Underwater imaging dataset in a very shallow water environment of Pramuka Island, Seribu Island District, Indonesia

In this article, we present a dataset of underwater videos captured through manual dives in a complex and unstructured seabed area dominated by harbor structures and coral reefs. The area is shallow (0.5 - 7.0 m depth) with an enclosed embayment for the harbor area, offering protection from ocean currents and waves. The coral reef area is located in a more open ocean sloping gently toward the deeper seafloor, leading to a more pronounced rolling shutter effect and camera motion. The dataset was collected using a GoPro Hero 10 camera, employing a standard wide lens with a horizontal field of view (FoV) of 109° and 768 × 432 image resolution. The camera is also equipped with an Inertial Measurement Unit (IMU) sensor, comprising a 200 Hz frequency accelerometer and gyroscope. During underwater deployment, the camera is protected with a 5 mm thick flat glass panel. This camera setting hence creates three medium layers of water-glass-air leading to additional refraction distortion. To address the refraction distortion, the dataset has been subject to pre-calibration utilizing flat refractive geometry found in the Pinax camera model. The Pinax camera model for the underwater imagery is calculated by combining the aspects of pinhole calibration parameters with axial camera projection. The main aim of the dataset collection is to facilitate the testing and evaluation of underwater imaging algorithms that are used in underwater robotics, such as computer vision, photogrammetry, and Simultaneous Localization and Mapping (SLAM).

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

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.