arXiv Computer Vision
MarsFM: Shading-Regularized Flow Matching for Martian Relief Estimation
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arXiv AI
Sep 15
Multimodal-Multiresolution Foundation Model for Lunar Remote Sensing
The paper introduces a multimodal foundation model for lunar remote sensing, trained from scratch on SomBench—a dataset of nearly two million co‑registered tile bundles across 11 modalities at 1 m and 100 m resolutions. The model extends the TerraMind masked‑token architecture with lunar‑specific features such as explicit acquisition geometry and joint training of two spatial scales, and employs FlexiViT patch embeddings for adaptable patch sizes. Evaluation on crater detection, irregular mare patch segmentation, and polar ice prospectivity regression shows that the pretrained model matches or surpasses ImageNet‑pretrained baselines, with notable label efficiency and effective adaptation via LoRA.
arXiv Computer Vision
Sep 22
MarsRecon: Self-Supervised and Multimodal Surface Representations for Mars
arXiv:2609.22379v1 Announce Type: new Abstract: High-resolution orbital imagery offers a rich record of the Martian surface, but sparse geological labels limit supervised representation learning. We...
arXiv Computer Vision
Sep 15
Global-Local Contextual Progressive Expansion Network for Martian Landslide Segmentation in Multimodal Remote Sensing Imagery
arXiv:2609.13332v1 Announce Type: new Abstract: Automated landslide segmentation on Mars is one of the important tasks for understanding its surface processes, and all will aid in future space explor...
arXiv AI
Jul 7
Moonstone: A Multimodal Foundation Model and Benchmark for Lunar Remote Sensing
arXiv:2607. 03644v1 Announce Type: cross Abstract: Decades of orbital missions have produced multi-modal remote sensing data for the Moon, spanning optical imagery, spectroscopy, thermal emission, radar, gravity, and elemental composition.
arXiv Computer Vision
Sep 3
Adapting a Foundation Model for Lunar Surface Height Estimation
The paper proposes a method to adapt the Depth Anything V2 (DAV2) zero‑shot relative depth model for estimating lunar surface height. By fine‑tuning DAV2 with publicly available stereophotogrammetry‑derived DEM data, the authors achieve a significant performance boost over the unadapted zero‑shot model. This improved estimator can provide more accurate relative height information useful for hazard detection in future ESA lunar landings.
arXiv Computer Vision
Sep 1
nnMNet: Baseline for Martian Terrain Semantic Segmentation
arXiv:2608.29609v1 Announce Type: new Abstract: Semantic segmentation is a crucial task for understanding Mars, the most Earth-like planet in our solar system. However, it is challenging because the...