Landslide inventory data sets are required for any landslide susceptibility mapping and prediction approaches. However, generating accurate landslide inventory data sets depends on applied methods and quality of input data, for example spatial resolution for satellite imagery. Therefore, the accuracy and availability of inventories vary in different studies. This study evaluated a strategy of sudden landslide identification product (SLIP) for landslide detection using Bi-Temporal Sentinel 2 Imagery and ALOS Digital Elevation Model (DEM). The resulting landslide detection map was then compared with an improved version of SLIP based on a fuzzy overlay. The resulting probability map was classified into three classes using the natural breaks method; the third class with the highest probability was extracted as the final map. The accuracy assessment stage demonstrated that using the improved version increased the accuracy by 16% compared to the SLIP method.