Abstract

The recognition of structures is fundamental to map generalization, furnishing structural information that assists in choosing and parameterizing generalization operators. We specifically focus on the process of recognizing groups of small polygons in geological maps as a prerequisite to subsequent aggregation or typification operators. Proximity between polygons represents an essential criterion in identifying neighboring map objects. Here, network-based analysis is used for effective definition and refinement of candidate group members, applying criteria such as the distance between polygons, polygon size, shape, orientation, and feature attributes such as rock type. Starting off from the Delaunay triangulation of the polygon centroids, the global and local long edges, which initially define the network, are removed. The modified network is loaded with additional criteria, and edges are kept or removed based on the local similarities of the polygons they connect. This approach leads to more homogeneous, meaningful groups of polygon features in geological maps.

摘要

结构的识别是映射泛化的基础，提供有助于选择和参数化泛化算子的结构信息。我们特别关注在地质图中识别小多边形组的过程，作为后续聚合或典型化运算符的先决条件。多边形之间的接近度代表了识别相邻地图对象的基本标准。在这里，基于网络的分析用于有效定义和细化候选组成员，应用多边形之间的距离、多边形大小、形状、方向和岩石类型等特征属性等标准。从多边形质心的 Delaunay 三角剖分开始，最初定义网络的全局和局部长边被删除。修改后的网络加载了额外的标准，并根据它们连接的多边形的局部相似性保留或删除边缘。这种方法导致地质图中更均匀、更有意义的多边形特征组。