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Generalization-oriented Road Line Classification by Means of an Artificial Neural Network

机译：人工神经网络的广义道路线分类

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摘要

In line generalization, a first goal to achieve is the classification of features previous to the selection of processes and parameters. A feed forward backpropagation artificial neural network (ANN) is designed for classifying a set of road lines through a supervised learning process, attempting to emulate a classification performed by a human expert for cartographic generalization purposes. The main steps of the process are presented in this paper: (a) experimental data selection; (b) segmentation of lines into homogeneous sections, (c) sections enrichment through a set of quantitative measures derived from a principal component analysis, and qualitative information derived from road network and road type; (d) expert classification of the sections; and finally (e) the ANN design, training and validation. The quality of results is analyzed by means of error matrices after a cross-validation process giving a goodness, or percentage of agreement, over 83%.
机译：在线概括中，要实现的首要目标是在选择过程和参数之前对特征进行分类。前馈反向传播人工神经网络（ANN）设计用于通过监督学习过程对一组道路线进行分类，试图模拟人类专家为制图一般化而进行的分类。本文介绍了该过程的主要步骤：（a）实验数据的选择； （b）将线划分为同质路段；（c）通过一系列从主成分分析得出的定量措施以及从路网和道路类型得出的定性信息来丰富路段； （d）各节的专家分类；最后（e）人工神经网络的设计，培训和验证。经过交叉验证过程后，通过误差矩阵分析结果的质量，得出的优良率或协议百分比超过83％。

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