Conventional and advanced geospatial techniques for landslide detection and modeling: a comprehensive overview

Abstract Background Landslides represent a significant natural hazard, causing widespread human, infrastructure, and environmental losses. Geospatial technologies have become essential for monitoring, detection, and risk assessment of landslides. This review provides a comprehensive examination of the evolution and application of geospatial technologies in landslide research, addressing a significant gap in the current literature. First, we focus on landslide monitoring and detection, covering spaceborne and airborne Earth Observation (EO) technologies, ground-based remote sensing, mobile Geographic Information System (GIS) applications, and data processing methodologies, including traditional image and Artificial Intelligence (AI)-based approaches. Second, we examine landslide analysis, which includes susceptibility mapping, vulnerability and risk assessments. Results Our bibliometric analysis reveals that landslide susceptibility is the most extensively studied category, followed by risk, while vulnerability remains significantly underexplored. China, India, South Korea, Iran, and the United States are the most active contributors across all three categories. China leads in susceptibility research due to high publication volume. A similar pattern is observed in risk studies, where China, Italy, India, and the United States have the most publications. However, the notable underrepresentation of vulnerability research suggests a gap in understanding the socio-economic and infrastructure impacts of landslides. These findings highlight the need for greater emphasis on vulnerability studies to improve landslide risk mitigation. Conclusions Our results emphasize the need for increased focus on vulnerability studies to strengthen landslide risk mitigation strategies. In addition, we identify key challenges in landslide management and discuss emerging trends aimed at improving prediction, monitoring, and disaster response.