Abstract : The surface of Earth and Mars present abundant periodic topographic forms at different scales (mm- km) and in different environments (e.g. Aeolian, Subglacial) called bedforms (e.g. dunes, drumlins). We developed an automated protocol in order to extract the characteristics of aeolian dunes on two observation scales: dunes mapping and analysis on QGis, Python and eCognition are performed using a steps succession: i) Residual Relief extraction as a key feature in deep learning; ii) Deep learning approach (Convolutional Neural Network (CNN), used for the morphology segmentation process from DTM derivatives; iii) Volumetric Obscurance approach (DTM derivative) used for the crestlines extraction and simplification. The workflow is tested in the Quaternary aeolian dunes of Rub'Al Khali desert, in the Arabian Peninsula and reveal a good performance to map dune (precision = 91%; recall = 87%; quality = 70%).