摘要
地貌类型识别是多因素联合影响下的复杂决策问题.由于地貌区域环境的广泛性、差异性及地学要素作用的复杂性,简单地引入人工智能方法,通过典型样本监督学习并不能获得该问题的满意结果.因此,本文尝试将等高线形态知识这种测绘自然智能与人工智能结合,在地形形态表达规则和典型地貌类型样本训练联合驱动下,开展混合智能下黄土地貌类型识别研究,提出了整合等高线形态知识与带池化操作图神经网络(graph neural network,GNN)的地貌类型识别方法.本文方法将地貌单元的等高线建模为图结构,并将提取的等高线顶点的形态知识嵌入图节点中,采用带池化操作的GNN模型,挖掘图结构中的高层次特征和上下文信息,以识别地貌类型识别.试验结果证明了本文方法在黄土地貌类型识别上的有效性:在测试数据上获得了 86.1％的F1值,比两个对比方法高出3.0％～8.2％.
Abstract
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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