Embedding Geographic Locations for Modelling the Natural Environment using Flickr Tags and Structured Data
This work addresses the challenge of combining diverse data sources for ecological modeling, but it is incremental as it builds on prior insights about complementarity.
The paper tackled the problem of integrating Flickr tags and structured scientific data to model geographic locations, resulting in improved performance over existing approaches, particularly when structured information is present.
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.