5 results about "Land-use planning" patented technology

This application belongs to the field of land use planning verification technology, and discloses a method, apparatus, storage medium, and equipment for land use planning verification. It involves acquiring a planning vector map and a color block planning map of the area to be verified; inputting the color block planning map into a convolutional neural network to extract color block features, obtaining a multi-scale feature map; inputting the multi-scale feature map into a pre-trained semantic segmentation network for color block segmentation, outputting color block regions; constructing a topological structure map of the area to be verified based on the color block regions; inputting the topological structure map and the planning vector map into a graph neural network to analyze topological relationships, and obtaining a state prediction result for each target node by aggregating the features of the target node itself and the features of its neighboring nodes. The state prediction results include compliance, attribute violation, spatial violation, or combined violation; integrating the state prediction results, and generating a verification report based on a rule engine. This can improve the efficiency of land use planning verification tasks and enhance the consistency and stability of verification results.

This invention relates to the field of machine learning technology, proposing a method and system for public participation planning in urban renewal using resident agent reward feedback reinforcement learning. The method includes: acquiring geospatial data of the city to be planned and constructing an adjacency topology graph model; constructing resident agents corresponding to different roles based on public participation corpora of the city to be planned using a large language model; generating land use adjustment actions based on the features of the adjacency topology graph model using a reinforcement learning policy network; inputting the land use adjustment actions into the resident agents and calculating resident satisfaction reward values ​​based on the evaluation results; iteratively training the reinforcement learning policy network with the goal of maximizing the resident satisfaction reward value; and outputting a land use planning scheme for the city to be planned through the trained reinforcement learning policy network. This invention achieves a real-time closed loop between public participation and planning optimization, significantly improving the public adaptability and implementation efficiency of urban renewal planning.

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

The application belongs to the technical field of land use planning and ecological environment protection, more specifically, relates to a land use scenario simulation method and system for ecological function improvement, through obtaining land use and driving factor data of a study area, dynamically evaluating habitat quality and analyzing spatio-temporal evolution characteristics; eliminating redundant collinear factors, accurately identifying key driving factors and their influence thresholds through multi-model coupling, solving the problems of core driving selection and quantitative constraint deficiency; then converting the thresholds into spatial ecological constraints, combining with regional policies to build differentiated land use scenarios, substituting into the calibrated spatial simulation model to predict future land use patterns; finally, calculating future habitat quality, positioning ecological risk areas, coupling ecological background grade to delimit control zones and develop differentiated management strategies, forming a complete optimization scheme; perfecting the connection between simulation results and land space governance, and forming a land use pattern optimization scheme for ecological function improvement.

This invention discloses a method for identifying and coordinating conflicts among multiple land use plans, comprising: collecting multi-dimensional data through a land use dynamic monitoring and analysis platform; identifying potential conflict areas and types using a spatiotemporal dynamic conflict early warning and simulation model; analyzing the conflict mechanism of spatial elements using a three-dimensional spatial element collaborative optimization algorithm; classifying conflict intensity levels using an ecological land use conflict quantitative assessment model; and formulating and simulating adjustments to a coordination and control scheme based on various parameters for identifying and coordinating conflicts among multiple land use plans. This invention, through multi-model collaborative linkage and a closed-loop design throughout the entire process, accurately captures the characteristics of multi-plan conflicts in the spatiotemporal dimensions, achieving a systematic and precise approach to conflict identification and coordination. It effectively solves the problems of identification bias and insufficient scheme adaptability in traditional methods, providing technical support for the scientific implementation of land use planning.