• Adrijana Car – Thomas Jekel – Josef Strobl – Gerald Griesebner (Eds.)

GI_Forum 2015, Volume 3

Journal for Geographic Information Science

Geospatial Minds for Society

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The Journal for Geographic Information Science issue 1-2015 presents peer-reviewed papers presented at the Geoinformatics Forum (www.gi-forum.org), held in Salzburg from July 7-10, 2015. The annual GI_Forum symposium provides a platform for dialogue among geospatial minds in an ongoing effort to support the creation of an informed GISociety.

In this issue, the following topics are addressed:
• GIScience Contributions for Society
• Learning with Geoinformation
• Earth Observation for Humanitarian Operations
• GI for Public Health
• Technical and Human Sensors
• Transportation Modelling
• Environmental GIS and Applications

Verlag der Österreichischen Akademie der Wissenschaften
Austrian Academy of Sciences Press
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Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400
https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at

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GI_Forum 2015, Volume 3

ISSN 2308-1708
Online Edition
ISBN 978-3-87907-558-4
Print Edition
ISBN 978-3-7001-7826-2
Online Edition



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Austrian Academy of Sciences Press
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Improving Navigation: Automated Name Extraction for Separately Mapped Pedestrian and Cycle Links

    Anita Graser, Markus Straub

GI_Forum 2015, Volume 3, pp. 546-556, 2015/06/29

Journal for Geographic Information Science

Geospatial Minds for Society

doi: 10.1553/giscience2015s546


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doi:10.1553/giscience2015s546


Abstract

Navigation instructions in pre- and on-trip routing services are usually based on street names and types, distances, and turn directions. However, in digital street graphs it is common that street names for separately mapped pedestrian and cycle links are missing. This leads to unsatisfactory instructions containing “unknown road” records. Often, these unnamed links run parallel to a named road, and it would be beneficial to use this information to generate instructions similar to “follow the sidewalk along Street A”, whereby “Street A” has to be determined by an algorithm. This paper introduces the Unnamed Link Naming Problem (ULNP) and presents a new approach to automatically extract suitable names to describe separately mapped pedestrian and cycle links. The approach has been tested using OpenStreetMap data and manually generated ground truth data for the second district of the city of Vienna, Austria. Results show that our best method achieves 90.7% correct matches in this challenging setting.