Papers for

planetary mapping teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Earth trained image features help classify Titan radar terrains

CETUS: How Far Do Representations Trained on Earth Transfer to Cassini SAR of Titan?

Abstract: Cassini synthetic aperture radar (SAR) images reveal the dunes, plains, and lake basins of Titan, providing an instance of representations learned from Earth imagery for planetary terrain classification. Cross-domain Evaluation of Earth-to-Titan Transfer Using SAR (CETUS) compares features from DINOv2, DOFA and CROMA with classical image measurements and features from an untrained vision transformer on the U.S. Geological Survey's Cassini SAR mosaic. The classifiers learn terrain labels from an expert geomorphological map and predict those labels in geographically separate Titan regions. Under logistic regression settings, pretrained encoders achieve higher mean macro F1 than the combined classical features. Encoder rankings change when feature scaling, optimization, and regularization change together. Further training on Titan improves DINOv2 performance, degrades DOFA performance, and leads to mixed results for CROMA under the tested settings. Architectural and input processing differences prevent these comparisons from isolating the effect of pretraining. Classifier fitting and performance on individual terrain classes matter when assessing representation transfer for planetary mapping. Since the map draws partly on the same radar observations, the scores measure agreement with expert interpretation.

Tue 6 OctComputer Vision and Pattern RecognitionMachine Learning
The gist
Cassini spacecraft took radar pictures of Titan, one of Saturn’s moons, showing different types of land like dunes and lakes. The authors tested if computer models trained on Earth images could recognize these land types on Titan’s radar pictures. They found that Earth-trained models generally performed better than traditional image measurements when identifying terrain, though results varied based on training and settings. This means some Earth imagery knowledge can transfer to helping map other planets, but more tuning is needed for best results.
Open → 2610.07576v1

MarsFM improves Martian terrain maps from single images

MarsFM: Shading-Regularized Flow Matching for Martian Relief Estimation

Abstract: We present MarsFM, an image-conditioned latent flow-matching model for local Martian relief estimation from single-band HiRISE RED orthoimagery. The method combines a pretrained generative prior with stereo-derived geometric supervision and a differentiable Lunar--Lambert shading objective. Relief, normal, gradient, curvature, and ordinal terms constrain complementary aspects of terrain structure, while a positive-affine-invariant image comparison constrains rendered appearance. An evaluation comprising 2024 gathered patch records per integration-step count yields mean affine-aligned RMSE between 0.0935 and 0.0957 in normalized signed-log relief space for one to twenty Euler steps. These scores measure agreement with VAE-reconstructed references on positive-reference support. Their narrow range supports low-step inference under this protocol. Spatial, differential, and spectral diagnostics show broad terrain correspondence alongside smoothing, amplitude compression, and boundary mismatch. MarsFM provides a framework for combining learned terrain priors with image-based constraints; establishing improved physical terrain resolution requires matched baselines and independent high-resolution reference data. Data: https://huggingface.co/datasets/SuperComputer/mars_hirise_dtm_processed-6aa9b66ba461e07f; code: https://github.com/Marius-Juston/MarsRecon.

Thu 17 SeptComputer Vision and Pattern Recognition
The gist
Mapping the surface shapes of Mars from images is tricky because images alone don't easily show height details. The authors designed MarsFM, a computer method that guesses local Martian terrain shapes using single red-band images combined with learned mountain and shadow patterns. They check their guesses against known references and find the method closely matches expected terrain shapes even with fewer computation steps. This approach could help produce better 3D maps of Mars without needing multiple images from different angles.
Open → 2609.21095v1