Lowe Scott C, Misiuk Benjamin, Xu Isaac, Abdulazizov Shakhboz, Baroi Amit R, Bastos Alex C, Best Merlin, Ferrini Vicki, Friedman Ariell, Hart Deborah, Hoegh-Guldberg Ove, Ierodiaconou Daniel, Mackin-McLaughlin Julia, Markey Kathryn, Menandro Pedro S, Monk Jacquomo, Nemani Shreya, O'Brien John, Oh Elizabeth, Reshitnyk Luba Y, Robert Katleen, Roelfsema Chris M, Sameoto Jessica A, Schimel Alexandre C G, Thomson Jordan A, Wilson Brittany R, Wong Melisa C, Brown Craig J, Trappenberg Thomas
Vector Institute, Toronto, Ontario, Canada.
Memorial University of Newfoundland, Department of Geography, St. John's, Newfoundland, Canada.
Sci Data. 2025 Feb 7;12(1):230. doi: 10.1038/s41597-025-04491-1.
Advances in underwater imaging enable collection of extensive seafloor image datasets necessary for monitoring important benthic ecosystems. The ability to collect seafloor imagery has outpaced our capacity to analyze it, hindering mobilization of this crucial environmental information. Machine learning approaches provide opportunities to increase the efficiency with which seafloor imagery is analyzed, yet large and consistent datasets to support development of such approaches are scarce. Here we present BenthicNet: a global compilation of seafloor imagery designed to support the training and evaluation of large-scale image recognition models. An initial set of over 11.4 million images was collected and curated to represent a diversity of seafloor environments using a representative subset of 1.3 million images. These are accompanied by 3.1 million annotations translated to the CATAMI scheme, which span 190,000 of the images. A large deep learning model was trained on this compilation and preliminary results suggest it has utility for automating large and small-scale image analysis tasks. The compilation and model are made openly available for reuse.
水下成像技术的进步使得收集监测重要底栖生态系统所需的大量海底图像数据集成为可能。收集海底图像的能力已经超过了我们对其进行分析的能力，这阻碍了这些关键环境信息的应用。机器学习方法为提高海底图像分析效率提供了机会，但支持此类方法开发的大规模且一致的数据集却很稀缺。在此，我们展示了BenthicNet：一个全球海底图像汇编，旨在支持大规模图像识别模型的训练和评估。我们收集并整理了最初的1140多万张图像，使用130万张图像的代表性子集来呈现各种海底环境。这些图像还配有310万条按照CATAMI方案翻译的注释，涵盖了19万张图像。基于此汇编训练了一个大型深度学习模型，初步结果表明它可用于自动化大规模和小规模图像分析任务。该汇编和模型已公开提供以供重用。
