European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry

Krzysztof Kotowski, Christoph Haskamp, Jacek Andrzejewski, Bogdan Ruszczak, Jakub Nalepa +6 more
6/25/2024
cs.LGcs.AI

No official code found. Best implementation: kplabs-pl/ESA-ADB (110 stars, MIT, last commit 2025-09-11).

Abstract

Machine learning has vast potential to improve anomaly detection in satellite telemetry which is a crucial task for spacecraft operations. This potential is currently hampered by a lack of comprehensible benchmarks for multivariate time series anomaly detection, especially for the challenging case of satellite telemetry. The European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry (ESA-ADB) aims to address this challenge and establish a new standard in the domain. It is a result of close cooperation between spacecraft operations engineers from the European Space Agency (ESA) and machine learning experts. The newly introduced ESA Anomalies Dataset contains annotated real-life telemetry from three different ESA missions, out of which two are included in ESA-ADB. Results of typical anomaly detection algorithms assessed in our novel hierarchical evaluation pipeline show that new approaches are necessary to address operators' needs. All elements of ESA-ADB are publicly available to ensure its full reproducibility.

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Code Implementations(1)

kplabs-pl/ESA-ADBCommunity100%
MIT

Code of the ESA Anomaly Detection Benchmark

11034R, HTMLJun 25, 20241 years agoMIT

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Cite this paper

@article{kotowski2024european,
  title  = {European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry},
  author = {Krzysztof Kotowski and Christoph Haskamp and Jacek Andrzejewski and Bogdan Ruszczak and Jakub Nalepa and Daniel Lakey and Peter Collins and Aybike Kolmas and Mauro Bartesaghi and Jose Martinez-Heras and Gabriele De Canio},
  year   = {2024},
  eprint = {2406.17826},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.17826v2}
}

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