Is Artificial Intelligence Reshaping the Landscape of the International Academic Community of Geosciences?
- URL: http://arxiv.org/abs/2508.20117v2
- Date: Thu, 04 Sep 2025 06:26:17 GMT
- Title: Is Artificial Intelligence Reshaping the Landscape of the International Academic Community of Geosciences?
- Authors: Liang Li, Yuntian Li, Wenxin Zhao, Shan Ye, Yun Lu,
- Abstract summary: We find that artificial intelligence (AI) is positively transforming geosciences research, with a notable increase in AI-related scientific output in recent years.<n>We are encouraged to observe that earth scientists from developing countries have gained better visibility in the recent AI for Science (AI4S) paradigm.
- Score: 8.391049739588452
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Through bibliometric analysis and topic modeling, we find that artificial intelligence (AI) is positively transforming geosciences research, with a notable increase in AI-related scientific output in recent years. We are encouraged to observe that earth scientists from developing countries have gained better visibility in the recent AI for Science (AI4S) paradigm and that AI is also improving the landscape of international collaboration in geoscience-related research.
Related papers
- AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions [65.44445343399126]
We look at AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing.<n>Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific synthesis.<n>Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation.
arXiv Detail & Related papers (2025-11-26T02:10:28Z) - From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery [90.64813998433253]
Agentic AI shows capabilities in hypothesis generation, experimental design, execution, analysis, and iterative refinement.<n>This survey provides a domain-oriented review of autonomous scientific discovery across life sciences, chemistry, materials science, and physics.
arXiv Detail & Related papers (2025-08-18T05:25:54Z) - How Far Are AI Scientists from Changing the World? [30.483767443654504]
We focus on the central question: How far are AI scientists from changing the world and reshaping the scientific research paradigm?<n>We provide a prospect-driven review that comprehensively analyzes the current achievements of AI Scientist systems.<n>We hope this survey will contribute to a clearer understanding of limitations of current AI Scientist systems.
arXiv Detail & Related papers (2025-07-31T06:32:06Z) - A Self-Evolving AI Agent System for Climate Science [59.08800209508371]
We introduce EarthLink, the first self-evolving AI agent system designed as an interactive "copilot" for Earth scientists.<n>Through natural language interaction, EarthLink automates the entire research workflow by integrating planning, code execution, data analysis, and physical reasoning.<n>It exhibits human-like cross-disciplinary analytical ability and proficiency comparable to a junior researcher in expert evaluations on core large-scale climate tasks.
arXiv Detail & Related papers (2025-07-23T08:29:25Z) - Scaling Laws in Scientific Discovery with AI and Robot Scientists [72.3420699173245]
An autonomous generalist scientist (AGS) concept combines agentic AI and embodied robotics to automate the entire research lifecycle.<n>AGS aims to significantly reduce the time and resources needed for scientific discovery.<n>As these autonomous systems become increasingly integrated into the research process, we hypothesize that scientific discovery might adhere to new scaling laws.
arXiv Detail & Related papers (2025-03-28T14:00:27Z) - Unlocking the Potential of AI Researchers in Scientific Discovery: What Is Missing? [20.94708392671015]
We project that AI4Science's share of total publications will rise from 3.57% in 2024 to approximately 25% by 2050.<n>We propose structured and actionable strategies to position AI researchers at the forefront of scientific discovery.
arXiv Detail & Related papers (2025-03-05T09:29:05Z) - Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation [58.064940977804596]
A plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently.<n>Ethical concerns regarding shortcomings of these tools and potential for misuse take a particularly prominent place in our discussion.
arXiv Detail & Related papers (2025-02-07T18:26:45Z) - Bridging AI and Science: Implications from a Large-Scale Literature Analysis of AI4Science [25.683422870223076]
We present a large-scale analysis of the AI4Science literature.<n>We quantitatively highlight key disparities between AI methods and scientific problems.<n>We explore the potential and challenges of facilitating collaboration between AI and scientific communities.
arXiv Detail & Related papers (2024-11-27T00:40:51Z) - GeoAI in Social Science [0.9527350779226282]
GeoAI, or geospatial artificial intelligence, is an exciting new area that leverages artificial intelligence (AI), geospatial big data, and massive computing power to solve problems with high automation and intelligence.
This paper reviews the progress of AI in social science research, highlighting important advancements in using GeoAI to fill critical data and knowledge gaps.
arXiv Detail & Related papers (2023-12-19T20:23:18Z) - Interpretable Geoscience Artificial Intelligence (XGeoS-AI): Application to Demystify Image Recognition [10.366695826805659]
This study proposes an interpretable geoscience artificial intelligence (XGeoS-AI) framework to unravel the mystery of image recognition in the Earth sciences.
Inspired by the mechanism of human vision, the proposed XGeoS-AI framework generates a threshold value from a local region within the whole image to complete the recognition.
arXiv Detail & Related papers (2023-11-08T01:54:56Z) - Artificial intelligence adoption in the physical sciences, natural
sciences, life sciences, social sciences and the arts and humanities: A
bibliometric analysis of research publications from 1960-2021 [73.06361680847708]
In 1960 14% of 333 research fields were related to AI, but this increased to over half of all research fields by 1972, over 80% by 1986 and over 98% in current times.
In 1960 14% of 333 research fields were related to AI (many in computer science), but this increased to over half of all research fields by 1972, over 80% by 1986 and over 98% in current times.
We conclude that the context of the current surge appears different, and that interdisciplinary AI application is likely to be sustained.
arXiv Detail & Related papers (2023-06-15T14:08:07Z) - GeoAI at ACM SIGSPATIAL: The New Frontier of Geospatial Artificial
Intelligence Research [4.723592249469651]
In this article, we revisit and discuss the state of GeoAI open research directions.
The workshop series has fostered nexus for geoscientists, computer scientists, engineers, entrepreneurs, and decision-makers.
arXiv Detail & Related papers (2022-10-20T18:02:17Z) - Learning from learning machines: a new generation of AI technology to
meet the needs of science [59.261050918992325]
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery.
The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data.
arXiv Detail & Related papers (2021-11-27T00:55:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.