A global scientometric analysis of artificial intelligence and geospatial technologies in water resources and land use research

Abstract

Abstract The integration of artificial intelligence (AI) and geospatial technologies is transforming environmental research, particularly in water resource management and land use/land cover (LULC) analysis. In response to increasing challenges such as climate change, water scarcity, and rapid urbanization, this study presents a global scientometric analysis of research at the intersection of AI, geospatial technologies, water resources, and LULC from 2000 to 2024. A total of 1,180 peer-reviewed publications from Scopus and Web of Science were analyzed using Bibliometrix and Python-based network analysis. The results reveal a significant increase in scientific production after 2018, with China, the USA, and India accounting for more than 40% of total publications. Remote sensing (40.4%) and machine learning (ML) (34.9%) dominate the methodological landscape, highlighting a shift toward data-driven environmental analysis. The study identifies key research trends, collaboration networks, and emerging themes such as deep learning and climate resilience. It also highlights regional disparities and methodological limitations, emphasizing the need for more integrative and reproducible approaches. This work provides a comprehensive global perspective and supports future research and policy development in sustainable water and land management.