Dataset: Improved Benthic Classification from Different AUV Surveys with Unsupervised Domain Adaptation

Published: 15 September 2022| Version 1 | DOI: 10.17632/d2yn52n9c9.1
Contributors:
Heather Doig,
,

Description

This dataset contains patch images of benthic species and physical features from two sets of AUV surveys used for machine learning research into Unsupervised Domain Adaptation. The dataset includes a README.md to explain the structure of the directories. The first dataset (EMR) is from Elizabeth and Middleton Reef, Australia captured in January 2020 by the AUV Sirius and the AUV Nimbus. The second dataset (SHR) is from the South Hydrate Ridge in the North Eastern Pacific captured by the AUV AE2000F and the AUV Tuna-sand during the Adaptive Robotics cruise in 2018. This dataset is intended for machine learning research. The original images and manual point annotations are available on squidle.org (EMR) and soi.squidle.org (SHR). ACKNOWLEDGMENT The data from the EMR dataset was part of Australia’s Integrated Marine Observing System (IMOS) – IMOS is enabled by the National Collaborative Research Infrastructure Strategy (NCRIS). The EMR annotations were based on work undertaken for the Marine Biodiversity Hub, a collaborative partnership supported through funding from the Australian Government’s National Environmental Science Program (NESP). The images for the SHR dataset were collected during the Schmidt Ocean Institute's FK180731 \#Adaptive Robotics campaign, with support from the Japanese Government's Zipangu in the Ocean Strategic Innovation Program.

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Institutions

University of Sydney Australian Centre for Field Robotics, University of Sydney

Categories

Robotics Machine Vision, Benthic Ecology, Transfer Learning

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