Abstract In rockfall susceptibility mapping, the reliability of predictive models depends heavily on the quality and structure of input data, particularly the rockfall inventory that forms the foundation of any analysis. Despite its central role, one critical aspect is often overlooked: the geometric representation of this inventory. Many studies apply different formats, points, polylines, or polygons, without questioning their methodological appropriateness or impact on model performance. This