Pattern recognition in road networks on the example of circular road detection

Authors:
Frauke Heinzle;Karl-Heinrich Anders;Monika Sester
Affiliations:
Institute of Cartography and Geoinformatics, University of Hannover, Hannover, Germany;Institute of Cartography and Geoinformatics, University of Hannover, Hannover, Germany;Institute of Cartography and Geoinformatics, University of Hannover, Hannover, Germany
Venue:
GIScience'06 Proceedings of the 4th international conference on Geographic Information Science
Year:
2006

Citing 6
Cited 2

Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Spatial information retrieval and geographical ontologies an overview of the SPIRIT project

SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Computer and Robot Vision

Computer and Robot Vision
Discovery of Spatial Association Rules in Geographic Information Databases

SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
A Structural Approach to the Model Generalization of an Urban Street Network

Geoinformatica
Data-driven matching of geospatial schemas

COSIT'05 Proceedings of the 2005 international conference on Spatial Information Theory

Recognition of island structures for map generalization

GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Semi-automatic interpretation of buildings and settlement areas in user-generated spatial data

Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paper will introduce into the subject of recognition of typical patterns in road networks. Especially we will describe the search for ring structures and its implementation in detail. Applications to detect these patterns and to use them for eliciting additional implicit knowledge in vector data are shown. We will familiarise the reader with different methods and approaches for the automatic detection of those patterns in vector data. The retrieval of implicit information in vector data can be very helpful for many tasks, ranging from generalisation of maps to the spatial analysis and enrichment of GIS data to make it searchable by search engines.