Artificial intelligence (AI) has significantly advanced Earth sciences, yet its full potential in to comprehensively modeling Earth's complex dynamics remains unrealized. Geoscience foundation models (GFMs) emerge as a paradigm-shifting solution, integrating extensive cross-disciplinary data to enhance the simulation and understanding of Earth system dynamics. These data-centric AI models extract insights from petabytes of structured and unstructured data, effectively addressing the complexities of Earth systems that traditional models struggle to capture. The unique strengths of GFMs include flexible task specification, diverse input-output capabilities, and multi-modal knowledge representation, enabling analyses that surpass those of individual data sources or traditional AI methods. This review not only highlights the key advantages of GFMs, but also presents essential techniques for their construction, with a focus on transformers, pre-training, and adaptation strategies. Subsequently, we examine recent advancements in GFMs, including large language models, vision models, and vision-language models, particularly emphasizing the potential applications in remote sensing. Additionally, the review concludes with a comprehensive analysis of the challenges and future trends in GFMs, addressing five critical aspects: data integration, model complexity, uncertainty quantification, interdisciplinary collaboration, and concerns related to privacy, trust, and security. This review offers a comprehensive overview of emerging geoscientific research paradigms, emphasizing the untapped opportunities at the intersection of advanced AI techniques and geoscience. It examines major methodologies, showcases advances in large-scale models, and discusses the challenges and prospects that will shape the future landscape of GFMs.

翻译：人工智能（AI）已显著推动地球科学发展，但其在全面建模地球复杂动力学方面的全部潜力尚未实现。地球科学基础模型（GFMs）作为一种范式转换的解决方案应运而生，它整合了广泛的跨学科数据，以增强对地球系统动力学的模拟与理解。这些以数据为中心的AI模型从海量结构化和非结构化数据中提取洞见，有效应对了传统模型难以捕捉的地球系统复杂性。GFMs的独特优势包括灵活的任务定义、多样化的输入输出能力以及多模态知识表示，使其能够实现超越单一数据源或传统AI方法的分析。本文不仅重点阐述了GFMs的关键优势，还系统介绍了构建此类模型的核心技术，重点关注Transformer架构、预训练策略及适配方法。随后，我们审视了GFMs领域的最新进展，包括大语言模型、视觉模型及视觉-语言模型，特别强调了其在遥感领域的潜在应用。此外，本文通过综合分析GFMs面临的挑战与未来趋势作结，涵盖数据整合、模型复杂性、不确定性量化、跨学科协作，以及与隐私、信任和安全相关的五大关键议题。本综述全面概述了新兴的地球科学研究范式，强调了先进AI技术与地球科学交叉领域尚未开发的机遇，系统检视了主要方法论，展示了大规模模型的进展，并讨论了将塑造GFMs未来格局的挑战与前景。