arXiv Machine Learning
Aug 11
A Machine Learning Based Search for Lunar Anomalies
arXiv:2608. 09350v1 Announce Type: cross Abstract: The Lunar Reconnaissance Orbiter (LRO) has been collecting high-resolution images (at around 0.
A Machine Learning Based Search for Lunar Anomalies
The Lunar Reconnaissance Orbiter (LRO) has been collecting high-resolution images (at around 0. 5-2 meters per pixel linearly with its Narrow Angle Camera) of the Moon since 2009, amassing a large dataset of images and offering researchers the opportunity to study the surface of the Moon at unprecedented scale.
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arXiv Machine Learning
Sep 15
SomBench: Benchmark Dataset for Advancing Machine Learning in Lunar Science
arXiv:2609.13277v1 Announce Type: cross Abstract: Lunar orbital missions, such as Lunar Reconnaissance Orbiter, Kaguya/SELENE, Gravity Recovery and Interior Laboratory, and Lunar Prospector, among ot...
arXiv Computer Vision
Sep 3
Vision-Language Model for Accurate Crater Detection
The paper presents a deep‑learning crater detection algorithm (CDA) based on the OWLv2 Vision Transformer, fine‑tuned with Low‑Rank Adaptation on a manually labeled IMPACT dataset. It optimizes a combined loss of CIoU for localization and contrastive loss for classification, achieving a maximum recall of 92.6% and precision of 71.4% on lunar images. The method demonstrates reliable crater detection under varied illumination and rugged terrain, supporting safer lunar landings.
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 Machine Learning
Jun 16
Deep Learning-Based Lunar Crater Terrain Relative Navigation
arXiv:2606. 14776v1 Announce Type: cross Abstract: Accurate position estimation is crucial for the successful implementation of future lunar landings using autonomous vehicles, especially in dangerous environments with sparse terrain features.
arXiv Machine Learning
Jul 27
LunarFM: A Shared Multimodal Representation of the Moon's Surface
arXiv:2607. 22408v1 Announce Type: new Abstract: The renewed global focus on lunar exploration, driven by the prospect of in-situ resource utilization and a sustained human presence on the Moon, has created growing demand for accurate, large-scale characterization of the lunar surface.
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 Machine Learning
Jul 21
VAE with Hyperspherical Coordinates: Improving Anomaly Detection from Hypervolume-Compressed Latent Space
arXiv:2601. 18823v4 Announce Type: replace Abstract: Variational autoencoders (VAE) encode data into lower-dimensional latent vectors before decoding those vectors back to data.
arXiv AI
Jul 22
A Self-Supervised Framework for Space Object Behaviour Characterisation
arXiv:2504. 06176v4 Announce Type: replace-cross Abstract: Foundation Models, which leverage large neural networks pre-trained on unlabelled data before fine-tuning for specific tasks, are increasingly being applied to specialised domains.
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 Machine Learning
Aug 11
Verifiably grounded machine interpretation of lunar geology
arXiv:2608. 09276v1 Announce Type: cross Abstract: Planetary geology relies on historical, interpretive reasoning to reconstruct past events from diverse observations.
Hugging Face Trending Papers
Jun 9
Globally Localizing Lunar Rover in Pixels via Graph Alignment
Precise rover localization is a prerequisite for autonomous lunar exploration, yet the absence of Global Navigation Satellite System (GNSS) signals and the cumulative drift of local localization methods severely constrain long-range missions. Cross-view localization provides a promising drift-free global solution by matching rover-view and satellite-view imagery.