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

Autor:	Francisco Javier Ariza López, José Luis García Balboa
Rok vydání:	2007
Předmět:	Cartographic generalization Artificial neural network Computer science Generalization Geography Planning and Development Supervised learning computer.software_genre Set (abstract data type) Principal component analysis Line (geometry) Segmentation Data mining computer Information Systems
Zdroj:	GeoInformatica. 12:289-312
ISSN:	1573-7624 1384-6175
DOI:	10.1007/s10707-007-0026-z
Popis:	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%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::27b905f174fc1fc1b6e0aa6f8225bb16 https://doi.org/10.1007/s10707-007-0026-z Zobrazit plný text záznamu Full text from SpringerLink