Developing global web landslide observatory to support sustainable development and landslide risk reductionMikoš, Matjaž (Avtor) Pita Costa, João (Avtor) landslide monitoringmultilingual data integrationnatural language processinggeospatial analysisdisaster risk reductioncapacity buildingThe use of digital technologies is becoming increasingly vital in complementing traditional methods for observing and understanding landslide events on a global scale. This article presents a novel framework for a global web Landslide Observatory powered by artificial intelligence (AI), designed to enhance early detection, monitoring, and response to landslide events. The proposed observatory leverages multilingual news streams, peer-reviewed scientific literature, and AI-related policy documents as dynamic data sources. By integrating natural language processing (NLP) the system enables real-time extraction, classification, and mapping of landslide events across diverse linguistic and geographic contexts. The observatory also incorporates policy-aware AI models to align with evolving global governance and ethical standards. This interdisciplinary approach aims to bridge gaps in traditional landslide monitoring systems, offering a scalable, inclusive, and policy-responsible tool for disaster risk reduction and environmental resilience. By supporting informed decision-making and timely action, the observatory contributes directly to several United Nations Sustainable Development Goals (SDGs), including Goal 6 (in water-related disasters), Goal 11 (on the resilience of Sustainable Cities and Communities), and Goal 13 (on Climate Change Preparedness). The global web Landslide Observatory is also a contribution to the IHP-IX Program (2022–2029) on Science for a Water Secure World in a Changing Environment, and to the Kyoto Landslide Commitment 2020. The global web Landslide Observatory is also a contribution to the new governance, asked by the last Global Assessment Report on Disaster Risk Reduction.20262026-08-07 09:06:11Drugo185520sl