All Outputs (2)

DeepTEA: effective and efficient online time-dependent trajectory outlier detection (2022)
Journal Article
Han, X., Cheng, R., Ma, C., & Grubenmann, T. (2022). DeepTEA: effective and efficient online time-dependent trajectory outlier detection. Proceedings of the VLDB Endowment, 15(7), 1493-1505. https://doi.org/10.14778/3523210.3523225

In this paper, we study anomalous trajectory detection, which aims to extract abnormal movements of vehicles on the roads. This important problem, which facilitates understanding of traffic behavior and detection of taxi fraud, is challenging due to... Read More about DeepTEA: effective and efficient online time-dependent trajectory outlier detection.

Spatial concept learning and inference on geospatial polygon data (2022)
Journal Article
Westphal, P., Grubenmann, T., Collarana, D., Bin, S., Bühmann, L., & Lehmann, J. (2022). Spatial concept learning and inference on geospatial polygon data. Knowledge-Based Systems, 241, Article 108233. https://doi.org/10.1016/j.knosys.2022.108233

Geospatial knowledge has always been an essential driver for many societal aspects. This concerns in particular urban planning and urban growth management. To gain insights from geospatial data and guide decisions usually authoritative and open data... Read More about Spatial concept learning and inference on geospatial polygon data.