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LGM-PC: A tool for POI classification on QGIS

Published: 19 August 2019 Publication History

Abstract

In this demonstration, we present LGM-PC, a QGIS plugin for automatic recommendation of categories for new POIs. LGM-PC allows users to train classification models on individual areas of POIs and, then, use these models in order to classify new POIs into categories. The tool produces ranked category recommendations, based solely on the name of the POI, its coordinates and properties of its surrounding POIs, which are already annotated with categories. The user is then required to validate the produced recommendations, by selecting the most fitting category. Being implemented as a QGIS plugin, LGM-PC allows the visualization of POIs on map layers, for further assisting the user in the final category selection task.

References

[1]
Giorgos Giannopoulos, Nikos Karagiannakis, Dimitrios Skoutas, and Spiros Athanasiou. 2016. Learning to Classify Spatiotextual Entities in Maps. In Proceedings of the 13th International Conference on The Semantic Web. Latest Advances and New Domains - Volume 9678. Springer-Verlag, Berlin, Heidelberg, 539--555.
[2]
Musfira Jilani, Padraig Corcoran, and Michela Bertolotto. 2014. Automated Highway Tag Assessment of OpenStreetMap Road Networks. In Proceedings of the 22Nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL '14). ACM, New York, NY, USA, 449--452.
[3]
Nikos Karagiannakis, Giorgos Giannopoulos, Dimitrios Skoutas, and Spiros Athanasiou. 2015. OSMRec Tool for Automatic Recommendation of Categories on Spatial Entities in OpenStreetMap. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys '15). ACM, New York, NY, USA, 337--338.
[4]
Arnaud Vandecasteele and Rodolphe Devillers. 2015. Improving Volunteered Geographic Information Quality Using a Tag Recommender System: The Case of OpenStreetMap. 59--80.

Cited By

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  • (2022)Large-scale Vietnamese point-of-interest classification using weak labelingFrontiers in Artificial Intelligence10.3389/frai.2022.10205325Online publication date: 9-Dec-2022

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SSTD '19: Proceedings of the 16th International Symposium on Spatial and Temporal Databases
August 2019
245 pages
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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  • TU Wien: TU Wien

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 August 2019

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Author Tags

  1. Categorization
  2. Classification
  3. Feature Extraction
  4. ML
  5. POI
  6. Spatial

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  • Demonstration
  • Research
  • Refereed limited

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SSTD '19

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Cited By

View all
  • (2022)Large-scale Vietnamese point-of-interest classification using weak labelingFrontiers in Artificial Intelligence10.3389/frai.2022.10205325Online publication date: 9-Dec-2022

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