14 results about "Urban land" patented technology

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.

This invention discloses a method for assessing urban land green efficiency based on cross-regional technology flow maps. The method acquires the technology input factors, technology output factors, and land input factors of the target city and its related cities through data collection and processing rules, forming a basic data set. Based on cross-regional technology association determination rules, it identifies the correspondence between different cities in terms of technology transfer, technology output, and technology absorption, generating a cross-regional technology flow map. Based on this map, it constructs a set of land green efficiency influencing factors. By introducing the impact of cross-regional technology flow into the correspondence between land input and output, it achieves a comprehensive assessment of the target city's land green efficiency. This invention can reflect the impact of cross-regional technology flow on urban land green utilization efficiency, improves the objectivity and comparability of assessment results, and is applicable to urban green development assessment and land resource management decision-making scenarios.

The application belongs to the technical field of deep learning and remote sensing image processing, and specifically discloses a city land use mapping method based on multi-modal data collaborative perception, which comprises the following steps: performing parcel division on high-resolution remote sensing images of a research area to obtain irregular city parcels and remote sensing spectral features thereof; setting street sampling points along a road network, extracting street perception features and extracting interest point semantic features; constructing a heterogeneous graph structure based on the street sampling points, the street perception features and the interest point semantic features falling into the same parcel, and then extracting parcel-level multi-source geographic features; inputting the remote sensing spectral features and the parcel-level multi-source geographic features into a full sparse topic model for semantic alignment and fusion to generate fused parcel feature representations; and performing classification based on the fused parcel feature representations to output city land use mapping results. The application can realize high-precision and high-robustness city land use recognition under the condition that multi-source data is unevenly distributed or sparse.

The application discloses a kind of urban land reserve scale prediction system and method, it is related to urban planning and land management technical field, the application is by collecting urban land reserve total, annual increase and consumption area, land structure distribution and planning target structure etc. Data, calculate the dynamic coefficient reflecting the dynamic change of reserve scale and the structure adaptation coefficient for measuring the adaptation degree of structure and historical target. After the dynamic coefficient and the structure adaptation coefficient are standardized, the comprehensive index is calculated by combining weight, according to which the annual adjustment ratio is determined, and then the benchmark forecast reserve is calculated. Combined with the historical actual adjustment ratio, generate the forecast reserve of conservative, benchmark and active three kinds of scenes, determine the final forecast reserve by comparing with the average value of history. The application comprehensively dynamic trend and structure adaptability, supports multi-scenario analysis, and can provide scientific, flexible decision-making basis for urban land reserve planning.

The application relates to the technical field of land monitoring, in particular to an intelligent urban land space change monitoring system based on data processing, which comprises a processor and a memory, the processor executes a computer program stored in the memory to realize the following steps: acquiring a suspected land change region, screening the suspected land change region according to pixel values of pixel points in the suspected land change region and each edge section of the suspected land change region, obtaining a shadow region and a land change region on a current shadow correction image, performing shadow correction on the shadow region according to a non-shadow region adjacent to the shadow region, obtaining a current target image, and re-performing region change judgment on the current target image to obtain a land non-change region and a land change region on the current target image. The application can improve the effect of monitoring and identifying urban land space changes.

This application discloses a method and apparatus for urban environmental thermal risk assessment and early warning based on multi-source data, relating to the field of data processing technology. The method includes: acquiring multi-source data of the study city in a target year; preprocessing the multi-source data to obtain a standardized multi-source dataset; generating a local climate zone map based on the standardized multi-source dataset, and constructing a future land use simulation model and a thermal risk assessment model; calculating the thermal risk index of different blocks in the local climate zone map and the thermal risk index under future urban land use change scenarios based on the thermal risk assessment model; dividing the study city's areas into corresponding thermal risk levels based on the thermal risk index, thereby generating a thermal risk level distribution map; and triggering a graded early warning mechanism based on a preset risk threshold and the thermal risk level distribution map. This application rapidly and completely identifies the spatial distribution of urban environmental thermal risks and provides thermal risk early warning through multi-source data, which can assist in the formulation of thermal mitigation strategies.

The application discloses a kind of urban public transport corridor design methods based on multi-objective optimization model, this method can fuse city land use POI (Point of Interest, point of interest) data, resident trip OD (Origin-Destination, origin-destination) data and corridor construction cost, to realize the line selection and level planning of multi-angle consideration city public transport corridor.The present application is based on multi-objective optimization theory, forms the mathematical model of public transport corridor design, compared with the experience judgment method and two-step clustering method widely used at present, the method can be based on existing traffic demand and future development trend, obtain more accurate and reasonable public transport corridor design result, greatly improve the efficiency of urban public transport corridor planning, reduce the time and cost of artificial design.

This invention provides a quantitative method for assessing the impact of urbanization expansion patterns on flood factors in small and medium-sized watersheds. This method is based on a coupling of three modules: quantification and clustering identification of urban expansion patterns, cross-validation mechanism of hydrological models and empirical formulas, and a pattern-process response analysis framework. This invention innovatively constructs a comprehensive quantitative index system for urban land use expansion patterns from three dimensions: level, rate, and pattern. By fully utilizing limited rainfall data and watershed geographic information, and through cross-validation using two methods based on different principles, it significantly improves the reliability and accuracy of flood parameter estimation in data-free areas.

The present disclosure provides an accurate inversion method and system for aboveground biomass of urban vegetations considering vegetation types. The method includes: based on different research regions, performing corresponding biomass calculation after plot sampling; preprocessing high-resolution optical data and space-borne photon counting LiDAR data, performing fine extraction on urban vegetation information, extracting spectral features of high-resolution remote-sensing image data and constructing an optical-biomass inversion sub-model, extracting vegetation height data of the space-borne photon counting LiDAR data and constructing a space-borne LiDAR-biomass inversion sub-model; with the support of the information on fine classification on the urban vegetation types, performing integration on the biomass inversion sub-models; based on the sample plot biomass data of the urban vegetations, obtaining an integrated inversion model fusing the horizontal information and the three-dimensional structure information of the urban land surface vegetations by training to perform accurate mapping of the aboveground biomass of the urban vegetations.

This invention discloses an online intelligent assessment system for urban land value based on big data analysis, comprising: a data normalization module for acquiring and preprocessing multi-source heterogeneous data; an association mapping module for performing temporal registration, using data components as graph nodes and calculating association weights to construct a value association graph; a propagation solution module for locating nodes to be assessed, iteratively updating access probabilities along node connections to obtain an association propagation state sequence; an evolution and recombination module for performing multi-scale time window rearrangement, elevating to a linear evolution space and recursively recombinating to obtain the value evolution state; an inversion assessment module for variational Bayesian inversion, filtering out posterior state components and retaining posterior parameters to obtain the value assessment result; and an incremental update module for acquiring feedback data and incrementally updating the value association graph. This invention enables online intelligent assessment of urban land value, improving the accuracy of value assessment.

This invention discloses an urban land cover classification system based on multispectral remote sensing technology, belonging to the field of spectral spatial data calculation technology. This system acquires land surface temperature data of the target area and sub-regions and calculates their rate of change differences. First, based on a comparison of the absolute value of the difference with a preset threshold, a first-level correction is performed on the initial contribution using a material thermal tuning coefficient. Then, based on the correlation coefficient between the temperature changes of the marked sub-region and adjacent regions, a second-level spatial coupling correction is performed, ultimately generating an accurate urban land surface thermal contribution report. This invention realizes a transformation from static identification to dynamic quantitative assessment of urban thermal environment contributions, significantly improving the accuracy, precision, and practical value of the assessment results. It solves the technical problem of insufficient accuracy in the spatial correlation dynamic assessment and adjustment of urban land surface classification thermal contributions.

This application provides a machine learning-driven method for analyzing the intensity mechanism of urban surface heat islands, integrating a local climate zone framework. The method includes: acquiring geospatial data of an urban agglomeration region; dividing the urban agglomeration region into multiple LCZ type units based on the geospatial data under the LCZ1-LCZ10 / LCZA-LCZG classification system to obtain LCZ classification results; determining SUHII based on the geospatial data and LCZ classification results to obtain SUHII spatial distribution data; extracting two-dimensional and three-dimensional urban morphology factors based on multi-source geospatial data; training an XGBoost model based on the SUHII spatial distribution data and urban morphology factors to obtain an optimized XGBoost model; performing a two-scale analysis on the optimized XGBoost model using the SHAP analysis method to obtain two-scale analysis results; and generating thermal environment regulation strategies for the entire urban agglomeration and various LCZ units based on the two-scale analysis results.

The application relates to the technical field of remote sensing image processing, and discloses a kind of urban ground cover change image intelligent detection method, system, terminal and storage medium.The method comprises the following steps: obtaining detection task configuration parameters, determining an interpretation model and to-be-detected remote sensing image data according to the detection task configuration parameters; using the interpretation model to perform inference analysis on multi-temporal remote sensing images to generate an initial change detection graph patch; generating a corrected change graph patch according to the user's correction operation on the initial change detection graph patch; storing the change graph patch and the associated original image data as new samples in a sample library; using the updated data in the sample library to iteratively optimize and train the interpretation model to obtain a target interpretation model, and using the target interpretation model to infer the to-be-detected remote sensing image data to output the urban ground cover change detection result.The application realizes dynamic matching of business requirements, interpretation models and to-be-detected remote sensing image data, and improves the efficiency of remote sensing image processing.

This invention discloses a method and system for predicting intercity passenger transport demand distribution based on the similarity of land use in adjacent cities. Specifically, the method involves: acquiring urban characteristic data; calculating the spatial similarity matrix L of adjacent urban land use based on the urban characteristic data; calculating the urban size index considering the similarity of land use in adjacent cities based on L and urban size index data; constructing an intercity passenger transport demand distribution model based on the similarity of land use in adjacent cities; solving for the model parameters; and then using the parameter-calibrated intercity passenger transport demand distribution prediction model, with the characteristic data of the city in the year to be predicted as the model input, to obtain its intercity passenger transport demand distribution. This invention significantly improves the prediction accuracy of intercity passenger transport demand distribution.