Moonstone: A Multimodal Foundation Model Benchmark for Lunar Remote Sensing

28-channel, 128 pixels-per-degree (~237 m/pixel) global multimodal lunar dataset assembled from seven instrument families across five missions (LRO WAC/LOLA/Diviner/Mini-RF, Chandrayaan-1 M3, GRAIL, Lunar Prospector GRS, Clementine). All channels are aligned to a common equirectangular grid (46,080 x 23,040 px, lunar sphere a=b=1,737,400 m) and organized into 7 physical modality groups (surface, thermal, spectral_M3, gravity, radar, hapke, composition).

Contents

Path	Description
aligned/	28 source-of-truth GeoTIFFs at 128 ppd (+ M3 geometry, crater_mask)
mmap/	Pre-normalized (z-scored) memory-mapped float32 arrays for pretraining (unlimited random crops) + NaN masks + channel_index.json
lunar_patches_v4.h5	16,200 patches (180x90 grid) x 28 x 256 x 256, with geology/age/mare metadata and fixed 70/15/15 split, for downstream evaluation
channel_stats.json	Per-channel (mean, std) normalization statistics (200 random 256x256 windows)

Channels (28)

surface: wac_morphology, elevation, slope, roughness · thermal: diviner_tbol_midnight, diviner_temp_night, rock_abundance, christiansen_feature · spectral_M3: m3_{750,950,1000,1250,1580,2000,2817,2857} · gravity: grail_{freeair,bouguer,uncertainty} · radar: minirf_{cpr,s1} (log1p) · hapke: wac_hapke_{415,566,604,689}nm · composition: clementine_uvvis_750nm, lpgrs_{tio2,feo}

Benchmark tasks

Six downstream tasks define the benchmark. Geology (49-class), Age (5-class), Composition (FeO and TiO2 regression), Cross-modal thermal prediction, Mare and highlands segmentation, Crater (over 10 km) segmentation.

Provenance

All data derived from public NASA PDS / USGS / ODE archives. Built via the 15-step pipeline in the Moonstone code repository (steps 01-15 + fix_minirf). Normalization: z-score, NaN->0 after norm.