Generalization-oriented road line segmentation by means of an artificial neural network applied over a moving window

  • Ariza López F
  • García Balboa J
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In line generalization, results depend very much on the characteristics of the line. For this reason it would be useful to obtain an automatic segmentation and enrichment of lines in order to apply to each section the best algorithm and the appropriate parameter. In this paper we present a methodology for applying a line-classifying backpropagation artificial neural network (BANN) for a line segmentation task. The procedure is based on the use of a moving window along the line to detect changes in the sinuosity and directionality of the line. A summary of the BANN design is presented, and a test is performed over a set of roads from a 1:25k scale map with a recommendation of the value of the parameters of the moving window. Segmentation results were assessed by an independent group of experts; a summary of the evaluation procedure is shown. © 2007 Elsevier Ltd. All rights reserved.

Author-supplied keywords

  • Artificial neural network
  • Cartographic generalization
  • Knowledge acquisition
  • Line segmentation
  • Machine learning

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