To better understand Martian Surface, which is needed to enable Rovers navigate Mars with ease, it is necessary to be able to determine the location of mounds. Detecting and studying these morphologies can also help us find evidence of extraterrestrial life, in this case, more specifically, water or signs of life conducive environments. Detection of mounds was done by manually mapping morphological parameters onto Digital Elevation Models. This paper solves the problem by automatically detecting and or predicting mounds on Mars using Neural Network based Semantic Segmentation methodologies. This is done by using supervised semantic segmentation model and generative adversarial approach. A comparison of the approaches shows that adding extra artificially generated data did not improve the result.

翻译：为了更好地理解火星表面，从而使火星车能够轻松导航，确定土丘的位置至关重要。检测和研究这些地貌形态还有助于我们寻找地外生命的证据，在本研究中，具体而言是水或利于生命存在的环境迹象。土丘的检测过去是通过将形态学参数手动映射到数字高程模型上完成的。本文通过采用基于神经网络的语义分割方法，自动检测或预测火星上的土丘，从而解决了这一问题。具体方法包括使用有监督语义分割模型和生成对抗方法。对这些方法的比较表明，额外添加人工生成的数据并未改善结果。