arXiv:2608. 09350v1 Announce Type: cross Abstract: The Lunar Reconnaissance Orbiter (LRO) has been collecting high-resolution images (at around 0.
By Cameron Kelahan, Daniel Angerhausen, Adam Lesnikowski, Valentin T. Bickel
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.
By Ayush Prasad, Swarnalee Mazumder
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.
By Batu Candan, Simone Servadio
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.
By Marc Girona-Mata, Jakob Gawlikowski, Sumit Goski, Gautier Bardi de Fourtou, Valentin T. Bickel, Ben Moseley, Abigail Calzada-Diaz, Sylvester Kaczmarek, Ra\'ul Ramos-Poll\'an
arXiv:2601. 18823v4 Announce Type: replace Abstract: Variational autoencoders (VAE) encode data into lower-dimensional latent vectors before decoding those vectors back to data.
By Alejandro Ascarate, Leo Lebrat, Rodrigo Santa Cruz, Clinton Fookes, Olivier Salvado
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.
By Ian Groves, Andrew Campbell, James Fernandes, Diego Ram\'irez Rodr\'iguez, Paul Murray, Massimiliano Vasile, Victoria Nockles