Ovid：一种用于OpenStreetMap中自动破坏检测的机器学习方法
Ovid: A Machine Learning Approach for Automated Vandalism Detection in OpenStreetMap
摘要：
OpenStreetMap是一个开放的全球地图数据的独特来源，越来越多地被现实世界的应用所采用。OpenStreetMap中的恶意破坏检测是关键的，并且由于数据集的大规模、贡献者的绝对数量、各种恶意破坏形式以及缺乏用于训练机器学习算法的注释数据而具有显著挑战性。本文介绍了Ovid -一种新的机器学习方法，用于OpenStreetMap中的破坏检测。Ovid依赖于一个神经网络架构，该架构采用多头注意力机制来有效地总结OpenStreetMap变更集中的破坏信息。为了便于自动破坏检测，我们引入了一组原始功能，捕获更改，用户和编辑信息。我们对真实世界的故意破坏数据的评估结果表明，所提出的Ovid方法在F1得分上比基线高出4.7个百分点。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
