When Geoscience Meets Generative AI and Large Language Models: Foundations, Trends, and Future Challenges

• URL: http://arxiv.org/abs/2402.03349v1
• Date: Thu, 25 Jan 2024 12:03:50 GMT
• Title: When Geoscience Meets Generative AI and Large Language Models: Foundations, Trends, and Future Challenges
• Authors: Abdenour Hadid, Tanujit Chakraborty, Daniel Busby
• Abstract summary: Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. This paper explores the potential applications of generative AI and large language models in geoscience.
• Score: 4.013156524547072
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a large spectrum of applications ranging from biology, medicine, education, legislation, computer science, and finance. As one strives for enhanced safety, efficiency, and sustainability, generative AI indeed emerges as a key differentiator and promises a paradigm shift in the field. This paper explores the potential applications of generative AI and large language models in geoscience. The recent developments in the field of machine learning and deep learning have enabled the generative model's utility for tackling diverse prediction problems, simulation, and multi-criteria decision-making challenges related to geoscience and Earth system dynamics. This survey discusses several GAI models that have been used in geoscience comprising generative adversarial networks (GANs), physics-informed neural networks (PINNs), and generative pre-trained transformer (GPT)-based structures. These tools have helped the geoscience community in several applications, including (but not limited to) data generation/augmentation, super-resolution, panchromatic sharpening, haze removal, restoration, and land surface changing. Some challenges still remain such as ensuring physical interpretation, nefarious use cases, and trustworthiness. Beyond that, GAI models show promises to the geoscience community, especially with the support to climate change, urban science, atmospheric science, marine science, and planetary science through their extraordinary ability to data-driven modeling and uncertainty quantification.

Related papers

• Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation [26.397297480169858]
  Graph Neural Networks (GNNs) emerge as an important innovation, propelling DL into the non-Euclidean domain. GNNs can effectively tackle the challenges posed by diverse modalities, multiple sensors, and the heterogeneous nature of Earth Observation data. This paper explores a broad spectrum of GNNs' applications to scientific problems in Earth systems, covering areas such as weather and climate analysis, disaster management, air quality monitoring, agriculture, land cover classification, hydrological process modeling, and urban modeling.
  arXiv  Detail & Related papers  (2024-11-05T16:12:12Z)
• Foundation Models for Remote Sensing and Earth Observation: A Survey [101.77425018347557]
  This survey systematically reviews the emerging field of Remote Sensing Foundation Models (RSFMs) It begins with an outline of their motivation and background, followed by an introduction of their foundational concepts. We benchmark these models against publicly available datasets, discuss existing challenges, and propose future research directions.
  arXiv  Detail & Related papers  (2024-10-22T01:08:21Z)
• Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
  Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks. We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
  arXiv  Detail & Related papers  (2024-02-21T06:22:41Z)
• Challenges in data-based geospatial modeling for environmental research and practice [19.316860936437823]
  Data-based geospatial modelling using machine learning (ML) has gained popularity in environmental research. This survey reviews common nuances in geospatial modelling, such as imbalanced data, spatial autocorrelation, prediction errors, model generalisation, domain specificity, and uncertainty estimation.
  arXiv  Detail & Related papers  (2023-11-18T12:30:49Z)
• 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)
• 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)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)
• 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)
• 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)
• 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.