论文标题
OVID：OpenStreetMap中自动故意破坏的机器学习方法
Ovid: A Machine Learning Approach for Automated Vandalism Detection in OpenStreetMap
论文作者
论文摘要
OpenStreetMap是全球地图数据的独特来源，在现实世界中越来越多地采用。由于数据集的大规模，贡献者的数量，各种故意破坏的形式以及缺乏带注释的数据来训练机器学习算法，因此OpenStreetMap中的故意检测至关重要，而且具有挑战性。本文介绍了Ovid-一种新型的机器学习方法，用于OpenStreetMap中的破坏性检测。 OVID依靠神经网络架构，该神经网络架构采用多头注意机制来有效地总结信息，指示OpenStreetMap更改中的故意破坏。为了促进自动故意破坏检测，我们引入了一组原始功能，以捕获更改，用户和编辑信息。我们对现实世界故意破坏数据的评估结果表明，所提出的OVID方法在F1分数中优于基准的4.7个百分点。
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.
