Landslides remain one of the most frequent and destructive natural hazards globally, significantly influenced by climate change, rapid urbanization, deforestation, and unplanned developmental activities. In response, geospatial technologies such as Remote Sensing (RS), Geographic Information Systems (GIS), Light Detection and Ranging (LiDAR), Interferometric Synthetic Aperture Radar (InSAR), Global Navigation Satellite System (GNSS) and machine learning techniques have increasingly been applied to improve landslide prediction, monitoring, susceptibility mapping, and disaster risk reduction. This study presents a bibliometric evaluation of global research trends related to geospatial technologies in landslide studies during the period 2000–2025. A total of 14,909 publications indexed in Web of Science and Scopus were analysed using the bibliometrix package in R. The analysis identified 2,385 publication sources and an annual scientific growth rate of 14.64%, indicating the rapid expansion of research activities in the field. The study examined publication trends, citation impact, thematic evolution, keyword structure, and international collaboration patterns. The results revealed substantial growth in scientific production, particularly after 2018, driven by advances in spatial data acquisition, artificial intelligence integration, and increasing global disaster risk awareness. China, Italy, India, and the United States emerged as the leading contributors to the research domain. Keyword and thematic analyses demonstrated a clear transition from conventional geomorphological and mapping-based approaches toward advanced machine learning-driven predictive modelling and geospatial monitoring frameworks. The study further identified two major interconnected research streams: remote sensing-based monitoring and GIS-based susceptibility modelling. The findings highlight the growing global significance, interdisciplinary nature, and technological advancement of geospatial applications in landslide research and disaster management.
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P et al. (2026) studied this question.
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