Identification of loess landform types jointly affected by contour morpho-logical knowledge and the graph neural network
Landform type identification is a complex decision-making problem jointly affected by multi-factors.Due to the ex-tensiveness and differences of landform regional environments and the complexity of the roles of geological elements,it is not possible to obtain satisfactory results by simply introducing artificial intelligence(AI)methods and supervising learning through typical samples.Thus,this study tries to integrate the knowledge of contour morphology as the natural intelligence in surveying and mapping into AI technology and carries out the research on loess landform type identification by hybrid intelli-gence integrating landform sample training and landform morphological representation rules.This paper presents a landform type recognition method that integrates contour morphological knowledge with the graph neural network(GNN).In this meth-od,the contours of the landform unit are modeled as a graph structure composed of nodes and connecting edges,and the ex-tracted contour vertex morphology knowledge is embedded in the graph nodes.A GNN model with pooling operations is used to mine high-level features and context information in the graph structure to identify unit types.The experimental results dem-onstrate the effectiveness of the proposed approach in identifying loess landform types,achieving an F1 score of 86.1％on the test dataset,which represents a 3.0％～8.2％improvement over the two comparative methods.
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