 This paper aims to detect and assess topographic changes quantitatively over Mount Amiens in Seoul, Korea using digital elevation models, DEMS, derived from airborne laser scanning, ALS, data by estimating the spatially distributed uncertainty of ALS-derived DEMS and applying probabilistic analysis with Bayes' theorem to detect landslide traces efficiently. The results indicate that ALS-derived DEMS have the potential to detect landslides with their uncertainty estimation, although the ALS data were acquired in hilly and densely vegetated areas. Quantifying topographic changes due to landslides with high reliability is beneficial for disaster recovery. This article was authored by M.I.Kyong Kim, Hong-Gye-U-Sung, and C. Ong-Sung Kim.