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Embedding Geographic Locations for Modelling the Natural Environment Using Flickr Tags and Structured Data

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Advances in Information Retrieval (ECIR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11437))

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Abstract

Meta-data from photo-sharing websites such as Flickr can be used to obtain rich bag-of-words descriptions of geographic locations, which have proven valuable, among others, for modelling and predicting ecological features. One important insight from previous work is that the descriptions obtained from Flickr tend to be complementary to the structured information that is available from traditional scientific resources. To better integrate these two diverse sources of information, in this paper we consider a method for learning vector space embeddings of geographic locations. We show experimentally that this method improves on existing approaches, especially in cases where structured information is available.

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Notes

  1. 1.

    http://www.flickr.com.

  2. 2.

    One exception is perhaps when we want to predict the scenicness of a given location, where e.g. terms that are related to professional landscape photography might be a strong indicator of scenicness.

  3. 3.

    http://www.eea.europa.eu/data-and-maps/data/eu-dem.

  4. 4.

    http://data.europa.eu/89h/jrc-luisa-europopmap06.

  5. 5.

    http://worldclim.org.

  6. 6.

    http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-2.

  7. 7.

    https://www.soilgrids.org.

  8. 8.

    http://ec.europa.eu/environment/nature/natura2000/index_en.htm.

  9. 9.

    http://scenic.mysociety.org/.

  10. 10.

    http://www.cs.cornell.edu/people/tj/svm_light/.

  11. 11.

    The EGEL source code is available online at https://github.com/shsabah84/EGEL-Model.git.

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Acknowledgments

Shelan Jeawak has been sponsored by HCED Iraq. Steven Schockaert has been supported by ERC Starting Grant 637277.

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Authors and Affiliations

  1. School of Computer Science and Informatics, Cardiff University, Cardiff, UK

    Shelan S. Jeawak, Christopher B. Jones & Steven Schockaert

  2. Department of Computer Science, Al-Nahrain University, Baghdad, Iraq

    Shelan S. Jeawak

Authors
  1. Shelan S. Jeawak
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  2. Christopher B. Jones
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  3. Steven Schockaert
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Corresponding author

Correspondence to Shelan S. Jeawak .

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Editors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Leif Azzopardi

  2. Bauhaus Universität Weimar, Weimar, Germany

    Benno Stein

  3. Universität Duisburg-Essen, Duisburg, Germany

    Norbert Fuhr

  4. GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany

    Philipp Mayr

  5. Delft University of Technology, Delft, The Netherlands

    Claudia Hauff

  6. University of Twente, Enschede, The Netherlands

    Djoerd Hiemstra

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Jeawak, S.S., Jones, C.B., Schockaert, S. (2019). Embedding Geographic Locations for Modelling the Natural Environment Using Flickr Tags and Structured Data. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11437. Springer, Cham. https://doi.org/10.1007/978-3-030-15712-8_4

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