MarsDEMNet

MarsDEMNet is a comparative deep learning study for single-image Digital Elevation Model (DEM) prediction from Mars CTX satellite imagery. Four architectures are evaluated, a classical Random Forest baseline, a single-output U-Net, a multi-output U-Net with multi-task learning, and an encoder depth ablation — all trained on the MCTED dataset of 80,898 paired CTX orthoimage and DEM patches.

Model Details

Model Description

MarsDEMNet addresses a fundamental coverage asymmetry on Mars: while the CTX instrument has photographed ~99.5% of the Martian surface at 5–6 m/pixel, high-resolution stereo DEMs exist for only ~0.5–1% of that coverage. Models trained on MCTED learn to predict dense elevation maps from single optical images, extending effective DEM coverage to nearly the entire planet.

• Model type: Convolutional encoder-decoder (U-Net)
• License: CC-BY 4.0
• Finetuned from: Trained from scratch — no pretrained weights

Model Sources

• Repository: https://github.com/harshithkethavath/MarsDEMNet
• Dataset: https://huggingface.co/datasets/ESA-Datalabs/MCTED

Checkpoints

Four model checkpoints are provided:

File	Architecture	Val RMSE	Val MAE	Delta-1
marsdеmnet-unet-elevation-4block.pt	Single-output U-Net, 4-block encoder, 7.8M params	74.38m	52.86m	0.418
marsdеmnet-unet-multitask-4block.pt	Multi-output U-Net, 4-block encoder, 7.8M params	74.29m	52.68m	0.422
marsdеmnet-unet-multitask-3block.pt	Multi-output U-Net, 3-block encoder, 1.9M params	82.80m	58.29m	0.440
marsdеmnet-unet-multitask-5block.pt	Multi-output U-Net, 5-block encoder, 31.4M params	59.88m	42.67m	0.409

The 5-block multi-output model is the best overall, achieving 19% lower RMSE than the 4-block baseline with no overfitting observed.

How to Get Started

import torch
from scripts.deeplearning.unet import UNet

# Single-output (elevation only) — 4-block
model = UNet(in_channels=1, out_channels=1, num_blocks=4, base_ch=32)
ckpt  = torch.load("marsdеmnet-unet-elevation-4block.pt", map_location="cpu")
model.load_state_dict(ckpt["model_state"])
model.eval()

# Multi-output (elevation + slope + roughness) — 5-block (best)
model = UNet(in_channels=1, out_channels=3, num_blocks=5, base_ch=32)
ckpt  = torch.load("marsdеmnet-unet-multitask-5block.pt", map_location="cpu")
model.load_state_dict(ckpt["model_state"])
model.eval()

# Inference
with torch.no_grad():
    # optical: (1, 1, 518, 518) normalized CTX patch
    pred = model(optical)
    # Single-output: pred shape (1, 1, 518, 518) — elevation
    # Multi-output:  pred shape (1, 3, 518, 518) — [elevation, slope, roughness]

Input normalization: clip to 2nd–98th percentile, then z-score per patch. DEM targets are mean-subtracted per patch (relative elevation in meters).

Training Details

Training Data

MCTED (Mars CTX Terrain-Elevation Dataset) — 80,898 paired CTX orthoimage and DEM patches derived from 1,122 quality-filtered stereo scenes. Geography-aware train/val split at the scene level to prevent spatial leakage. Train: 65,090 patches. Val: 15,808 patches.

Training Procedure

• Optimizer: AdamW, lr=1e-4, weight_decay=1e-4
• Schedule: Cosine annealing to 1e-6 over 50 epochs
• Early stopping: Patience 10 on val RMSE
• Batch size: 16
• Augmentation: Random horizontal/vertical flips and 90° rotations applied jointly to image and labels
• Loss: Masked MAE (single-output); weighted sum of masked MAE losses (multi-output, uniform 1:1:1 weights)
• Training regime: fp32
• Hardware: NVIDIA H100 GPU

Preprocessing

• CTX patches: percentile clip (2nd–98th) + per-patch z-score normalization
• DEM patches: per-patch mean subtraction (relative elevation)
• Validity masking: logical AND of NaN mask and deviation mask; invalid pixels excluded from loss and metrics

Evaluation

Metrics

• MAE — mean absolute elevation error in meters
• RMSE — primary ranking metric; penalizes large errors
• Delta-1 — fraction of valid pixels where max(pred/gt, gt/pred) < 1.25

Results

Model	Params	Val RMSE	Val MAE	Delta-1
Random Forest (classical baseline)	—	58.39m (elev std)	41.29m	—
Single-output U-Net (4-block)	7.8M	74.38m	52.86m	0.418
Multi-output U-Net uniform (4-block)	7.8M	74.29m	52.68m	0.422
Multi-output U-Net (3-block ablation)	1.9M	82.80m	58.29m	0.440
Multi-output U-Net (5-block ablation)	31.4M	59.88m	42.67m	0.409

Bias, Risks, and Limitations

• Models are trained on regions of Mars where stereo DEMs exist, which are geographically biased toward scientifically interesting terrain. Performance on flat, featureless plains may be lower.
• Textureless terrain with no illumination gradient provides no depth cue, a known failure mode.
• Predictions are relative elevation (mean-subtracted per patch), not absolute MOLA-referenced altitude.
• Not suitable for safety-critical mission planning without further validation.

Technical Specifications

Model Architecture

U-Net encoder-decoder with configurable depth. Each encoder block: Conv2d(3×3) → BatchNorm → ReLU × 2 → MaxPool. Decoder: bilinear upsampling + lateral skip connections. Multi-output variant has three separate 1×1 conv heads for elevation, slope, and roughness.

Citation

If you use MarsDEMNet, please cite:

@misc{marsdеmnet2026,
  title     = {MarsDEMNet: Classical and Deep Learning Approaches for Single-Image Digital Elevation Model Prediction from Mars CTX Imagery},
  author    = {Harshith Kethavath},
  year      = {2026},
  publisher = {GitHub},
  url       = {https://github.com/harshithkethavath/MarsDEMNet}
}