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 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 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 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.
arXiv Machine Learning
Aug 4
Using Seismic Statistical Features and VQ-VAE to Improve Spatiotemporal Seismicity Predictability
arXiv:2606. 10069v3 Announce Type: replace Abstract: In this paper we build upon a previous study in which we demonstrated, using XGBoost and earthquake catalogue data from Japan and Chile, that a set of 60 seismic statistical features (SSFs) had much greater predictive value than a set of 428 generic time series features from the tsfresh package.
arXiv Machine Learning
Jun 10
Spatiotemporal Seismic Hazard Assessment Using VQ-VAE and Seismic Statistical Features
arXiv:2606. 10069v1 Announce Type: new Abstract: In this paper we build upon a previous study in which we demonstrated, using XGBoost and earthquake catalogue data from Japan and Chile, that a set of 60 seismic statistical features (SSFs) had much greater predictive value than a set of 428 generic time series features from the tsfresh package.
arXiv Machine Learning
Jun 4
Identifying Gems from Roman RAPIDly
arXiv:2606. 05103v1 Announce Type: new Abstract: The Nancy Grace Roman Space Telescope (Roman), set for launch as early as September 2026, will conduct wide-field infrared imaging surveys with unprecedented spatial resolution and cadence, enabling the discovery of millions of astronomical transients.
arXiv Machine Learning
Jun 17
AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning
arXiv:2505. 03509v3 Announce Type: replace Abstract: Anomaly detection in large datasets is essential in astronomy and computer vision.