Roll, D.S., Kurt, Z. orcid.org/0000-0003-3186-8091, Li, Y. et al. (2 more authors) (2025) Frigate: A novel dataset comprising wide field-of-view astronomical FITS images of Low Earth Orbit Scenes for machine learning applications. Scientific Data. ISSN: 2052-4463
Abstract
This report presents Frigate, a novel astronomical dataset comprising still-frame images of a section of Low Earth Orbit (LEO), collected by ExoAnalytic Solutions Inc., a commercial space situational awareness (SSA) provider using ground-based optical systems. Datasets of this kind are rare and typically inaccessible to most stakeholders. Through agreement with the company, the images were collected and prepared for use in machine learning and computer vision tasks related to SSA. The raw images contain lens glare artefacts, vignetting, and outer-region degradation. To address this, we additionally release a lightly processed version of each image featuring artefact removal, background subtraction, and sharpening. Usage notes describe optional downstream techniques including stacking, streak detection, and automated annotation. Given the lack of publicly available space surveillance data, this dataset represents a valuable resource for satellite tracking, orbital debris detection, and astronomical computer vision. The dataset described in this paper is wholly owned by ExoAnalytic Solutions Inc. and was released to the research team through mutual agreement of the benefits of its processing and application to the domain. All data and code are openly available.
Metadata
| Item Type: | Article |
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| Copyright, Publisher and Additional Information: | © The Author(s) 2025. Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Dates: |
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| Institution: | The University of Sheffield |
| Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Department of Journalism Studies (Sheffield) ?? Sheffield.IJC ?? |
| Date Deposited: | 01 Dec 2025 09:50 |
| Last Modified: | 01 Dec 2025 09:50 |
| Status: | Published online |
| Publisher: | Springer Science and Business Media LLC |
| Refereed: | Yes |
| Identification Number: | 10.1038/s41597-025-06220-0 |
| Related URLs: | |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:234985 |

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