  OpenStreetMap is a unique source of openly available worldwide map data,
increasingly adopted in real-world applications. Vandalism detection in
OpenStreetMap is critical and remarkably challenging due to the large scale of
the dataset, the sheer number of contributors, various vandalism forms, and the
lack of annotated data to train machine learning algorithms. This paper
presents Ovid - a novel machine learning method for vandalism detection in
OpenStreetMap. Ovid relies on a neural network architecture that adopts a
multi-head attention mechanism to effectively summarize information indicating
vandalism from OpenStreetMap changesets. To facilitate automated vandalism
detection, we introduce a set of original features that capture changeset,
user, and edit information. Our evaluation results on real-world vandalism data
demonstrate that the proposed Ovid method outperforms the baselines by 4.7
percentage points in F1 score.
