To GAN or Not To GAN: Segmentation Analysis on Mars DEM

• URL: http://arxiv.org/abs/2606.13252v1
• Date: Thu, 11 Jun 2026 12:10:26 GMT
• Title: To GAN or Not To GAN: Segmentation Analysis on Mars DEM
• Abstract summary: This paper solves the problem by automatically detecting and or predicting mounds on Mars using Neural Network based Semantic methodologies.<n>A comparison of the approaches shows that adding extra artificially generated data did not improve the result.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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