A fully-annotated imagery dataset of sublittoral benthic species in Svalbard, Arctic /

• Published in: Data in Brief
• Main Authors: Šiaulys, Andrius, Vaičiukynas, Evaldas, Medelytė, Saulė, Olenin, Sergej, Šaškov, Aleksej, Buškus, Kazimieras, Verikas, Antanas
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2021
• Subjects: underwater imagery; mosaicking; ROV; drop-down camera; machine vision; image segmentation; semantic segmentation; Arctic; Svalbard; Svalbard Archipelago; Climate change; Spitsbergen
• Online Access: https://vb.ktu.edu/KTU:ELABAPDB83384345&prefLang=en_US

Underwater imagery is widely used for a variety of applications in marine biology and environmental sciences, such as classification and mapping of seabed habitats, marine environment monitoring and impact assessment, biogeographic reconstructions in the context of climate change, etc. This approach is relatively simple and cost-effective, allowing the rapid collection of large amounts of data. However, due to the laborious and time-consuming manual analysis procedure, only a small part of the information stored in the archives of underwater images is retrieved. Emerging novel deep learning methods open up the opportunity for more effective, accurate and rapid analysis of seabed images than ever before. We present annotated images of the bottom macrofauna obtained from underwater video recorded in Spitsbergen island's European Arctic waters, Svalbard Archipelago. Our videos were filmed in both the photic and aphotic zones of polar waters, often influenced by melting glaciers. We used artificial lighting and shot close to the seabed (<1 m) to preserve natural colours and avoid the distorting effect of muddy water. The underwater video footage was captured using a remotely operated vehicle (ROV) and a drop-down camera. The footage was converted to 2D mosaic images of the seabed. 2D mosaics were manually annotated by several experts using the Labelbox tool and co-annotations were refined using the SurveyJS platform. A set of carefully annotated underwater images associated with the original videos can be used by marine biologists as a biological atlas, as well as practitioners in the fields of machine vision, pattern recognition, and deep learning as training materials for the development of various tools for automatic analysis of underwater imagery.