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 data, which has fostered a shift towards the use of automated systems to increase processing efficiency. However, these systems require manually annotated data on which to be trained, which is lacking. This dataset describes a total of 819 annotated and classified whale Features of Interest (FOIs) from a multi-season survey of Wilhelmina Bay on the Western Antarctic Peninsula (WAP). These data are comprised of FOIs that have been annotated and classified based on existing protocols by seven individual observers who scanned ~1,900 km2 of WorldView-03 imagery acquired between 2018/2019 and 2021/2022. This work was supported by an Innovation Voucher from the British Antarctic Survey and grants from the World Wildlife Fund (GB107301) and NC-International NERC (NE/T012439/1).

Monitoring whales in remote regions is important for their conservation, using traditional survey platforms (boat and plane) is logistically difficult. The use of very high-resolution satellite imagery to survey whales, particularly in remote regions, is gaining interest and momentum. However, development is hindered by the lack of automated systems to detect whales. Such a system requires an open source library containing examples of whales and confounding features in satellite imagery. Here we present such a database, created by surveying 6,300 km2 of satellite imagery in various regions across the globe, which allowed us to detect 633 whale objects. This dataset contains image chips as png files. Funding was provided from a BAS Innovation Voucher.

Monitoring whales in remote regions is important for their conservation, using traditional survey platforms (boat and plane) is logistically difficult. The use of very high-resolution satellite imagery to survey whales, particularly in remote regions, is gaining interest and momentum. However, development is hindered by the lack of automated systems to detect whales. Such a system requires an open source library containing examples of whales and confounding features in satellite imagery. Here we present such a database, created by surveying 6,300 km2 of satellite imagery in various regions across the globe, which allowed us to detect 633 whale objects and 120 confounding features. Funding was provided from a BAS Innovation Voucher.