Directional Statistics and Machine Learning for crater detection in Space

Craters are distinctive features on the surfaces of most terrestrial planets such as Mars and Venus. The distribution of craters reveals the relative ages of surface units and provides information on surface geology. Extracting craters is one of the fundamental tasks in planetary research. Although many automated crater detection algorithms have been developed to extract craters from image or topographic data, most of them are applicable only in particular regions, and only a few can be widely used, especially in complex surface settings. On the other side, once we have a reasonable craters data, statistics play an important role in better understanding their features, in particular their distribution.

In this workshop, we will demonstrate to participants how basic methodologies with directional statistics and machine learning/deep learning models help in the detection and analysis of craters in our Universe.