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arxiv: 2606.13252 · v1 · pith:FVKN7EXFnew · submitted 2026-06-11 · 💻 cs.LG

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

Douglas Dziedzorm Agbeve , Aditya V. Handrale , Salim Fares , Seif E. Idani This is my paper

Pith reviewed 2026-06-27 07:30 UTC · model grok-4.3

classification 💻 cs.LG
keywords Mars DEMsemantic segmentationGANmound detectionneural networksplanetary mappingdata augmentation
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The pith

Adding GAN-generated data does not improve neural network segmentation for detecting mounds on Mars.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper compares supervised semantic segmentation with a generative adversarial network approach for automatically detecting mounds on Martian digital elevation models. It finds that the supervised model does not benefit from additional artificially generated data. A sympathetic reader would care because this challenges the assumption that GANs reliably help when real training data is scarce in specialized remote-sensing tasks. The methods aim to support rover navigation and searches for past water or habitable environments by automating what was previously manual mapping.

Core claim

A comparison of the approaches shows that adding extra artificially generated data did not improve the result when using supervised semantic segmentation and generative adversarial methods to detect mounds on Mars digital elevation models.

What carries the argument

Direct comparison of supervised semantic segmentation performance versus the same models trained with additional GAN-generated training examples, measured against manually mapped morphological parameters as ground truth.

If this is right

  • Manual mapping of mound morphologies can remain the primary source of training labels without loss of model performance.
  • Computational effort spent on GAN training and data synthesis may be redirected toward collecting more real DEM coverage or refining network architectures.
  • For this Mars dataset size and terrain variety, standard supervised training suffices for mound segmentation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The result may indicate that the distribution of GAN outputs diverges enough from real Martian terrain to add noise rather than signal.
  • Similar segmentation tasks on other planetary bodies with sparse labeled data might also see limited gains from current GAN augmentation techniques.
  • Varying the GAN architecture or conditioning it more tightly on elevation statistics could be tested to see if the negative finding persists.

Load-bearing premise

The manually mapped morphological parameters supply accurate and sufficiently representative ground-truth labels for both training and evaluating the segmentation models.

What would settle it

A replication study that reports higher segmentation accuracy (for example by IoU or F1 score) when the GAN-augmented dataset is used compared with the supervised-only baseline on an identical held-out test set.

Figures

Figures reproduced from arXiv: 2606.13252 by Aditya V. Handrale, Douglas Dziedzorm Agbeve, Salim Fares, Seif E. Idani.

Figure 1
Figure 1. Figure 1: Variations of Segmentation [27] proposed, to the best of our knowledge, the original idea of using GANs to generate terrain from DEMs. Their architecture was based on deep convolutional GAN (DCGAN)[28] – a type of GAN made up of a fractional-strided convolutions (generator) and a discriminator of strided convolutions. Spick et al.[29] presented a method of generating height maps from digital elevation of r… view at source ↗
Figure 2
Figure 2. Figure 2: The purpose of using interpolation is to fill the [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Splitting the original DEM into a set of tiles to. In [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 3
Figure 3. Figure 3: Slope and Hillshade gave us a better visualization [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Mounds samples from the training dataset. [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The annotated image (mask) is obtained by overlap [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 9
Figure 9. Figure 9: Pix2Pix architecture 4.5 Training For training the U-Net and the FPN we used 2728 images in total. Of which, 1632 were used for the training dataset and 546 for the testing and validation set each. These images were obtained after the feature engineering step where we combine channels of original DEM, slope, hillshade. The dimension of the images is 224 x 192 x 3. All the images have 3 channels. For the FP… view at source ↗
Figure 10
Figure 10. Figure 10: GAN generated output results [PITH_FULL_IMAGE:figures/full_fig_p007_10.png] view at source ↗
Figure 12
Figure 12. Figure 12: Comparison on predicted mask To compare both U-Net and FPN models performance, we tested the trained models with augmented data and real data using F1 score as a metric. Results are shown in the table 1. U-Net without GAN U-Net with GAN FPN with￾out GAN FPN with GAN Accuracy 0.95 0.97 0.97 0.94 Precision 0.77 0.80 0.75 0.78 Recall 0.77 0.68 0.83 0.73 FDR 0.23 0.20 0.25 0.23 FOR 0.02 0.03 0.02 0.03 F1-Scor… view at source ↗
Figure 11
Figure 11. Figure 11: Examples of generated augmented data - The second strategy consist of rating the performances of a semantic segmentation network model while segmenting the real data and while segmenting the synthetic generated data. The model used in our case is U-Net pre-trained model. In this context, we will investigate whether the U-net model and the FPN are able to segment data, and whether its performance improves … view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper addresses automatic detection of mounds on Mars using semantic segmentation on Digital Elevation Models (DEMs). It describes supervised neural network segmentation and a generative adversarial approach, with the central empirical claim being that augmenting the training set with GAN-generated data does not improve segmentation performance over the supervised baseline. Ground truth is obtained by manually mapping morphological parameters onto DEMs.

Significance. If the no-improvement result is substantiated with proper controls, it would provide a concrete negative finding on the utility of GAN augmentation for this planetary mapping task, which could inform data-scarce remote-sensing applications. The work also highlights the potential of semantic segmentation for extraterrestrial feature detection, but the current manuscript supplies none of the quantitative evidence needed to evaluate that claim.

major comments (2)
  1. [Abstract] Abstract: the headline claim that 'adding extra artificially generated data did not improve the result' is presented without any dataset size, train/test split, evaluation metrics (IoU, precision, recall, etc.), training hyperparameters, or statistical tests. This absence makes the comparison impossible to assess and directly undermines the soundness of the central result.
  2. [Abstract] Abstract and methods description: the ground-truth labels are produced by 'manually mapping morphological parameters onto Digital Elevation Models' with no reported inter-annotator agreement, coverage statistics, boundary-error analysis, or class-balance information. Because both the baseline and GAN-augmented models are trained and scored against the same unvalidated labels, any 'no improvement' conclusion could be an artifact of label noise rather than a property of the augmentation strategy.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for these constructive comments on the abstract and methods. We agree that the current version lacks essential quantitative details and will revise accordingly to make the central empirical claim evaluable.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the headline claim that 'adding extra artificially generated data did not improve the result' is presented without any dataset size, train/test split, evaluation metrics (IoU, precision, recall, etc.), training hyperparameters, or statistical tests. This absence makes the comparison impossible to assess and directly undermines the soundness of the central result.

    Authors: We agree that the abstract is missing these details and that this prevents proper evaluation of the no-improvement result. In the revised manuscript we will expand the abstract (and add a corresponding results table) to report dataset size, train/test split, IoU/precision/recall/F1 scores for both models, training hyperparameters, and any statistical significance tests. The underlying experiments already contain these quantities; they were simply omitted from the original submission. revision: yes

  2. Referee: [Abstract] Abstract and methods description: the ground-truth labels are produced by 'manually mapping morphological parameters onto Digital Elevation Models' with no reported inter-annotator agreement, coverage statistics, boundary-error analysis, or class-balance information. Because both the baseline and GAN-augmented models are trained and scored against the same unvalidated labels, any 'no improvement' conclusion could be an artifact of label noise rather than a property of the augmentation strategy.

    Authors: We acknowledge the concern. The original manuscript provides only a brief description of the manual labeling process. In revision we will add a dedicated subsection on label generation that includes coverage statistics, class balance, and any available boundary-error analysis. If inter-annotator agreement was not collected, we will explicitly discuss this limitation and its possible effect on the interpretation of the GAN-augmentation result. We will also clarify that both models were evaluated against the identical label set, so any label noise affects both equally. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical comparison with no derivations or self-referential fits

full rationale

The paper reports an empirical study: manual morphological mapping supplies labels for training supervised segmentation models, with and without GAN augmentation; performance is compared directly on held-out data. No equations, parameter fits presented as predictions, uniqueness theorems, or self-citation chains appear in the provided text. The central claim reduces to measured IoU/F1 differences rather than any definitional or fitted-input reduction. This matches the default expectation for non-circular empirical ML papers.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; it invokes no explicit free parameters, mathematical axioms, or newly postulated entities beyond the standard assumptions of supervised learning and GAN training.

pith-pipeline@v0.9.1-grok · 5647 in / 964 out tokens · 23125 ms · 2026-06-27T07:30:49.131443+00:00 · methodology

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