Abstract
Traditional statistical methods were mainly used to study the evolution process of rural settlement form and scale from a qualitative perspective, but it was difficult to quantitatively analyze the evolution process of the rural settlement form. Therefore, this paper proposed an intelligent monitoring method of rural settlement morphology evolution process based on the graph neural network (GNN) algorithm. Firstly, the specific working process of image feature extraction, analysis, and processing based on the graph neural network (GNN) algorithm was described. Secondly, combined with the change characteristics of rural settlement morphology evolution and scale development, the graphical neural network algorithm was used to effectively extract the morphological characteristics of rural settlements, and the monitoring information characterizing the dynamic changes of rural settlement morphology and scale was obtained through feature clustering. Finally, through experiments and using the graph neural network algorithm, the evolution process of rural settlement morphology was monitored in real time. The experimental results showed that the monitoring data obtained by this method were basically consistent with the actual statistical results, which showed that the intelligent monitoring method of the rural settlement form evolution process based on graph neural network algorithm can better reflect the dynamic change process of the rural settlement form and scale development. This study will provide some theoretical reference and guiding significance for the quantitative analysis of the evolution process of the rural settlement morphology and its influencing factors.
Highlights
Compared with plain areas and traditional agricultural areas, the shape and distribution of rural settlements in mountainous areas are complex and changeable [1]. erefore, it is of certain guiding significance for the rational guidance, regulation, and optimization of the scale of rural settlements to study the change process of temporal and spatial characteristics of rural settlements in mountainous areas and deeply explore the evolution law and influencing factors of rural settlements
From the above structure and function of graph neural network, it is known that when using the graph neural network model to process image features, it is mainly to optimize the convolution kernel parameters of the network model, and make it reach the best value through repeated intensive training. erefore, in this paper, polynomial expansion is applied to convolution kernel operation
Because the algorithm trains and learns many sample images through GNN network model, and repeatedly uses the new model to forward process the image samples of the test set until the ideal prediction image is output, the continuous optimization of graph neural network model is the guarantee of outputting the ideal result [19]
Summary
With the acceleration of urbanization, some rural settlements have developed rapidly; especially, the form, function, and scale of settlements have been greatly developed. Erefore, through the exploration of the evolution process of the rural settlement form, scholars at home and abroad have revealed the Security and Communication Networks relationship between the evolution of rural settlement spatial pattern and different regions and development backgrounds, which is one of the research hotspots of rural settlement. Some scholars conducted an in-depth research on the relationship between the temporal and spatial structure of rural settlements and the external environment [3, 4]. E development scale, form, and structure of the rural settlement can fully reflect the internal relationship between residents’ life and nature. Traditional methods are usually used to explore the development process of rural settlement evolution from a qualitative perspective, but cannot quantitatively reflect the rural settlement form and its dynamic changes
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