Interpretable foundation models as decryptors peering into the Earth system

THE EARTH SYSTEM AMID THE BIG DATA PARADIGM.The processes of the Earth system drive interactions between energy,matter,and life,and a comprehensive understanding of their full evolutionary trajectory is critical for sustainable human development.Traditional modeling primarily relies on a set of theoretical equations to simulate dynamic process such as the carbon-nitrogen cycle,solar radiation dynamics,and terrestrial ecosystem dynamics.1 Despite the extensive modeling experience of Earth scientists,the rapid advancement of Earth observation techniques has led to a significant increase in the volume of databases,with data accumulating daily or even hourly.This has exacerbated the conflict between the capacity for data collection and utilization for big Earth data.Consequently,there is an urgent need to enhance the intelligent processing and analysis of big Earth data.

Artificial intelligence for geoscience:Progress,challenges,and perspectives

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection techniques.Traditional models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical processes.However,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world applicability.In contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical knowledge.ML techniques have shown promise in addressing Earth science-related questions.Nevertheless,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into geoscience.The integration of physics-based and data-driven methodologies into hybrid models presents an alternative paradigm.These models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data requirements.This review provides a comprehensive overview of geoscientific research paradigms,emphasizing 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 AI in geoscience.The paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

Information extraction from remote sensing imagery

Various platforms,such as satellite,aircraft,ground-based,some emerging aspects (e.g.internet) have resulted in a dramatic improvement in the capabilities of earth observations (EO).The numerous remote sensing data promote an enhanced possibility to assess,monitor,and predict the dynamics of land-covers,anthropologic processes,and influence to the environments.Nonetheless,the properties of the data acquired by such diverse sources pose challenges to the processing methodologies,and hence,development of a series of new methods for the analysis of remote sensing images is required,The aim of this special issue of Geospatial Information Science is to develop new ideas and technologies to facilitate the utility of remote sensing data and to further explore its potential in various applications.