Datasets:
license: cc-by-4.0
task_categories:
- image-classification
- image-segmentation
tags:
- lunar
- moon
- remote-sensing
- multimodal
- foundation-model
- planetary-science
pretty_name: Moonstone
size_categories:
- 10K<n<100K
configs:
- config_name: benchmark
data_files: benchmark/*
- config_name: pretraining
data_files: pretraining/mmap/*
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).
Repository layout
The dataset is split into two independent parts so you can download only what you need.
pretraining/ — self-supervised pretraining data
| Path | Description |
|---|---|
pretraining/aligned/ |
28 source-of-truth instrument GeoTIFFs at 128 ppd (+ M3 geometry) |
pretraining/mmap/ |
Pre-normalized (z-scored) memory-mapped float32 arrays for pretraining (unlimited random crops) + NaN masks + channel_index.json |
pretraining/channel_stats.json |
Per-channel (mean, std) normalization statistics (200 random 256x256 windows) |
benchmark/ — downstream evaluation data
| Path | Description |
|---|---|
benchmark/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 |
benchmark/geologic_units.tif, benchmark/geologic_units.json |
USGS geologic-unit label raster + class map (geology + age tasks) |
benchmark/mare_mask.tif |
Mare vs. highlands ground truth (segmentation) |
benchmark/crater_mask.tif |
Crater (over 10 km) ground truth (segmentation) |
Downloading
Download just one part (each is independent):
from huggingface_hub import snapshot_download
# Benchmark only (evaluation data + labels, ~74 GB)
snapshot_download("ayushprd/Moonstone", repo_type="dataset",
allow_patterns="benchmark/*", local_dir="Moonstone")
# Pretraining only (z-scored arrays + source GeoTIFFs, ~207 GB)
snapshot_download("ayushprd/Moonstone", repo_type="dataset",
allow_patterns="pretraining/*", local_dir="Moonstone")
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.
Code
Pretraining and benchmark code (MG-MAE model, the 15-step data pipeline, and downstream evaluation for all six tasks): https://github.com/ayushprd/Moonstone
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.