Extracting road vector data from raster maps

Yao Yi Chiang, Craig A. Knoblock

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Raster maps are an important source of road information. Because of the overlapping map features (e.g., roads and text labels) and the varying image quality, extracting road vector data from raster maps usually requires significant user input to achieve accurate results. In this paper, we present an accurate road vectorization technique that minimizes user input by combining our previous work on extracting road pixels and road-intersection templates to extract accurate road vector data from raster maps. Our approach enables GIS applications to exploit the road information in raster maps for the areas where the road vector data are otherwise not easily accessible, such as the countries of the Middle East. We show that our approach requires minimal user input and achieves an average of 93.2% completeness and 95.6% correctness in an experiment using raster maps from various sources.

Original languageEnglish (US)
Title of host publicationGraphics Recognition
Subtitle of host publicationAchievements, Challenges, and Evolution - 8th International Workshop, GREC 2009, Selected Papers
Pages93-105
Number of pages13
DOIs
StatePublished - 2010
Externally publishedYes
Event8th IAPR Workshop on Graphics Recognition, GREC 2009 - La Rochelle, France
Duration: Jul 22 2009Jul 23 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6020 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th IAPR Workshop on Graphics Recognition, GREC 2009
Country/TerritoryFrance
CityLa Rochelle
Period7/22/097/23/09

Keywords

  • GIS
  • map processing
  • raster maps
  • road vectorization

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