SomBench: Benchmark Dataset for Advancing Machine Learning in Lunar Science

2026-09-07

AI Summary

为解决月球科学中机器学习的可重复性问题，创建了包含30多个层的统一数据集SomBench，并通过ResNet-50和SwinV2-B模型验证了其有效性。

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.

Problem

• Lunar Science
• Machine Learning
• Heterogeneity
• Reproducibility
• Multi-instrument Observations

Innovation

• spatially-aligned
• ML-ready dataset
• image-anchored tiling pipeline