DeepStay: Stay Region Extraction from Location Trajectories using Weak
  Supervision
Christian Löwens Daniela Thyssens Emma Andersson Christina Jenkins Lars Schmidt-Thieme
Nowadays, mobile devices enable constant tracking of the user's position and
location trajectories can be used to infer personal points of interest (POIs)
like homes, workplaces, or stores. A common way to extract POIs is to first
identify spatio-temporal regions where a user spends a significant amount of
time, known as stay regions (SRs).
  Common approaches to SR extraction are evaluated either solely unsupervised
or on a small-scale private dataset, as popular public datasets are unlabeled.
Most of these methods rely on hand-crafted features or thresholds and do not
learn beyond hyperparameter optimization. Therefore, we propose a weakly and
self-supervised transformer-based model called DeepStay, which is trained on
location trajectories to predict stay regions. To the best of our knowledge,
this is the first approach based on deep learning and the first approach that
is evaluated on a public, labeled dataset. Our SR extraction method outperforms
state-of-the-art methods. In addition, we conducted a limited experiment on the
task of transportation mode detection from GPS trajectories using the same
architecture and achieved significantly higher scores than the
state-of-the-art. Our code is available at
https://github.com/christianll9/deepstay.