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

Content Provider	Springer Nature Link
Author	García Balboa, José Luis Ariza López, Francisco Javier
Copyright Year	2007
Abstract	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%.
Starting Page	289
Ending Page	312
Page Count	24
File Format	PDF
ISSN	13846175
Journal	GeoInformatica
Volume Number	12
Issue Number	3
e-ISSN	15737624
Language	English
Publisher	Springer US
Publisher Date	2007-08-17
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	artificial neural networks cartographic generalization knowledge acquisition line classification machine learning Multimedia Information Systems Information Storage and Retrieval Computer Science Data Structures, Cryptology and Information Theory Geographical Information Systems/Cartography
Content Type	Text
Resource Type	Article
Subject	Geography, Planning and Development Information Systems

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