Accelerating Earth Science Discovery via Multi-Agent LLM Systems

• URL: http://arxiv.org/abs/2503.05854v1
• Date: Fri, 07 Mar 2025 13:25:56 GMT
• Title: Accelerating Earth Science Discovery via Multi-Agent LLM Systems
• Abstract summary: This Perspective explores the transformative potential of Multi-Agent Systems (MAS) powered by Large Language Models (LLMs) in the geosciences.<n>MAS possesses transformative potential for improving scientists' interaction with geoscientific data by enabling intelligent data processing, natural language interfaces, and collaborative problem-solving capabilities.
• Score: 0.1398098625978622
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This Perspective explores the transformative potential of Multi-Agent Systems (MAS) powered by Large Language Models (LLMs) in the geosciences. Users of geoscientific data repositories face challenges due to the complexity and diversity of data formats, inconsistent metadata practices, and a considerable number of unprocessed datasets. MAS possesses transformative potential for improving scientists' interaction with geoscientific data by enabling intelligent data processing, natural language interfaces, and collaborative problem-solving capabilities. We illustrate this approach with "PANGAEA GPT", a specialized MAS pipeline integrated with the diverse PANGAEA database for Earth and Environmental Science, demonstrating how MAS-driven workflows can effectively manage complex datasets and accelerate scientific discovery. We discuss how MAS can address current data challenges in geosciences, highlight advancements in other scientific fields, and propose future directions for integrating MAS into geoscientific data processing pipelines. In this Perspective, we show how MAS can fundamentally improve data accessibility, promote cross-disciplinary collaboration, and accelerate geoscientific discoveries.

Related papers

• Compass: Navigating Global Marine Lead Data Integration through Expert-Guided LLM Agent [55.77954024801206]
  Marine lead (Pb) and its isotopes are critical tracers for ocean circulation and anthropogenic pollution.<n>Manual extraction is unscalable, while general-purpose Large Language Models (LLMs) lack the necessary domain-specific knowledge.<n>We introduce an expert-guided adaptation approach that enables LLMs to perform rigorous scientific data extraction without fine-tuning.
  arXiv  Detail & Related papers  (2026-05-28T14:06:25Z)
• Connecting the Dots: A Machine Learning Ready Dataset for Ionospheric Forecasting Models [0.27300286905606946]
  We present a curated, open-access dataset that integrates diverse ionospheric and heliospheric measurements into a coherent, machine learning-ready structure.<n>Our workflow integrates a large selection of data sources comprising Solar Dynamic Observatory data, solar irradiance indices (F10.7), solar wind parameters (velocity and interplanetary magnetic field), geomagnetic activity indices (Kp, AE, SYM-H), and NASA JPL's Global Ionospheric Maps of Total Electron Content (GIM-TEC)
  arXiv  Detail & Related papers  (2025-11-18T20:13:25Z)
• A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers [251.23085679210206]
  Scientific Large Language Models (Sci-LLMs) are transforming how knowledge is represented, integrated, and applied in scientific research.<n>This survey reframes the development of Sci-LLMs as a co-evolution between models and their underlying data substrate.<n>We formulate a unified taxonomy of scientific data and a hierarchical model of scientific knowledge.
  arXiv  Detail & Related papers  (2025-08-28T18:30:52Z)
• 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)
• Spatial Knowledge Graph-Guided Multimodal Synthesis [78.11669780958657]
  We introduce a novel multimodal synthesis approach guided by spatial knowledge graphs, grounded in the concept of knowledge-to-data generation.<n>In experiments, data synthesized from diverse types of spatial knowledge, including direction and distance, enhance the spatial perception and reasoning abilities of MLLMs markedly.<n>We hope that the idea of knowledge-based data synthesis can advance the development of spatial intelligence.
  arXiv  Detail & Related papers  (2025-05-28T17:50:21Z)
• OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence [51.0456395687016]
  multimodal large language models (LLMs) have opened new frontiers in artificial intelligence. We propose a MLLM (OmniGeo) tailored to geospatial applications. By combining the strengths of natural language understanding and spatial reasoning, our model enhances the ability of instruction following and the accuracy of GeoAI systems.
  arXiv  Detail & Related papers  (2025-03-20T16:45:48Z)
• Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and Human-Like Reasoning Framework [59.42946541163632]
  We introduce a comprehensive geolocation framework with three key components.<n>GeoComp, a large-scale dataset; GeoCoT, a novel reasoning method; and GeoEval, an evaluation metric.<n>We demonstrate that GeoCoT significantly boosts geolocation accuracy by up to 25% while enhancing interpretability.
  arXiv  Detail & Related papers  (2025-02-19T14:21:25Z)
• EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision [72.84868704100595]
  This paper presents a dataset specifically designed for self-supervision on remote sensing data, intended to enhance deep learning applications on Earth monitoring tasks.<n>The dataset spans 15 tera pixels of global remote-sensing data, combining imagery from a diverse range of sources, including NEON, Sentinel, and a novel release of 1m spatial resolution data from Satellogic.<n>Accompanying the dataset is EarthMAE, a tailored Masked Autoencoder developed to tackle the distinct challenges of remote sensing data.
  arXiv  Detail & Related papers  (2025-01-14T13:42:22Z)
• Cross-Domain Foundation Model Adaptation: Pioneering Computer Vision Models for Geophysical Data Analysis [18.928794841454312]
  We explore adapting foundation models (FMs) from the computer vision domain to geoscience. This study considers adapting FMs from computer vision to geoscience, analyzing their scale, adaptability, and generality for geoscientific data analysis. We introduce a workflow that leverages existing computer vision FMs, fine-tuning them for geoscientific tasks, reducing development costs while enhancing accuracy.
  arXiv  Detail & Related papers  (2024-08-22T13:41:18Z)
• Swarm Intelligence in Geo-Localization: A Multi-Agent Large Vision-Language Model Collaborative Framework [51.26566634946208]
  We introduce smileGeo, a novel visual geo-localization framework. By inter-agent communication, smileGeo integrates the inherent knowledge of these agents with additional retrieved information. Results show that our approach significantly outperforms current state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-21T03:31:30Z)
• Data Science for Geographic Information Systems [0.0]
  The integration of data science into Geographic Information Systems has facilitated the evolution of these tools into complete spatial analysis platforms. The adoption of machine learning and big data techniques has equipped these platforms with the capacity to handle larger amounts of increasingly complex data. This work traces the historical and technical evolution of data science and GIS as fields of study, highlighting the critical points of convergence between domains.
  arXiv  Detail & Related papers  (2024-04-04T18:50:58Z)
• GeoGalactica: A Scientific Large Language Model in Geoscience [95.15911521220052]
  Large language models (LLMs) have achieved huge success for their general knowledge and ability to solve a wide spectrum of tasks in natural language processing (NLP) We specialize an LLM into geoscience, by further pre-training the model with a vast amount of texts in geoscience, as well as supervised fine-tuning (SFT) the resulting model with our custom collected instruction tuning dataset. We train GeoGalactica over a geoscience-related text corpus containing 65 billion tokens, preserving as the largest geoscience-specific text corpus. Then we fine-tune the model with 1 million pairs of instruction-tuning
  arXiv  Detail & Related papers  (2023-12-31T09:22:54Z)
• Pathway to a fully data-driven geotechnics: lessons from materials informatics [1.2172320168050468]
  This paper highlights the challenges and opportunities inherent in integrating data-driven methodologies into geotechnics. By leveraging the transformative power of deep learning, we envision a paradigm shift towards a more collaborative and innovative geotechnics field.
  arXiv  Detail & Related papers  (2023-12-01T13:45:42Z)
• When Geoscience Meets Foundation Models: Towards General Geoscience Artificial Intelligence System [6.445323648941926]
  Geoscience foundation models (GFMs) are a paradigm-shifting solution, integrating extensive cross-disciplinary data to enhance the simulation and understanding of Earth system dynamics. The unique strengths of GFMs include flexible task specification, diverse input-output capabilities, and multi-modal knowledge representation. This review offers a comprehensive overview of emerging geoscientific research paradigms, emphasizing the untapped opportunities at the intersection of advanced AI techniques and geoscience.
  arXiv  Detail & Related papers  (2023-09-13T08:44:09Z)
• A General Purpose Neural Architecture for Geospatial Systems [142.43454584836812]
  We present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias. We envision how such a model may facilitate cooperation between members of the community.
  arXiv  Detail & Related papers  (2022-11-04T09:58:57Z)
• Applications of physics-informed scientific machine learning in subsurface science: A survey [64.0476282000118]
  Geosystems are geological formations altered by humans activities such as fossil energy exploration, waste disposal, geologic carbon sequestration, and renewable energy generation. The responsible use and exploration of geosystems are thus critical to the geosystem governance, which in turn depends on the efficient monitoring, risk assessment, and decision support tools for practical implementation. Fast advances in machine learning algorithms and novel sensing technologies in recent years have presented new opportunities for the subsurface research community to improve the efficacy and transparency of geosystem governance.
  arXiv  Detail & Related papers  (2021-04-10T13:40:22Z)

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.