mlsanityData to 14 Oct 2026

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

arXiv 2606.13252 ↗First posted 11 Jun 2026cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 37 references that are in the map. First posted on arXiv 11 Jun 2026.

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14 of 37 references.

References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  4. 2022Generative Adversarial Networkscited by 5,627
  5. 2016Feature Pyramid Networks for Object Detectioncited by 5,036
  6. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  7. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
  8. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,546
  9. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  10. 2018Adversarial Learning for Semi-Supervised Semantic Segmentationcited by 173
  11. 2018GAN Augmentation: Augmenting Training Data using Generative Adversarial Networkscited by 107
  12. 2016Semantic Segmentation using Adversarial Networkscited by 73
2 more
  1. 2016Texture Synthesis with Spatial Generative Adversarial Networkscited by 45
  2. 2015ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentationcited by 19

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