# Attention-Based Vandalism Detection in OpenStreetMap

arXiv: https://arxiv.org/abs/2201.10406 · HTML: https://mlsanity.com/paper/2201.10406/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 25 Jan 2022.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing](https://mlsanity.com/paper/2007.06277/) (2020)
- [VEWS: A Wikipedia Vandal Early Warning System](https://mlsanity.com/paper/1507.01272/) (2015)
- [GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings on World Scale](https://mlsanity.com/paper/2108.13092/) (2021)
- [WorldKG: A World-Scale Geographic Knowledge Graph](https://mlsanity.com/paper/2109.10036/) (2021)
- [Linking OpenStreetMap with Knowledge Graphs -- Link Discovery for Schema-Agnostic Volunteered Geographic Information](https://mlsanity.com/paper/2011.05841/) (2020)
- [Towards Neural Schema Alignment for OpenStreetMap and Knowledge Graphs](https://mlsanity.com/paper/2107.13257/) (2021)
- [Defacing the map: Cartographic vandalism in the digital commons](https://mlsanity.com/paper/1404.3341/) (2014)

## Built on by (most cited first, 2 of 2)

- [Creating Knowledge Graphs for Geographic Data on the Web](https://mlsanity.com/paper/2302.08823/) (2023)
- [iWatchRoadv2: Pothole Detection, Geospatial Mapping, and Intelligent Road Governance](https://mlsanity.com/paper/2510.16375/) (2025)

## Abstract (arXiv)

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.