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ayushprd/Moonstone

Moonstone: A Multimodal Foundation Model Benchmark for Lunar Remote Sensing This repository contains the dataset for the paper Moonstone: A Multimodal Foundation Model and Benchmark for Lunar Remote Sensing. The official code is available at GitHub. A 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… See the full description on the dataset page: https://huggingface.co/datasets/ayushprd/Moonstone.

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Moonstone: A Multimodal Foundation Model Benchmark for Lunar Remote Sensing

This repository contains the dataset for the paper Moonstone: A Multimodal Foundation Model and Benchmark for Lunar Remote Sensing.

The official code is available at GitHub.

A 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: wacmorphology, elevation, slope, roughness · thermal: divinertbolmidnight, divinertempnight, rockabundance, christiansenfeature · spectralM3: m3{750,950,1000,1250,1580,2000,2817,2857} · gravity: grail{freeair,bouguer,uncertainty} · radar: minirf{cpr,s1} (log1p) · hapke: wachapke{415,566,604,689}nm · composition: clementineuvvis750nm, lpgrs{tio2,feo}

Benchmark tasks

[image]

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.