Features of interest from a multi-season satellite survey of baleen whales on the West Antarctic Peninsula

The application of very high-resolution satellite imagery for the purpose of studying wildlife, particularly in remote regions, has gained significant traction in recent years. With this, there has been an exponential increase in the volume of satellite data collected, which has fostered a shift towards the use of automated systems to increase processing efficiency. However, these automated systems require manually annotated data on which to be trained, which is lacking due to the time required to manually annotate satellite imagery and the lack of published records to collaboratively build large enough training datasets. Here, we present a dataset that describes a total of 819 annotated and classified Features of Interest (FOIs) from a multi-season baleen whale-focussed survey of Wilhelmina Bay on the Western Antarctic Peninsula. These data are comprised of FOIs that have been annotated and classified based on existing protocols by seven individual observers who scanned ~1,900 km<sup>2</sup> of WorldView-3 imagery acquired between 2018 and 2022 to expedite the creation of training datasets for automated detection models.

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Publication Details

Journal
Scientific Data
Published
2025-12-31
DOI
https://doi.org/10.1038/s41597-025-06463-x
Primary Topic
Marine animal studies overview
Type
article
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article

Features of interest from a multi-season satellite survey of baleen whales on the West Antarctic Peninsula

J. A. Jackson, G. Macfarlane, N. Kelly, H. Snead et al.
Scientific Data
Marine animal studies overview
article

Features of interest from a multi-season satellite survey of baleen whales on the West Antarctic Peninsula

J. A. Jackson, G. Macfarlane, N. Kelly, H. Snead, P. J. Clarke, G. Perry, L. Fouda, E. Longden, C. C. G. Bamford, M. Weyn, H. Cubaynes
article en

Abstract

The application of very high-resolution satellite imagery for the purpose of studying wildlife, particularly in remote regions, has gained significant traction in recent years. With this, there has been an exponential increase in the volume of satellite data collected, which has fostered a shift towards the use of automated systems to increase processing efficiency. However, these automated systems require manually annotated data on which to be trained, which is lacking due to the time required to manually annotate satellite imagery and the lack of published records to collaboratively build large enough training datasets. Here, we present a dataset that describes a total of 819 annotated and classified Features of Interest (FOIs) from a multi-season baleen whale-focussed survey of Wilhelmina Bay on the Western Antarctic Peninsula. These data are comprised of FOIs that have been annotated and classified based on existing protocols by seven individual observers who scanned ~1,900 km<sup>2</sup> of WorldView-3 imagery acquired between 2018 and 2022 to expedite the creation of training datasets for automated detection models.

Scientific DataVol. 13(1)
Andrews University (US), University of New Brunswick (CA), British Antarctic Survey (GB), Australian Antarctic Division (AU), Royal Society for the Protection of Birds (GB), University of St Andrews (GB), Simon Fraser University (CA), University of Évora (PT), Madeira Tecnopolo (PT), Australian Government (AU), Universidade da Madeira (PT), University of Edinburgh (GB)
Openalex Percentile: Top 28%
Marine animal studies overview
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Features of interest from a multi-season satellite survey of baleen whales on the West Antarctic Peninsula — J. A. Jackson, G. Macfarlane, et al. · Scientific Data (2025) | TGRS Research Map | TGRS