This special issue explores how researchers are dealing with the increasing issue of (i) defining the quality of landslide inventories that are being produced, including how this impacts training and validating models of automatic landslide detection as well as the reliability of susceptibility and hazard modelling, and (ii) comparing different landslide maps of the same areas.

Topics include but are not limited to the following:

• metrics for evaluating mapping accuracy, errors, and uncertainty;
• statistical modelling of mapping errors and machine-learning-based (ML-based) classification;
• quality assessment methods for landslide inventory maps (LIMs);
• the impact of error propagation on susceptibility models, hazard assessment, and risk evaluation;
• model inter-comparisons;
• relating LIM quality to use limitations and decision-making at different land-management levels.

Review process: all papers of this special issue underwent the regular interactive peer-review process of Natural Hazards and Earth System Sciences handled by members of the editorial board as well as guest editors designated by the NHESS executive editors.