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.

We present the Weddell Sea Benthic Dataset (WSBD), a computer vision-ready collection of high-resolution seafloor imagery and corresponding annotations designed to support automated analysis of Antarctic benthic communities. The dataset comprises 100 top-down images captured during RV Polarstern Expedition PS118 (cruises 69-1 and 69-6) in 2019, using the Ocean Floor Observation and Bathymetry System (OFOBS) in the Weddell Sea, Antarctica. A subset of this imagery was manually annotated by ecologists at the British Antarctic Survey (BAS) to support ecological analyses, including benthic community composition and species interaction studies. These annotations were subsequently standardised into 25 morphotypes to serve as class labels for object detection tasks. Bounding box annotations are provided in COCO format, alongside the training, validation, and test splits used during model development at BAS. This dataset provides a benchmark for developing and evaluating machine learning models aimed at enhancing biodiversity monitoring in Antarctic benthic environments. This work was funded by the UKRI Future Leaders Fellowship MR/W01002X/1 ''The past, present and future of unique cold-water benthic (sea floor) ecosystems in the Southern Ocean'' awarded to Rowan Whittle.

Model weights for the optimal object detection model, trained on the Weddell Sea Benthic Dataset. Trained 2025-05. Weights should be used with a Deformable-DETR architecture. This work was funded by the UKRI Future Leaders Fellowship MR/W01002X/1 ''The past, present and future of unique cold-water benthic (sea floor) ecosystems in the Southern Ocean'' awarded to Rowan Whittle.