Clues from the beaten path: Location estimation with bursty sequences of tourist photos

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PlaNet - Photo Geolocation with Convolutional Neural Networks (2016, background): “[6, 29] ﬁrst cluster the photo collection into landmarks and then learn to predict the sequence of landmarks in a query photo sequence.”
Supervised Text-based Geolocation Using Language Models on an Adaptive Grid (2012, methodology): “For tweet-level geolocation, it will be useful to build on work in geolocation that considers the temporal dimen-sion (Chen and Grauman, 2011; Kalogerakis et al., 2009; Sadilek et al., 2012) to make better predictions for documents/images that are surrounded by others with excellent cues, but which…”