Toward a Standard for Landslide Data: Bridging Gaps in Landslide Susceptibility Modeling and Early Warning Systems

Landslides claim more than 4,000 lives annually and lead to approximately US$20 billion in economic losses. However, landslide hazard, risk assessment, and early warning systems remain constrained by fragmented, inconsistent, and incomplete data. This study addresses the global data gap by proposing a standardized, interoperable framework for documenting landslide events across countries. Using open-access data and machine learning–based susceptibility modeling in Nepal, the paper assesses the limitations of existing inventories in terms of spatial resolution, temporal updates, and missing attributes such as triggers, volumes, impacts, and soil-geotechnical properties that are critical for hazard prediction and risk modeling. These deficiencies reduce the accuracy and transferability of models, limiting the effectiveness of early warning and risk mitigation strategies. To fill this gap, the paper proposes a tiered data standard aligned with the International Organization for Standardization 19115, the Open Geospatial Consortium standards, and the Sendai Framework indicators, enabling scalable, consistent reporting of landslide events. The framework improves data completeness and model performance and supports risk-informed decision-making. The World Bank is well-positioned to operationalize this standard through its extensive portfolio of landslide mitigation and disaster risk reduction programs. Institutionalizing such a framework can improve global coordination, reduce disaster losses, and protect vulnerable communities.