Publikation: Automatic Extrapolation of Missing Road Network Data in OpenStreetMap
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2015
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KATAKIS, Ioannis, ed. and others. Emerging learning paradigms and applications for smart cities : proceedings of the 2nd International Workshop on Mining Urban Data, MUD 2015, co-located with 32nd International Conference on Machine Learning (ICML 2015), Lille, France, July 11th, 2015. Aachen: RWTH Aachen, 2015, pp. 27-35. CEUR workshop proceedings. 1392. eISSN 1613-0073
Zusammenfassung
Road network data from OpenStreetMap (OSM) is the basis of various real-world applications such as fleet management or traffic flow estimation, and has become a standard dataset for research on route planning and related subjects. The quality of such applications and conclusiveness of research crucially relies on correctness and completeness of the underlying road network data. We introduce methods for automatic detection of gaps in the road network and extrapolation of missing street names by learning topological and semantic characteristics of road networks. Our experiments show that with the help of the learned data, the quality of the OSM road network data can indeed be improved.
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2nd International Workshop on Mining Urban Data, MUD 2015, 11. Juli 2015 - 11. Juli 2015, Lille, France
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FUNKE, Stefan, Robin SCHIRMEISTER, Sabine STORANDT, 2015. Automatic Extrapolation of Missing Road Network Data in OpenStreetMap. 2nd International Workshop on Mining Urban Data, MUD 2015. Lille, France, 11. Juli 2015 - 11. Juli 2015. In: KATAKIS, Ioannis, ed. and others. Emerging learning paradigms and applications for smart cities : proceedings of the 2nd International Workshop on Mining Urban Data, MUD 2015, co-located with 32nd International Conference on Machine Learning (ICML 2015), Lille, France, July 11th, 2015. Aachen: RWTH Aachen, 2015, pp. 27-35. CEUR workshop proceedings. 1392. eISSN 1613-0073BibTex
@inproceedings{Funke2015Autom-43820,
year={2015},
title={Automatic Extrapolation of Missing Road Network Data in OpenStreetMap},
url={http://ceur-ws.org/Vol-1392/paper-04.pdf},
number={1392},
publisher={RWTH Aachen},
address={Aachen},
series={CEUR workshop proceedings},
booktitle={Emerging learning paradigms and applications for smart cities : proceedings of the 2nd International Workshop on Mining Urban Data, MUD 2015, co-located with 32nd International Conference on Machine Learning (ICML 2015), Lille, France, July 11th, 2015},
pages={27--35},
editor={Katakis, Ioannis},
author={Funke, Stefan and Schirmeister, Robin and Storandt, Sabine}
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<dcterms:abstract xml:lang="eng">Road network data from OpenStreetMap (OSM) is the basis of various real-world applications such as fleet management or traffic flow estimation, and has become a standard dataset for research on route planning and related subjects. The quality of such applications and conclusiveness of research crucially relies on correctness and completeness of the underlying road network data. We introduce methods for automatic detection of gaps in the road network and extrapolation of missing street names by learning topological and semantic characteristics of road networks. Our experiments show that with the help of the learned data, the quality of the OSM road network data can indeed be improved.</dcterms:abstract>
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2018-11-14
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