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European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry

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arxiv 2406.17826 v2 pith:A2EWKD4E submitted 2024-06-25 cs.LG cs.AI

Krzysztof Kotowski , Christoph Haskamp , Jacek Andrzejewski , Bogdan Ruszczak , Jakub Nalepa , Daniel Lakey , Peter Collins , Aybike Kolmas
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Mauro Bartesaghi Jose Martinez-Heras Gabriele De Canio
This is my paper · ORCID
classification cs.LGcs.AI
keywords anomalydetectiontelemetrysatelliteagencyesa-adbeuropeanspace
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Forward citations

Cited by 4 Pith papers

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  1. Trojan Horse Hunt in Time Series Forecasting for Space Operations

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    A new Kaggle competition asks teams to reverse engineer 45 hidden triggers in poisoned satellite-telemetry forecasting models.

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    QLoRA-tuned LLM risk scoring for synthetic telecom/IoT fraud-control requests becomes much more usable than zero-shot prompting but mainly approaches, rather than outperforms, a lower-cost centralized ML ensemble unde...

  3. Toward Deployable Satellite Anomaly Detection: A Benchmark Study on Large-Scale ESA-ADB Telemetry

    cs.CE 2026-07 conditional novelty 4.0 of 10

    Supervised models (especially GAT and Multiscale CNN) outperform unsupervised detectors on ESA-ADB telemetry, yet unsupervised methods deliver competitive precision at far lower compute cost.

  4. CAPMix: Robust KPI Anomaly Detection for AIOps in Noisy and Dynamic Environments

    cs.LG 2025-09 conditional novelty 4.0 of 10

    CAPMix combines CutAddPaste anomaly injection, DTW-based label revision, and dual-space mixup to improve time-series anomaly detection, reporting gains over prior methods on five benchmarks.

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