MCTED - Mars CTX Terrain-Elevation Dataset

Dataset repository (pending ESA license) | arXiv article

Overview

MCTED is a machine-learning-ready dataset of optical images of the surface of Mars, paired with their corresponding digital elevation models. It was created using an extensive repository of orthoimage-DEM pairs with the NASA Ames Stereo Pipeline using the Mars Reconneissance Orbiter's CTX instrument imagery by Day et al. 2023. We process the samples from the repository using a developed pipeline aimed at eliminating elevation artifacts, imputing missing data points and sample selection. The dataset is provided in the form of 518x518 patches.

This dataset is fully open-source, with all data and code used for it's generation available publicly.

Dataset contents

The dataset contains in total 80,898 samples, divided into two splits:

Training	Validation
65,090	15,808

Each sample consists of 4 different files:

Type	Description
optical.png	The monochromatic optical image patch. Despite being monochromatic, the image still has 3 channels, with all channels being the same
elevation.tiff	The elevation data patch in meters w.r.t. the Martian datum
deviation_mask.png	Binary mask with locations that were identified as elevation artifacts during dataset generation and were replaced with interpolated values
initial_nan_mask.png	Binary mask with locations that contained missing values in the Day et al. data samples and were imputed during processing

Sample naming

Each sample follows the following naming convention:

Data source

The dataset has been generated using a orthoimage-DEM pair repository generated from MROs CTX imagery using the NASA Ames Stereo Pipeline by Day et al. 2023. We pass the samples through an extensive processing and selection pipeline, using approximately 47% of the available data.

Typical usage

The simplest way to use MCTED is by using the load_dataset function from HuggingFace's datasets python package:

from datasets import load_dataset

# Download and load the dataset 
mcted = load_dataset("ESA-Datalabs/MCTED", num_proc=8)

Example of accessing sample data

from datasets import load_dataset
import matplotlib.pyplot as plt
import numpy as np

mcted = load_dataset("ESA-Datalabs/MCTED", num_proc=8)

# Load one sample from the validation split
sample = mcted["validation"][0]

plt.figure(figsize=(15, 5))
plt.subplot(1, 4, 1)
plt.imshow(sample["optical.png"])
plt.title("Optical image")

plt.subplot(1, 4, 2)
plt.imshow(np.array(sample["elevation.tif"]), cmap="terrain")
plt.title("DEM")

plt.subplot(1, 4, 3)
plt.imshow(sample["deviation_mask.png"], cmap="gray")
plt.title("Elevation outlier mask")

plt.subplot(1, 4, 4)
plt.imshow(sample["initial_nan_mask.png"], cmap="gray")
plt.title("Initial invalid values mask")

Citation

@misc{osadnik2025,
      title={MCTED: A Machine-Learning-Ready Dataset for Digital Elevation Model Generation From Mars Imagery}, 
      author={Rafał Osadnik and Pablo Gómez and Eleni Bohacek and Rickbir Bahia},
      year={2025},
      eprint={2509.08027},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2509.08027}, 
}