When Geoscience Meets Foundation Models: Toward a general geoscience artificial intelligence system

被引:3
作者
Zhang, Hao [1 ,2 ]
Xu, Jin-Jian [2 ,3 ]
Cui, Hong-Wei [3 ]
Li, Lin [4 ]
Yang, Yaowen [5 ]
Tang, Chao-Sheng [4 ]
Boers, Niklas [6 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut, Coll Elect & Informat Engn, Nanjing 211106, Peoples R China
[2] Nanjing Univ, Nanjing 211106, Peoples R China
[3] Huawei Technol Co Ltd, Sch Earth Sci & Engn, Shanghai 210023, Peoples R China
[4] Nanjing Univ, Nanjing 200040, Peoples R China
[5] Nanyang Technol Univ, Sch Civil & Environm Engn, Nanyang 639798, Singapore
[6] Tech Univ Munich, Sch Engn & Design, Earth Syst modeling, D-80333 Munich, Germany
基金
中国国家自然科学基金; 新加坡国家研究基金会;
关键词
Geoscience; Artificial intelligence; Frequency modulation; Earth; Reviews; Data models; Adaptation models; Surveys; Analytical models; Computational modeling; NEURAL-NETWORKS; DEEP;
D O I
10.1109/MGRS.2024.3496478
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
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 multimodal 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, vision-language models, and foundation-model-based agents, 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. The paper highlights a dynamic field rich with possibilities, poised to unlock new insights into Earth's complexities and further advance geoscience exploration.
引用
收藏
页码:2 / 41
页数:40
相关论文
共 298 条
[1]   A review of uncertainty quantification in deep learning: Techniques, applications and challenges [J].
Abdar, Moloud ;
Pourpanah, Farhad ;
Hussain, Sadiq ;
Rezazadegan, Dana ;
Liu, Li ;
Ghavamzadeh, Mohammad ;
Fieguth, Paul ;
Cao, Xiaochun ;
Khosravi, Abbas ;
Acharya, U. Rajendra ;
Makarenkov, Vladimir ;
Nahavandi, Saeid .
INFORMATION FUSION, 2021, 76 :243-297
[2]  
2023, Arxiv, DOI [arXiv:2303.08774, DOI 10.48550/ARXIV.2303.08774, 10.48550/arXiv.2303.08774]
[3]   Vision-Language Models for Zero-Shot Classification of Remote Sensing Images [J].
Al Rahhal, Mohamad Mahmoud ;
Bazi, Yakoub ;
Elgibreen, Hebah ;
Zuair, Mansour .
APPLIED SCIENCES-BASEL, 2023, 13 (22)
[4]   Efficient Machine Learning for Big Data: A Review [J].
Al-Jarrah, Omar Y. ;
Yoo, Paul D. ;
Muhaidat, Sami ;
Karagiannidis, George K. ;
Taha, Kamal .
BIG DATA RESEARCH, 2015, 2 (03) :87-93
[5]  
Alayrac JB, 2022, ADV NEUR IN
[6]   Challenges, tasks, and opportunities in modeling agent-based complex systems [J].
An, Li ;
Grimm, Volker ;
Sullivan, Abigail ;
Turner, B. L., II ;
Malleson, Nicolas ;
Heppenstall, Alison ;
Vincenot, Christian ;
Robinson, Derek ;
Ye, Xinyue ;
Liu, Jianguo ;
Lindkvist, Emilie ;
Tang, Wenwu .
ECOLOGICAL MODELLING, 2021, 457
[7]   A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets [J].
Anantrasirichai, N. ;
Biggs, J. ;
Albino, F. ;
Bull, D. .
REMOTE SENSING OF ENVIRONMENT, 2019, 230
[8]  
[Anonymous], "Claude 3.5 Sonnet news
[9]  
[Anonymous], 2024, AnthropicMar. 14
[10]  
[Anonymous], 2006, P 12 ACM SIGKDD IN