Håvard Boutera Toft , John Sykes , Andrew Schauer, Jordy Hendrikx, and Audun Hetland
Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2023-114, in review
Publication year: 2023

This article is currently under review for the Open Access journal Natural Hazards and Earth System Science. Click here to download a copy.


This paper documents substantial improvements to the original automated avalanche terrain exposure mapping (AutoATES v1.0) algorithm. The most significant drawbacks of AutoATES v1.0 have been addressed by including forest density data, improving the avalanche runout estimations in low-angle runout zones, accounting for overhead exposure and open-source software. The algorithm also supports the new ATES v2.0 terrain class ‘extreme’ terrain. We used two benchmark maps from Bow Summit and Connaught Creek to validate the improvements from AutoATES v1.0 to v2.0. For Bow Summit, the F1 score (a measure of how well the algorithm performs) improved from 64.01 % to 77.30 %. For Connaught Creek, the F1 score improved from 39.81 % to 71.38 %. The main challenge limiting large-scale mapping is the determination of optimal input parameters for different regions and climates. In areas where AutoATES v2.0 is applied, it can be a valuable tool for avalanche risk assessment and decision. Ultimately, our goal is for AutoATES v2.0 to enable efficient, large-scale, and potentially global ATES mapping in a standardized manner rather than based solely on expert judgement.