论文标题
基于注意力的破坏性检测在OpenStreetMap中
Attention-Based Vandalism Detection in OpenStreetMap
论文作者
论文摘要
OpenStreetMap（OSM）是一个协作的众包网络地图，是全球公开地图数据的独特来源，在Web应用程序中越来越多地采用。故意破坏是支持信任和维持OSM透明度的关键任务。由于数据集的大规模，贡献者的数量，各种破坏性形式以及缺乏带注释的数据，因此这项任务非常具有挑战性。本文介绍了Ovid-一种基于OSM中故意破坏性检测的新型基于注意力的方法。 OVID依靠一种新型的神经结构，该神经结构采用多头注意机制来汇总信息，以有效地表明OSM变化的破坏性。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。此外，我们首次从OSM编辑历史记录中提取了现实世界中故意破坏事件的数据集，并将此数据集作为开放数据。我们对现实世界故意破坏数据进行的评估证明了OVID的有效性。
OpenStreetMap (OSM), a collaborative, crowdsourced Web map, is a unique source of openly available worldwide map data, increasingly adopted in Web applications. Vandalism detection is a critical task to support trust and maintain OSM transparency. This task is remarkably challenging due to the large scale of the dataset, the sheer number of contributors, various vandalism forms, and the lack of annotated data. This paper presents Ovid - a novel attention-based method for vandalism detection in OSM. Ovid relies on a novel neural architecture that adopts a multi-head attention mechanism to summarize information indicating vandalism from OSM changesets effectively. To facilitate automated vandalism detection, we introduce a set of original features that capture changeset, user, and edit information. Furthermore, we extract a dataset of real-world vandalism incidents from the OSM edit history for the first time and provide this dataset as open data. Our evaluation conducted on real-world vandalism data demonstrates the effectiveness of Ovid.
