Ziheng Sun, Talya ten Brink, Wendy Carande, Gerbrand Koren, Nicoleta Cristea, Corin Jorgenson, Bhargavi Janga, Gokul Prathin Asamani, Sanjana Achan, Mike Mahoney, Qian Huang, Armin Mehrabian, Thilanka Munasinghe, Zhong Liu, Aaron Margolis, Peter Webley, Bing Gong, Yuhan Rao, Annie Burgess, Andrew Huang, Laura Sandoval, Brianna R. Pagán, Sebnem Duzgun
{"title":"实现地球科学中的实用人工智能","authors":"Ziheng Sun, Talya ten Brink, Wendy Carande, Gerbrand Koren, Nicoleta Cristea, Corin Jorgenson, Bhargavi Janga, Gokul Prathin Asamani, Sanjana Achan, Mike Mahoney, Qian Huang, Armin Mehrabian, Thilanka Munasinghe, Zhong Liu, Aaron Margolis, Peter Webley, Bing Gong, Yuhan Rao, Annie Burgess, Andrew Huang, Laura Sandoval, Brianna R. Pagán, Sebnem Duzgun","doi":"10.1007/s10596-024-10317-7","DOIUrl":null,"url":null,"abstract":"<p>Although Artificial Intelligence (AI) projects are common and desired by many institutions and research teams, there are still relatively few success stories of AI in practical use for the Earth science community. Many AI practitioners in Earth science are trapped in the prototyping stage and their results have not yet been adopted by users. Many scientists are still hesitating to use AI in their research routine. This paper aims to capture the landscape of AI-powered geospatial data sciences by discussing the current and upcoming needs of the Earth and environmental community, such as what practical AI should look like, how to realize practical AI based on the current technical and data restrictions, and the expected outcome of AI projects and their long-term benefits and problems. This paper also discusses unavoidable changes in the near future concerning AI, such as the fast evolution of AI foundation models and AI laws, and how the Earth and environmental community should adapt to these changes. This paper provides an important reference to the geospatial data science community to adjust their research road maps, find best practices, boost the FAIRness (Findable, Accessible, Interoperable, and Reusable) aspects of AI research, and reasonably allocate human and computational resources to increase the practicality and efficiency of Earth AI research.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"10 1","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Towards practical artificial intelligence in Earth sciences\",\"authors\":\"Ziheng Sun, Talya ten Brink, Wendy Carande, Gerbrand Koren, Nicoleta Cristea, Corin Jorgenson, Bhargavi Janga, Gokul Prathin Asamani, Sanjana Achan, Mike Mahoney, Qian Huang, Armin Mehrabian, Thilanka Munasinghe, Zhong Liu, Aaron Margolis, Peter Webley, Bing Gong, Yuhan Rao, Annie Burgess, Andrew Huang, Laura Sandoval, Brianna R. 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This paper also discusses unavoidable changes in the near future concerning AI, such as the fast evolution of AI foundation models and AI laws, and how the Earth and environmental community should adapt to these changes. 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Towards practical artificial intelligence in Earth sciences
Although Artificial Intelligence (AI) projects are common and desired by many institutions and research teams, there are still relatively few success stories of AI in practical use for the Earth science community. Many AI practitioners in Earth science are trapped in the prototyping stage and their results have not yet been adopted by users. Many scientists are still hesitating to use AI in their research routine. This paper aims to capture the landscape of AI-powered geospatial data sciences by discussing the current and upcoming needs of the Earth and environmental community, such as what practical AI should look like, how to realize practical AI based on the current technical and data restrictions, and the expected outcome of AI projects and their long-term benefits and problems. This paper also discusses unavoidable changes in the near future concerning AI, such as the fast evolution of AI foundation models and AI laws, and how the Earth and environmental community should adapt to these changes. This paper provides an important reference to the geospatial data science community to adjust their research road maps, find best practices, boost the FAIRness (Findable, Accessible, Interoperable, and Reusable) aspects of AI research, and reasonably allocate human and computational resources to increase the practicality and efficiency of Earth AI research.
期刊介绍：
Computational Geosciences publishes high quality papers on mathematical modeling, simulation, numerical analysis, and other computational aspects of the geosciences. In particular the journal is focused on advanced numerical methods for the simulation of subsurface flow and transport, and associated aspects such as discretization, gridding, upscaling, optimization, data assimilation, uncertainty assessment, and high performance parallel and grid computing.
Papers treating similar topics but with applications to other fields in the geosciences, such as geomechanics, geophysics, oceanography, or meteorology, will also be considered.
The journal provides a platform for interaction and multidisciplinary collaboration among diverse scientific groups, from both academia and industry, which share an interest in developing mathematical models and efficient algorithms for solving them, such as mathematicians, engineers, chemists, physicists, and geoscientists.
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