Squidle+: a collaborative platform to manage, discover and annotate marine imagery

The rapid growth of marine imaging has outpaced our ability to efficiently analyse the imagery, creating challenges in data management, collaboration, and standardisation. This paper presents Squidle+, a web-based, collaborative platform for the end-to-end management, delivery, discovery, and annotation of marine imagery. Squidle+ provides a centralised portal and annotation repository while linking to imagery hosted on pre-existing cloud storage, eliminating data transfer and duplication. The system features a user-friendly interface with map-based exploration tools, advanced annotation workflows, and integrated analytics through a comprehensive API back-end. Collaboration is managed through user groups with granular permissions, while integrated QA/QC tools enable cross-validation between human annotators and Machine Learning (ML) algorithms. A key innovation is a framework to translate between multiple standardised or user-defined annotation vocabularies. This gives users the flexibility to construct data sets that target specific scientific questions and facilitates data reuse, cross-project syntheses, large-scale ML training, and broad summaries that can be fed into national-level reporting. Squidle+ has been developed in close collaboration with an active user community and currently contains datasets from several platforms and operators around the world. It is currently the largest known repository of openly accessible georeferenced marine images with associated annotations. Squidle+ streamlines complex workflows and significantly enhances the Findability, Accessibility, Interoperability, and Reusability (FAIR) of marine image data.

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Publication Details

Journal
Frontiers in Marine Science
Published
2026-01-30
DOI
https://doi.org/10.3389/fmars.2025.1677103
Primary Topic
Remote-Sensing Image Classification
Type
article
Field-Weighted Citation Impact
0.00
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article

Squidle+: a collaborative platform to manage, discover and annotate marine imagery

Jacquomo Monk, Elizabeth Oh, Dhugal J. Lindsay, Andrew Carroll et al.
Frontiers in Marine Science
Remote-Sensing Image Classification
article

Squidle+: a collaborative platform to manage, discover and annotate marine imagery

Jacquomo Monk, Elizabeth Oh, Dhugal J. Lindsay, Andrew Carroll, Ariell Friedman, Blair Thornton, Stefan B. Williams, Oscar Pizarro, Rachel Przeslawski
article en

Abstract

The rapid growth of marine imaging has outpaced our ability to efficiently analyse the imagery, creating challenges in data management, collaboration, and standardisation. This paper presents Squidle+, a web-based, collaborative platform for the end-to-end management, delivery, discovery, and annotation of marine imagery. Squidle+ provides a centralised portal and annotation repository while linking to imagery hosted on pre-existing cloud storage, eliminating data transfer and duplication. The system features a user-friendly interface with map-based exploration tools, advanced annotation workflows, and integrated analytics through a comprehensive API back-end. Collaboration is managed through user groups with granular permissions, while integrated QA/QC tools enable cross-validation between human annotators and Machine Learning (ML) algorithms. A key innovation is a framework to translate between multiple standardised or user-defined annotation vocabularies. This gives users the flexibility to construct data sets that target specific scientific questions and facilitates data reuse, cross-project syntheses, large-scale ML training, and broad summaries that can be fed into national-level reporting. Squidle+ has been developed in close collaboration with an active user community and currently contains datasets from several platforms and operators around the world. It is currently the largest known repository of openly accessible georeferenced marine images with associated annotations. Squidle+ streamlines complex workflows and significantly enhances the Findability, Accessibility, Interoperability, and Reusability (FAIR) of marine image data.

Frontiers in Marine ScienceVol. 12
The University of Sydney (AU), University of Tasmania (AU), Japan Agency for Marine-Earth Science and Technology (JP), Geoscience Australia (AU), Tokyo University of Science (JP), Norwegian University of Science and Technology (NO), Gretsa University (KE), Community Connections (US), Robotics Research (United States) (US), Australian Centre for Robotic Vision (AU), University of Southampton (GB), Advanced Institute of Industrial Technology (JP)
Openalex Percentile: Top 75%
Remote-Sensing Image Classification
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