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.
Underwater video surveys play a significant role in marine benthic research. Usually, surveys are filmed in transects, which are stitched into 2D mosaic maps for further analysis. Due to the massive amount of video data and time-consuming analysis, the need for automatic image segmentation and quantitative evaluation arises. This paper investigates such techniques on annotated mosaic maps containing hundreds of instances of brittle stars. By harnessing a deep convolutional neural network with pre-trained weights and post-processing results with a common blob detection technique, we investigate the effectiveness and potential of such segment-and-count approach by assessing the segmentation and counting success. Discs could be recommended instead of full shape masks for brittle stars due to faster annotation among marker variants tested. Underwater image enhancement techniques could not improve segmentation results noticeably, but some might be useful for augmentation purposes.
Complex bottom topography and the presence of oating ice signi cantly complicates the use of traditional sampling methods in Arctic coastal waters, forcing to look for alternative approaches. One such technique is underwater imagery, which has grown in popularity in recent decades based on its effectiveness in hard-to-reach places. We demonstrate that an underwater video mosaic can be a reliable method for comparative analysis of Arctic habitats under the glacier in uence and glacier in uence-free habitats. The lming was carried out in the upper sublittoral (2-65 m) of Hornsund and Isfjorden areas, representing two ice-free and two glaciated bays. Video footage was obtained using an ROV mounted and "drop-down" video cameras and transformed into 148 video mosaics. Based on the lowest possible taxonomic level, 31 biological features (morphospecies) were identi ed and ascribed to benthic functional groups based on their feeding and mobility type. The morphospecies and functional groups were used for the comparative analysis of benthic communities. The study found that melting glaciers have a stronger impact on the structure of benthic communities across geographical areas. Morphological and functional composition of macrofauna re ected conditions in glacier in uence and in uence-free, and turbid water riverine bays. We discovered greater abundances of motile scavengers in glacier in uence bays whereas glacier in uence-free bay had more sessile suspension lters and glacier in uence-free riverine bay was dominated by discreetly motile and deposit feeders. Underwater imagery mosaics have proven to be a fairly reliable tool for eldwork-e cient quantitative characterization of benthic communities in the hard-to-reach Arctic's upper sublittoral. HighlightsCombination of underwater imagery and morphospecies approaches provides su cient data to quantify the dominant macrobenthos forms in the arctic fjords.The morphospecies approach makes it possible to reveal the differences in the functional structure of the benthos in the periglacial and glacier in uence-free areas of the upper sublittoral.Underwater imagery may be recommended for eldwork-e cient assessment of benthos in conditions of complex bottom topography and the presence of oating ice.Consequently, this study highlights a greater in uence of melting glaciers on the structure of benthic communities than across geographical areas
Complex bottom topography and the presence of floating ice significantly complicates the use of traditional sampling methods in Arctic coastal waters, forcing to look for alternative approaches. One such technique is underwater imagery, which has grown in popularity in recent decades based on its effectiveness in hard-to-reach places. We demonstrate that an underwater video mosaic can be a reliable method for comparative analysis of Arctic habitats under the glacier influence and glacier influence-free habitats. The filming was carried out in the upper sublittoral (2-65 m) of Hornsund and Isfjorden areas, representing two ice-free and two glaciated bays. Video footage was obtained using an ROV mounted and "drop-down" video cameras and transformed into 148 video mosaics. Based on the lowest possible taxonomic level, 31 biological features (morphospecies) were identified and ascribed to benthic functional groups based on their feeding and mobility type. The morphospecies and functional groups were used for the comparative analysis of benthic communities. The study found that melting glaciers have a stronger impact on the structure of benthic communities across geographical areas. Morphological and functional composition of macrofauna reflected conditions in glacier influence and influence-free, and turbid water riverine bays. We discovered greater abundances of motile scavengers in glacier influence bays whereas glacier influence-free bay had more sessile suspension filters and glacier influence-free riverine bay was dominated by discreetly motile and deposit feeders. Underwater imagery mosaics have proven to be a fairly reliable tool for fieldwork-efficient quantitative characterization of benthic communities in the hard-to-reach Arctic's upper sublittoral.
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