This review provides a critical analysis of the use of satellite data for landslide mapping. It examines 291 publications from over 1,000 authors, covering nearly 700,000 landslides across 52 countries, with China and Italy leading in contributions. The study highlights the significant increase in landslide-related research since 2014, driven by the availability of Sentinel-1 and Sentinel-2 data. Landslides are classified based on movement type and velocity, with satellite imagery used to detect flows and slides, often triggered by rainfall, earthquakes, or both. Manual detection remains common, but artificial intelligence is increasingly used for automated landslide mapping. The review discusses the evolution of Earth Observation (EO) datasets, emphasizing the role of satellite missions in improving landslide mapping capabilities. It highlights the use of EO data for mapping landslides at local and global scales, with a focus on the increasing availability of satellite imagery and the development of new techniques for processing and interpreting data. The study also addresses the challenges in effectively using EO data for landslide detection and mapping, suggesting future research directions based on current and planned satellite missions. The analysis of the literature shows that landslide mapping has been primarily conducted using satellite data, with a growing number of studies utilizing AI and machine learning techniques. The study identifies key factors influencing landslide mapping, including the availability of satellite data, the resolution and frequency of satellite imagery, and the use of AI for automated detection. The review also discusses the importance of integrating EO data with other sources, such as news and social media, to improve the accuracy and timeliness of landslide mapping. The study concludes that EO-based landslide mapping has become a critical tool for understanding and managing landslide hazards. However, challenges remain in the effective and operational use of EO data for landslide detection and mapping. The review emphasizes the need for continued research and development in this area, particularly in improving the accuracy and efficiency of landslide mapping techniques. The study also highlights the importance of collaboration between researchers, policymakers, and practitioners to ensure the effective use of EO data for landslide management.This review provides a critical analysis of the use of satellite data for landslide mapping. It examines 291 publications from over 1,000 authors, covering nearly 700,000 landslides across 52 countries, with China and Italy leading in contributions. The study highlights the significant increase in landslide-related research since 2014, driven by the availability of Sentinel-1 and Sentinel-2 data. Landslides are classified based on movement type and velocity, with satellite imagery used to detect flows and slides, often triggered by rainfall, earthquakes, or both. Manual detection remains common, but artificial intelligence is increasingly used for automated landslide mapping. The review discusses the evolution of Earth Observation (EO) datasets, emphasizing the role of satellite missions in improving landslide mapping capabilities. It highlights the use of EO data for mapping landslides at local and global scales, with a focus on the increasing availability of satellite imagery and the development of new techniques for processing and interpreting data. The study also addresses the challenges in effectively using EO data for landslide detection and mapping, suggesting future research directions based on current and planned satellite missions. The analysis of the literature shows that landslide mapping has been primarily conducted using satellite data, with a growing number of studies utilizing AI and machine learning techniques. The study identifies key factors influencing landslide mapping, including the availability of satellite data, the resolution and frequency of satellite imagery, and the use of AI for automated detection. The review also discusses the importance of integrating EO data with other sources, such as news and social media, to improve the accuracy and timeliness of landslide mapping. The study concludes that EO-based landslide mapping has become a critical tool for understanding and managing landslide hazards. However, challenges remain in the effective and operational use of EO data for landslide detection and mapping. The review emphasizes the need for continued research and development in this area, particularly in improving the accuracy and efficiency of landslide mapping techniques. The study also highlights the importance of collaboration between researchers, policymakers, and practitioners to ensure the effective use of EO data for landslide management.