Ovid: A Machine Learning Approach for Automated Vandalism Detection in OpenStreetMap

TL;DR

This paper introduces Ovid, a neural network-based machine learning method utilizing multi-head attention to detect vandalism in OpenStreetMap, achieving superior accuracy over existing baselines.

Contribution

Ovid is the first to apply multi-head attention neural networks with novel features for vandalism detection in OpenStreetMap.

Findings

Ovid outperforms baselines by 4.7% in F1 score.

The method effectively captures vandalism signals from changeset, user, and edit data.

The approach demonstrates robustness on real-world vandalism data.

Abstract

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.