Mars has kilometer‐thick polar ice caps, which represent a unique laboratory to study the dynamics of ice sheets and the possible effects of climate change on a planet other than Earth. Most of the north polar cap margins are characterized by steep scarps, where avalanches and ice‐block falls are frequently observed. These cause a measurable scarp retreat, depending on season and the associated solar heating cycle. Rock falls also represent useful sources for seismic experiment and the exploration of the subsurface of the planet. The High‐Resolution Imaging Science Experiment (HiRISE) on-board the Mars Reconnaissance Orbiter (MRO) has been monitoring the planet for more than a decade, returning images at resolutions up to 25 cm, many of which in stereo. However, the identification of such small “mass wasting” effects, such as block and rock falls, is far from trivial considering the vast number of images in their differing illumination and viewing geometry. The image analysis requires new approaches of automated detection involving machine learning, which go beyond the traditional classification and regression schemes. In this project we apply modern data science techniques, such as deep convolutional networks to identify and measure rock sizes and volumes. The techniques are to be trained on established areas and ultimately be applied to new sets of images to investigate the recent climate evolution and support the seismic exploration of the planet.