the profits of four policies of competition between the government and peasant agents in the land acquisition process within each grid range according to gridded data quantification, determining whether the policy set has a Nash equilibrium solution, and if the Nash equilibrium solution is available, combining all the grids having the Nash equilibrium solution to form an urban expansion change optional region, and within the optional region, deciding which grids z in the optional region are finally turned to urban land from non-urban land according to the house-purchase site selection preference of the resident agents of different types. The multi-agent urban expansion simulation method based on the land acquisition process competition result has the advantages that the competition process of the government and the peasants in the land acquisition process is quantitatively expressed, the alternative region of urban expansion can be obtained accurately, and the final simulation result also indicates that the multi-agent urban expansion simulation method is greatly improved in accuracy in contrast with a traditional cellular automaton simulation method.

The invention discloses an urban impervious surface information extraction method fusing interest point big data. The method comprises the steps of firstly, introducing a remote sensing super-resolution mapping technology, and according to the first law of geography, acquiring impervious surface spatial correlation characteristic values of internal fine sub pixels from an impervious surface proportion value of each pixel; secondly, crawling interest point big data from a network map, and acquiring the percentage of the number of interest points of each sub pixel in the total number of interest points of the pixel; thirdly, fusing the information of each sub pixel in the above two steps; and finally, constructing a spatial optimization model using a remote sensing image and the interest point big data, and acquiring a fine accurate result of the internal impervious surface information of the pixel under the sub pixel scale, thus realizing accurate extraction of urban impervious surface information. The method has the characteristics of strong practicability, high simulation precision and the like, and is applicable to geo-scientific researches on urban expansion, urban heat land effect, urban water resource protection and the like.

The invention relates to a method for monitoring urban expansion by remote sensing and evaluating urban quality, which comprises the following steps: (1) atmospheric correction of remote sensing imagebased on 6S radiation transfer model; (2) geometric correction of remote sensing image based on Polynomial model; (3) seamless mosaic of remote sensing images based on histogram matching; (4) Remotesensing image subtraction processing based on band operation; (5) Spatio-temporal information extraction of cities and towns based on synthetic PII and artificial aids; (6) Establishment of urban quality reference factors based on Improved TOPSIS. The invention has the advantages that: 1) a complete, effective and scientific remote sensing monitoring and urban quality evaluation method for urban expansion is proposed; 2) the method is suitable for extraction, monitoring and quality evaluation of urban information for various regions; 3) the method provides data support and scientific basis fornew urbanization construction and planning; 4) the method enriches and perfects the relevant theories and methods of urban information management and decision support.

The invention discloses a centrality and aggregation measurement method based on a city expansion global structure. The method comprises the steps: providing a position centrality index (LCI), whereinthe index adopts an 'area inverse distance' weighting algorithm to obtain the position weight of a newly added urban patch relative to an original urban overall space structure, and capturing the dynamic characteristics of an urban expansion structure by utilizing the geometrical characteristics of a circle. The method further provides another index, namely a central aggregation index (LCAI), andthe index describes the evolution heterogeneity of the urban form by combining the attributes of the new plaques on the basis of considering the urban expansion global space structure. According to the method, the indexes are combined, the dynamic evaluation blank of the urban expansion centrality is filled up by clearly revealing the global structure characteristics, the performance of identifying the urban expansion aggregation heterogeneity is improved, the expansion characteristic type of the urban newly-added land space is clarified, and the urban expansion aggregation heterogeneity is improved. The practical application requirements of reflecting different city expansion characteristics from bottom to top are met.

The invention discloses a remote sensing image change detection method using a low-rank prior learning discrimination dictionary, which mainly improves the accuracy of unsupervised remote sensing image change detection and replaces automatic threshold selection with learning of a change discrimination dictionary and a non-change discrimination dictionary. The method comprises the following steps:(1) respectively inputting two multi-temporal remote sensing images to be detected; (2) performing super-pixel segmentation on the difference image to obtain a super-pixel total sample; (3) learning aglobal dictionary and a sparse coefficient for the superpixel total sample; (4) selecting samples according to low-rank representation of the sparse coefficient matrix; (5) respectively learning thedictionaries by using the changed samples and the invariable samples; and (6) calculating a reconstruction error of each pixel point neighborhood block under the discrimination dictionary, and comparing the reconstruction errors to obtain a change region binary image. Change samples and unchanged samples can be selected in an unsupervised manner for change detection, and the method can be appliedto the remote sensing fields of disaster assessment, urban expansion detection, land coverage, utilization detection and the like.

The invention discloses a protector for penetrating through a sensitive area pipeline, belonging to the technical fields of petrochemical industry and natural gas. As the economy develops, the urban expansion becomes a certainty, and the condition of crossing or short distance between the urban pipe network and oil pipeline can occur in the urban expansion process. The influence of the urban pipe network on the oil pipeline is not taken into consideration in the design of the oil pipeline at that time, and heat, vapor, salt ions and the like in the urban pipe network can generate severe influence on the oil pipeline and aggravate the corrosion of the pipeline, so people are required to take new protective measures on the original oil pipeline so as to lower the accident occurrence probability when the urban pipe network and oil pipeline are crossed.

The invention discloses an urban three-dimensional space expansion simulation method and device, and the method comprises the steps: predicting the development probabilities of various land utilization types on all pixels in a simulation region based on a trained development probability prediction model; calculating the total distribution probability of each land utilization type on each pixel according to the development probability of each land utilization type on each pixel, the conversion matrix between the land utilization types and the inertia coefficient of each land type; and determining the simulated land utilization type of each pixel according to the total distribution probability of each land utilization type on each pixel, and then predicting the building height on the pixel through a trained building height model when the simulated land type of the pixel is urban land. According to the method, the accuracy of city expansion simulation can be improved.

The invention provides a constrained cellular automaton coupling modeling method for space strategic planning. The method comprises the following steps: data input, based on urban growth simulation, household energy consumption and waste discharge accounting and household energy consumption and waste discharge of a constrained cellular automaton, in an HI-TAC model, interaction between families isbased on the following assumptions: all families have reached cooperative consensus to comply with total amount control related to energy consumption and waste discharge; according to different landutilization types, initial values of household energy consumption and waste discharge are set for the intelligent agent; and the household energy consumption and the waste discharge amount are calculated; and data output and presentation. According to the method, on the basis of considering macroscopic and unified conversion rules, a special conversion mechanism of microscopic individuals is considered, so that an urban expansion simulation result is closer to reality.

The invention discloses a zigzag island type platform of a parallel elevated road, and belongs to the technical field of urban traffic and urban planning. Urban diseases such as excessive aggregation,job-house separation and traffic jam are caused by guiding urban expansion by a subway; parallel elevated roads are used for guiding city expansion, and the parallel elevated roads are too wide and are difficult to construct in old urban areas. According to the zigzag island type platform, the zigzag island type platform of the parallel elevated road is divided into three sections, and the two ends of a middle zigzag section are connected with two straight line sections respectively. The parallel elevated road adopting the zigzag island type platform is narrow, line selection is facilitated,the demolition amount is small, the engineering amount is small, and the cost is low. A public transport annular channel is formed by parallel elevated roads, new and old urban areas are connected, cities are guided to expand, and an annular city is formed; the bus speed is high, the bus slow stop spacing is small, fast and slow buses are matched, the walking environment is good, and the public transport advantages are obvious. The annular city is large in gathering scale, good in resource sharing and occupational-residential balance effect, balanced in house price, beneficial to employment and obvious in energy conservation and emission reduction effect.

The embodiment of the invention discloses a citrus planting management method. The method is characterized by comprising the following steps that 1, a garden plot is selected, wherein sandy loam or loam which is high in terrain, sufficient in water source, loose in structure and rich in organic matter is selected for garden building, and it is guaranteed that the planting garden plot cannot be affected by pollution of industrial production and development of urban expansion; meanwhile, sunlight in the garden plot is good and sufficient, the climatic environment is pleasant and comfortable, the soil environmental conditions reflect good fertility, the uniform soil fertility is guaranteed, and the organic matter content and the pH value are proper; 2, water and fertilizer management is conducted; 3, pruning is conducted, wherein a young tree is pruned, the stem fixing height is kept to range from 25 cm to 45 cm, and meanwhile for total arrangement for main branches and secondary branches, 3-4 main branches are adopted, and the adult tree pruning mode is that pinching treatment is conducted in summer.

The invention provides an urban expansion detection method and a device based on a heterologous noctilucent remote sensing image. The urban expansion detection method comprises the following steps: inputting an LJ1-01 night light image, an ISS night light image and land cover data of a corresponding year of a research area; preprocessing the image data, wherein the preprocessing comprises geometric correction and reprojection; constructing a relative radiometric calibration model and iteratively solving, and simulating an ISS image with LJ1-01 image similar features; and constructing a featurespace combination, and carrying out region classification. According to the relative radiometric calibration model between the heterologous noctilucent remote sensing images and the resolving method,the problem of shortage of night light data with the resolution of 100 m can be solved. The feature combination for classification is constructed by adopting the data before and after change and theratio of the data before and after change, the urban expansion area can be more accurately and automatically classified, and the efficiency and accuracy are much higher than those of a manual mode.

The invention discloses a land utilization simulation and prediction method. The method comprises the following steps of collecting land utilization data, performing preprocessing to obtain an initial data set, and dividing the initial data set into a training set and a test set based on a sliding window technology; constructing a ConvLSTM-CA model on the basis of a ConvLSTM algorithm and a city expansion CA model, and training the ConvLSTM-CA model by taking the training set as input to obtain a target ConvLSTM-CA model; and calculating the conversion probability of the land utilization type according to the target ConvLSTM-CA model and the land utilization data, and based on a maximum probability conversion principle, obtaining a land utilization simulation prediction result. According to the prediction method, the utilization rate and the application value of the prediction network are greatly improved, and meanwhile, the ConvLSTM-CA model is adopted, so that the simulation effect is ensured, and meanwhile, the simulation precision is greatly improved.

The invention discloses a heat supply energy saving system for a hot press. The heat supply energy saving system comprises a steam turbine, a unit steam condenser, a hot well, a heat supply network steam condenser and the hot press; the hot well is provided with the unit steam condenser; the hot press is arranged between the steam turbine and the unit steam condenser and is connected with the heat supply network steam condenser; the heat supply network steam condenser is connected with the hot well; steam discharged by the hot press enters the heat supply network steam condenser through a pipeline; and condensed water of the heat supply network steam condenser is recycled by the hot well through a pipeline. By using the heat supply energy saving system, the cold side loss of a power plant is reduced to the maximum extent and is even completely eliminated, and meanwhile, the heat supply quantity can be multiplied at most; not only are the problems that high-quality steam is consumed and the coal consumption is increased when the heat supply energy saving system operates under a low-load variable working condition solved, but also the problem of heat supply capacity insufficiency caused by urban expansion is solved; heat in waste steam is recovered, the thermal efficiency of a unit is increased, and the coal consumption for power generation is reduced; and the heat supply energy saving system is safe and maintenance-free.

The invention relates to an urban expansion prediction method and system based on deep learning. According to the invention, urban impervious surface coverage data is combined with remote sensing dataand human activity statistical data; and utilizing the LSTM-RNN model to obtain the urban impervious surface coverage of the to-be-measured time period, and constructing a standard deviation ellipseof the city according to the urban impervious surface coverage of the to-be-measured time period to obtain an urban expansion prediction graph of the to-be-measured time period. Compared with the prior art, the method has the advantages that the LSTM-RNN model is used for selecting multiple input variables such as the impermeable surface coverage, the remote sensing data and the human activity statistical data, and the output prediction value is updated according to the correlation of each variable, so that the urban impermeable surface coverage prediction precision in the to-be-tested time period is improved.

The invention relates to an urban dynamic expansion simulation method based on partitions and an improved CNN-CA model, and belongs to the field of geographic information. The method comprises the following steps: S1, carrying out region division on a research region; s2, establishing an improved CNN model; and S3, coupling the improved CNN with the CA model. According to the method, an improved deep learning method is combined with a traditional CA model, the idea of ensemble learning is introduced, the neighborhood effect is fully considered, the city expansion simulation precision is high,and data support and reference are provided for decision makers in related fields.