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SomBench: Benchmark Dataset for Advancing Machine Learning in Lunar Science
Lunar orbital missions, such as Lunar Reconnaissance Orbiter, Kaguya/SELENE, Gravity Recovery and Interior Laboratory, and Lunar Prospector, among others, provide rich multi-instrument observations, but their heterogeneity in sampling, projection, and conventions limits reproducible machine learning (ML). We introduce SomBench, a unified, spatially-aligned, ML-ready lunar dataset aggregating 30+ co-registered layers from ten instruments across four missions, spanning 1 meter to 20 kilometer/pixel and covering 82 degree latitude in 90 Lunar Transverse Mercator zones with two polar stereographic
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-08T02:58:32.000Z
First collected: 2026-09-20T20:22:01.598Z. This is not the publication date.