Avalanche terrain is classified according to the size of avalanches, depending on spatial distribution of potential release areas and corresponding runout. Maps classifying avalanche-prone terrain aid in planning activities. Examples generated by computational algorithms include the automated Avalanche Terrain Exposure Scale (autoATES) and the Classified Avalanche Terrain (CAT). This study aims to compare autoATES and CAT classification qualitatively and quantitatively. Therefore, both classification methods are applied to two test regions: an area around Davos (Switzerland) and one in Sellrain (Austria). While autoATES combines model chain results to classify terrain into four classes from simple to extreme, CAT explicitly delineates potential release areas, maximal runout, zones for potential remote triggering and very steep slopes. Despite differing outputs, both algorithms share similar intermediate steps, computing potential release areas and simulating avalanche runout using different tools.
