arXiv Machine Learning July 27, 2026 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

LunarFM: A Shared Multimodal Representation of the Moon's Surface

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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.

By Ayush Prasad, Swarnalee Mazumder

llms computer-vision multimodal benchmarks

arXiv AI

Jun 19

TerraMind: Large-Scale Generative Multimodality for Earth Observation

arXiv:2504. 11171v5 Announce Type: replace-cross Abstract: We present TerraMind, the first any-to-any generative, multimodal foundation model for Earth observation (EO).

By Johannes Jakubik, Felix Yang, Benedikt Blumenstiel, Erik Scheurer, Rocco Sedona, Stefano Maurogiovanni, Jente Bosmans, Nikolaos Dionelis, Valerio Marsocci, Niklas Kopp, Rahul Ramachandran, Paolo Fraccaro, Thomas Brunschwiler, Gabriele Cavallaro, Juan Bernabe-Moreno, Nicolas Long\'ep\'e

fine-tuning multimodal benchmarks

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.

By Cameron Kelahan, Daniel Angerhausen, Adam Lesnikowski, Valentin T. Bickel

Hugging Face Trending Papers

Aug 10

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.

arXiv AI

Jun 12

Emerging Flexible Designs for Geospatial Multimodal Foundation Models

arXiv:2606. 12595v1 Announce Type: cross Abstract: Foundation models are rapidly transforming Earth observation by enabling scalable pretraining across diverse unlabeled geospatial modalities.

By Philipe Dias, Waqwoya Abebe, Abhishek Potnis, Aristeidis Tsaris, Dan Lu, Xiao Wang, Dalton Lunga

computer-vision multimodal benchmarks safety

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.

By Tom Sander, Kay Wohlfarth, Christian W\"ohler

rag multimodal

Related stories

Moonstone: A Multimodal Foundation Model and Benchmark for Lunar Remote Sensing

TerraMind: Large-Scale Generative Multimodality for Earth Observation

A Machine Learning Based Search for Lunar Anomalies

A Machine Learning Based Search for Lunar Anomalies

Emerging Flexible Designs for Geospatial Multimodal Foundation Models

Verifiably grounded machine interpretation of lunar geology