SomBench: Benchmark Dataset for Advancing Machine Learning in Lunar Science

• URL: http://arxiv.org/abs/2609.13277v1
• Date: Tue, 08 Sep 2026 02:58:32 GMT
• Title: SomBench: Benchmark Dataset for Advancing Machine Learning in Lunar Science
• Abstract summary: We introduce SomBench, a unified, spatially-aligned, ML-ready lunar dataset aggregating 30+ co-registered layers from ten instruments across four missions.<n>An application benchmark suite spans impact processes, volcanic history, and polar volatiles.<n> Baseline experiments with ResNet-50 and SwinV2-B models confirm that each benchmark task is learnable from the released inputs.
• Score: 4.888910677829709
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 caps. An image-anchored tiling pipeline yields pretraining-ready multimodal tile views with leakage-safe splits, distributed as netCDF with Parquet catalogs. An application benchmark suite spans impact processes, volcanic history, and polar volatiles. Baseline experiments with ResNet-50 and SwinV2-B models confirm that each benchmark task is learnable from the released inputs, establishing reference points for future model development.

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