In recent years, artificial intelligence has gained significant traction in Earth sciences, driving a shift from qualitative approaches to quantitative, data-driven methodologies. In geomorphology, artificial intelligence techniques are now applied at multiple scales and for diverse purposes, leveraging a wide spectrum of methods including supervised and unsupervised machine learning, regression algorithms, classification models, clustering techniques, neural networks, and dimensionality reduction. This study presents a structured bibliometric analysis of the scientific literature indexed in Scopus, analyzing over 2000 articles published between 1990 and 2024. Through a bibliometric approach, we explore temporal trends, the most commonly used artificial intelligence techniques, thematic domains, geographic patterns, and associated keywords. Results reveal the pervasive use of artificial intelligence in key geomorphological areas, particularly in fluvial, coastal, and erosional contexts, alongside the adoption of a rich variety of algorithms. The study also highlights the wide range of AI techniques applied in geomorphological research, spanning from traditional machine learning models to advanced neural architectures. This review provides a critical overview of the current landscape and outlines future directions to support more transparent, equitable, and integrated adoption of artificial intelligence in geomorphological research. The findings of this study are relevant to a wide range of stakeholders. Researchers and Ph.D. candidates can use the results to identify dominant thematic and methodological trajectories and detect underexplored areas. Data scientists and AI specialists may benefit from the mapped applications to implement advanced techniques in geomorphological contexts. The analysis also offers useful insights for funding agencies aiming to support strategic and equitable research development, particularly in underrepresented regions. Finally, journal editors and publishers may use emerging trends to inform the design of thematic issues and research priorities.
Luppichini et al. (Wed,) studied this question.