A Graph Neural Network (GNN) Algorithm for Constructing the Evolution Process of Rural Settlement Morphology

• Published in: Security and communication networks Vol. 2022; pp. 1 - 10
• Main Authors: Hu, Zhe, Chen, Kexin, Xie, Xiaofei
• Format: Journal Article
• Language: English
• Published: London Hindawi 04.03.2022; John Wiley & Sons, Inc
• Subjects: Clustering; Evolutionary algorithms; Feature extraction; Geographic information systems; Geography; Graph neural networks; Landscape ecology; Monitoring; Morphology; Neural networks; Qualitative analysis; Quantitative analysis; Remote sensing; Research methodology; Social change; Statistical methods
• ISSN: 1939-0114; 1939-0122; 1939-0122
• DOI: 10.1155/2022/7517347

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.