Snow avalanches are destructive natural hazards in mountain regions that continue to claim lives, and cause infrastructural damage and traffic detours. Given that avalanches often occur in remote and poorly-accessible steep terrain, their detection and mapping is extensive and time consuming. Nonetheless, systematic avalanche detection over large areas could help to generate more complete and up-to-date inventories (cadastres) necessary for validating avalanche forecasting and hazard mapping. In this study, we have focused on automatically detecting avalanches and classifying them into release zones, tracks, and runout zones based on 0.25-m near-infrared (NIR) ADS80-SH92 aerial imagery using an object-based image analysis (OBIA) approach. Our algorithm takes into account the brightness, the normalised difference vegetation index (NDVI), and the normalised difference water index (NDWI) and its standard deviation (SDNDWI) in order to distinguish avalanches from other land-surface elements. Using normalised parameters allows readily applying this method across large areas. We trained the method by analysing the properties of snow avalanches at three 4-km<sup>2</sup> areas near Davos, Switzerland. We compared the results with manually-mapped avalanche polygons, and obtained a user's accuracy of > 0.9 and a Cohen's kappa of 0.79–0.85. Testing the method for a larger area of 226.3 km<sup>2</sup>, we estimated producer's and user's accuracies of 0.61 and 0.78, respectively, with a Cohen's kappa of 0.67. Detected avalanches that overlapped with reference data by > 80 % occurred randomly throughout the testing area, showing that our method avoids overfitting. Our method shows potential in large-scale avalanche mapping, although further investigations into other regions are desirable to verify the stability of our selected thresholds and the transferability of the method.