Deep-learning methods are of interest for the analysis of imagery and digital elevation models from Mars orbiting satellites. They detect various atmosphere and surface characteristics. For instance, these include dust storms and craters [1,2]. We approach this topic by using the deep-learning-based crater detection algorithm DeepMars2 [3,4]. The algorithm is applied to two digital elevation models (DEMs) of the Mars surface. The DEMs are based on the satellite instruments MOLA/MGS (Mars Orbiter Laser Altimeter/Mars Global Surveyor) and HRSC/MEX (High Resolution Stereo Camera/Mars Express) and have different resolution. Crater detection statistics are compared between both DEMs.