Toward a standard for landslide data: Bridging gaps in landslide susceptibility modeling and early warning systems

This report outlines the urgent need for a unified global standard for landslide data to strengthen disaster risk reduction, particularly in susceptibility modelling and early warning systems. Drawing on open-access datasets and machine‑learning analysis in Nepal, it demonstrates how fragmented, inconsistent and incomplete inventories—lacking key attributes such as triggers, volumes, impacts and geotechnical properties—undermine hazard prediction and risk modelling, ultimately limiting the effectiveness of preparedness and mitigation efforts. The study proposes a tiered, interoperable data framework aligned with ISO 19115, Open Geospatial Consortium standards and Sendai Framework indicators, aimed at enabling countries to document landslide events in a consistent, scalable manner and to support risk‑informed decision‑making across diverse contexts. 

The study recommends institutionalising this standard through international development programmes, including those of the World Bank, to improve global coordination, enhance model performance and reduce disaster losses. It highlights the need for regular updates to inventories, integration of geotechnical and impact data, and capacity‑building for national agencies to ensure sustained implementation. Strengthening data governance, promoting open‑access practices and embedding the framework in early warning and planning systems are presented as essential steps to protect vulnerable communities and improve the reliability of landslide risk assessments.