用哨兵-3数据训练的AI模型，能高效解析海洋颜色变化。
A Sentinel-3 foundation model for ocean colour
- 基于哨兵-3遥感数据自监督预训练，构建海洋颜色分析模型
- 在叶绿素浓度与初级生产力估算任务中表现优于现有方法
- 适合小样本高精度海洋监测，尤其擅长捕捉精细空间模式
人工智能基础模型（FMs）在海量无标签数据上预训练后，有望显著改变海洋科学中的应用格局，因为该领域标注数据稀少且获取成本高。本文介绍一种基于Prithvi-EO Vision Transformer架构的新基础模型，其在哨兵-3海洋与陆地色度仪（OLCI）数据上进行自监督预训练。通过微调评估其在两个下游海洋地球观测任务中的表现：一是与现有基线模型对比量化叶绿素浓度；二是评估其对遥感估算海洋初级生产力的改进能力。结果表明，该自训练基础模型能有效利用少量高质量标注数据，在捕捉海洋颜色细节空间分布的同时与点观测保持一致，展现出强大潜力。这表明新一代地理空间AI模型可为海洋生态系统及其在全球气候过程中的作用提供更稳健、数据驱动的洞察。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) Foundation models (FMs), pre-trained on massive unlabelled datasets, have the potential to drastically change AI applications in ocean science, where labelled data are often sparse and expensive to collect. In this work, we describe a new foundation model using the Prithvi-EO Vision Transformer architecture which has been pre-trained to reconstruct data from the Sentinel-3 Ocean and Land Colour Instrument (OLCI). We evaluate the model by fine-tuning on two downstream marine earth observation tasks. We first assess model performance compared to current baseline models used to quantify chlorophyll concentration. We then evaluate the FMs ability to refine remote sensing-based estimates of ocean primary production. Our results demonstrate the utility of self-trained FMs for marine monitoring, in particular for making use of small amounts of high quality labelled data and in capturing detailed spatial patterns of ocean colour whilst matching point observations. We conclude that this new generation of geospatial AI models has the potential to provide more robust, data-driven insights into ocean ecosystems and their role in global climate processes.
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