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Meta-data from photo-sharing websites such as Flickr can be used to obtainrich bag-of-words descriptions of geographic locations, which have provenvaluable, among others, for modelling and predicting ecological features. Oneimportant insight from previous work is that the descriptions obtained fromFlickr tend to be complementary to the structured information that is availablefrom traditional scientific resources. To better integrate these two diversesources of information, in this paper we consider a method for learning vectorspace embeddings of geographic locations. We show experimentally that thismethod improves on existing approaches, especially in cases where structuredinformation is available.

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