{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/european-space-agency-benchmark-for-anomaly","title":"European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry","arxiv_id":"2406.17826","date":"2024-06-25","proceeding":null,"authors":["Krzysztof Kotowski","Christoph Haskamp","Jacek Andrzejewski","Bogdan Ruszczak","Jakub Nalepa","Daniel Lakey","Peter Collins","Aybike Kolmas","Mauro Bartesaghi","Jose Martinez-Heras","Gabriele De Canio"],"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.","url_abs":"https://arxiv.org/abs/2406.17826v1","url_pdf":"https://arxiv.org/pdf/2406.17826v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"european-space-agency-benchmark-for-anomaly","repo_url":"https://github.com/kplabs-pl/ESA-ADB","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series-anomaly-detection","task_name":"Time Series Anomaly Detection"}],"methods":[],"datasets_introduced":[{"slug":"esa-ad","name":"ESA-AD","full_name":"European Space Agency Dataset for Anomaly Detection in Satellite Telemetry"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}