| --- |
| 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): |
|
|
| ```python |
| 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. |
| |
