%0 Conference Paper %1 gis/TempelmeierD21 %A Tempelmeier, Nicolas %A Demidova, Elena %B Proc. of the 29th International Conference on Advances in Geographic Information Systems, SIGSPATIAL'21 %D 2021 %I ACM %K demand myown smashhit tempelmeier worldkg %P 415--418 %R 10.1145/3474717.3484204 %T Ovid: A Machine Learning Approach for Automated Vandalism Detection in OpenStreetMap %U https://doi.org/10.1145/3474717.3484204 %X 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 accuracy.

@inproceedings{gis/TempelmeierD21, 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 accuracy.}, added-at = {2022-02-27T14:03:17.000+0100}, author = {Tempelmeier, Nicolas and Demidova, Elena}, biburl = {https://www.bibsonomy.org/bibtex/2054ab4feb4181219f484b3c7ab42ac6a/demidova}, booktitle = {Proc. of the 29th International Conference on Advances in Geographic Information Systems, SIGSPATIAL'21}, doi = {10.1145/3474717.3484204}, interhash = {7015b22b5830a7b0e49e8aef8c1a2d98}, intrahash = {054ab4feb4181219f484b3c7ab42ac6a}, keywords = {demand myown smashhit tempelmeier worldkg}, pages = {415--418}, publisher = {{ACM}}, timestamp = {2022-02-27T14:03:17.000+0100}, title = {Ovid: A Machine Learning Approach for Automated Vandalism Detection in OpenStreetMap}, url = {https://doi.org/10.1145/3474717.3484204}, year = 2021 }