Ocean science, which delves into the oceans that are reservoirs of life and
biodiversity, is of great significance given that oceans cover over 70% of our
planet's surface. Recently, advances in Large Language Models (LLMs) have
transformed the paradigm in science. Despite the success in other domains,
current LLMs often fall short in catering to the needs of domain experts like
oceanographers, and the potential of LLMs for ocean science is under-explored.
The intrinsic reason may be the immense and intricate nature of ocean data as
well as the necessity for higher granularity and richness in knowledge. To
alleviate these issues, we introduce OceanGPT, the first-ever LLM in the ocean
domain, which is expert in various ocean science tasks. We propose DoInstruct,
a novel framework to automatically obtain a large volume of ocean domain
instruction data, which generates instructions based on multi-agent
collaboration. Additionally, we construct the first oceanography benchmark,
OceanBench, to evaluate the capabilities of LLMs in the ocean domain. Though
comprehensive experiments, OceanGPT not only shows a higher level of knowledge
expertise for oceans science tasks but also gains preliminary embodied
intelligence capabilities in ocean technology. Codes, data and checkpoints will
soon be available at this https URL

OceanGPT 是第一个专注于海洋领域的大型语言模型，通过 DoInstruct 框架和 OceanBench 评测系统，在海洋科学任务上展示了更高水平的知识专业性，并在海洋技术中具备初步的具身智能能力。

OceanGPT：用于海洋科学任务的大型语言模型

OceanGPT: A Large Language Model for Ocean Science Tasks

With the increasing amount of spatial-temporal~(ST) ocean data, numerous
spatial-temporal data mining (STDM) studies have been conducted to address
various oceanic issues, e.g., climate forecasting and disaster warning.
Compared with typical ST data (e.g., traffic data), ST ocean data is more
complicated with some unique characteristics, e.g., diverse regionality and
high sparsity. These characteristics make it difficult to design and train STDM
models. Unfortunately, an overview of these studies is still missing, hindering
computer scientists to identify the research issues in ocean while discouraging
researchers in ocean science from applying advanced STDM techniques. To remedy
this situation, we provide a comprehensive survey to summarize existing STDM
studies in ocean. Concretely, we first summarize the widely-used ST ocean
datasets and identify their unique characteristics. Then, typical ST ocean data
quality enhancement techniques are discussed. Next, we classify existing STDM
studies for ocean into four types of tasks, i.e., prediction, event detection,
pattern mining, and anomaly detection, and elaborate the techniques for these
tasks. Finally, promising research opportunities are highlighted. This survey
will help scientists from the fields of both computer science and ocean science
have a better understanding of the fundamental concepts, key techniques, and
open challenges of STDM in ocean.

对海洋中基于时空数据挖掘的研究进行全面调查，总结了海洋中使用的时空海洋数据集的特征、数据质量增强技术，以及预测、事件检测、模式挖掘和异常检测等四类任务的技术，为计算机科学和海洋科学领域的科学家提供了更好的基本概念、关键技术和待解决问题的了解。