首个面向海底地质调查的大规模多模态数据集，助力海洋机器学习发展
SeafloorAI: A Large-scale Vision-Language Dataset for Seafloor Geological Survey
- 构建跨5类地质层的海底声呐图像与语言描述联合数据集
- 覆盖1.73万平方公里，含69.6万张图像和700万问答对
- 适合海洋科学与计算机视觉交叉研究者使用
机器学习在海洋科学，特别是声呐图像分析中的进展受限于高质量数据集的缺乏。尽管已有公开的声呐图像数据集，但其环境覆盖范围和数据规模仍有限。为此，我们推出SeafloorAI，首个由海洋科学家合作构建、涵盖5个地质层的大型可直接用于AI的海底测绘数据集。进一步，通过引入语言标注，扩展为SeafloorGenAI，支持视觉-语言协同建模。该数据集包含62个地理分布的数据勘测，覆盖17,300平方千米，包含696,000张声呐图像、827,000个分割掩码、696,000条详细语言描述以及约700万个问答对。我们开源数据处理代码，旨在促进海洋科学界共同丰富数据资源，并激发机器学习领域开发更鲁棒的模型。这种协作模式将提升两个领域的研究能力与应用前景。
原文摘要 · Abstract (English)
A major obstacle to the advancements of machine learning models in marine science, particularly in sonar imagery analysis, is the scarcity of AI-ready datasets. While there have been efforts to make AI-ready sonar image dataset publicly available, they suffer from limitations in terms of environment setting and scale. To bridge this gap, we introduce SeafloorAI, the first extensive AI-ready datasets for seafloor mapping across 5 geological layers that is curated in collaboration with marine scientists. We further extend the dataset to SeafloorGenAI by incorporating the language component in order to facilitate the development of both vision- and language-capable machine learning models for sonar imagery. The dataset consists of 62 geo-distributed data surveys spanning 17,300 square kilometers, with 696K sonar images, 827K annotated segmentation masks, 696K detailed language descriptions and approximately 7M question-answer pairs. By making our data processing source code publicly available, we aim to engage the marine science community to enrich the data pool and inspire the machine learning community to develop more robust models. This collaborative approach will enhance the capabilities and applications of our datasets within both fields.
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