The Slope Unit represents the fundamental unit for analyzing slope stability, and it necessitates essential parameters (such as elevation, slope, aspect, surface roughness, vegetation cover, profile curvature, etc.) for predicting the potential for slope unit collapse could be inferred by the 3D model which was generated from UAV(Unmanned Aerial Vehicle) Imagery. This study aims to investigate the feasibility and efficacy of employing UAV (Unmanned Aerial Vehicle) as a tool for analyzing slope unit collapse potential. Currently, models constructed from satellite data have demonstrated substantial competence in evaluating and forecasting slope collapse potential. In this research, multiple sets of UAV data with varying resolutions and a control group of satellite data are utilized as a test dataset within a random forest machine learning model to assess the relationship between resolution and scoring and the potential of UAV data as a satellite data alternative.