ABRUBAB/shadowastra-atlas

ShadowAstra Atlas: every land area on Earth, measured as a Moon or Mars practice site

Where on Earth can a team rehearse a Moon base or a Mars mission? We measured all 80,206 land areas on Earth (0.5° cells, about 55 km wide) with open NASA data, the same way we measured 18 Moon and Mars targets, and scored every place for every target. This dataset is everything behind the ShadowAstra Atlas website, built by Team ShadowAstra (Dhaka, Bangladesh) for the NASA Space Apps Challenge 2026: Identify Earth Locations that Analog the Permanent Moon Base Locations and Mars.

• Live site: https://shadowastra-atlas.vercel.app
• Code and pipeline: https://github.com/ABRUBAB/ShadowAstra_Atlas (pipeline/s21_open_dataset.py rebuilds these files)
• Every test, explained: https://shadowastra-atlas.vercel.app/methods

Files

File	Rows	What it holds
places.parquet	80,206	One row per land area: names, measurements in real units, NASA EMIT minerals, slope histogram, 0 to 1 indicators, the learned Moon and Mars scores and a confidence level
target_scores.parquet	1,443,708	One row per land area and target: the match score as the website shows it for the target's main job, and each factor behind it
targets.csv, targets.json	18	The Moon and Mars targets: terrain at 50 km, main rocks, published water-made minerals, LRO Diviner, Lunar Prospector and Mars Odyssey measurements
ai_twins.parquet	648,000	The 5 closest Earth places for every 1° cell of the Moon and Mars, from our terrain AI
known_sites.csv	38	Analog sites used to train the learned model, with the jobs they were used for
validation_sites.csv	28	Published analog sites never used for training, kept as an untouched test
validation.json		Every test result shown on the How it works page

Join target_scores and ai_twins (earth_cell) to places on cell.

Key columns

places

• cell, lat, lon: the centre of the 0.5° cell. country, region, nearest_town, nearest_town_km from Natural Earth.
• Terrain over a 50 km square (SRTM via AWS Terrain Tiles): elevation_m, height_range_50km_m (2nd to 98th percentile), mean_slope_deg, slope_share_<a>_<b>_deg (share of ground with slopes between a and b degrees).
• Climate (NASA POWER, MERRA-2 climatology): air_temp_*, rain_mm_per_year, humidity_pct, sunshine_kwh_m2_day.
• Surface (NASA GIBS, Terra MODIS 2024): ground_temp_hottest_day_c, ground_temp_coldest_night_c, plants_ndvi_max, bare_ground_share, snow_ice_share.
• Context: volcano_km and nearest_volcano (Smithsonian GVP Holocene volcanoes), impact_crater (Earth Impact Database), rock_type_glim (GLiM dominant class).
• emit_<clay|sulfate|carbonate|iron>_share: mineral share of the ground from NASA EMIT L3 (0.5°, v002). Empty where EMIT has no clear view (plants, water, snow, cloud, towns, or beyond ±52°); 16,868 places have values.
• indicator_*: the 0 to 1 transforms the scores use (dry, bare, cold, swing, airdry, hot, volcanic, impact, ice, remote, rugged).
• learned_moon, learned_mars: percentile rank (0 to 1) from the learned model, a rank-average of tuned XGBoost, tuned LightGBM and logistic regression trained positive-unlabelled on the 38 known sites. *_spread: how much its 24 single runs disagree. measured_share: share of measurements that are real, not gap-filled. confidence_*: high, medium or low from those two.
• known_site_within_150km, nearest_known_site_km: whether a known test site is nearby.

target_scores

• target, body, job (the target's main job, as the website picks it first).
• match_score = 40% factors_only + 60% learned, as the website shows by default.
• factors_only: the job-weighted mean of ground_shape (slope histograms and height range compared with the target at the same 50 km scale, LRO LOLA 118 m or MGS MOLA 463 m), rocks_craters, cold_heat, sunlight_angle (Sun-height match in the best month) and bare_dry_ground.
• ground_shape_ai: the terrain AI's similarity rank for the target (an option on the website, not in match_score).

targets: temp_avg_c, temp_max_c, ice_surface_share, ice_1m_share within 10 km of the seven south-pole Moon targets (LRO Diviner polar resource product, Paige et al.); temp_avg_c, temp_max_c and temp_min_c (coldest, before dawn) over about 45 km for the three Moon targets away from the pole, from the day-and-night curve in Diviner's global temperatures (Williams et al. 2017, PDS LRO-L-DLRE-5-GCP); feo_wt_pct, tio2_wt_pct (oxides by weight) and th_ppm for every Moon target from the Lunar Prospector gamma-ray spectrometer (Prettyman et al. 2006, about 150 km footprint); h2o_wt_pct, water-equivalent hydrogen in the topsoil at each Mars target (Mars Odyssey GRS, 5° map, Boynton et al. 2007); water_made_minerals from published CRISM, OMEGA, TES and rover results.

ai_twins: for each Moon or Mars cell (body, lat, lon), rank 1 to 5 and the Earth place (earth_cell, earth_lat, earth_lon). Made by a self-supervised vision transformer (DINO ViT-S/8) trained on Kaggle (2 × T4) with 50 km height maps from all three worlds and a domain-adversarial head.

How good is it?

Ranks are "top X% of all land": 1% means only 1 in 100 land areas scores higher.

Test	Result
Hidden known sites (each site and its neighbours within 300 km hidden, model retrained)	learned model top 1.2% (Moon 0.5%, Mars 2.4%); match_score top 2.4%
28 published sites never used for training	match_score top 17.8% (Moon 7.2%, Mars 25.8%); learned model alone 15%; a simple hand-made rule 40%
Scoring a site for the job it was really used for	ranks it higher for 78% of 78 sites (sign test p < 0.001)
Weights shifted by ±25%	9 of the top 10 places stay
Terrain AI, ground shape alone	known sites top 44% vs formula 62%; new sites 58% vs 71%; expert pairs 35% vs 34%

New data only changes the score if it ranks the known sites better. EMIT minerals did not (as a model input or as a Mars mineral term), so they are measurements here, not part of match_score. Details: validation.json and the How it works page.

Use it

import pandas as pd

base = "hf://datasets/ABRUBAB/shadowastra-atlas/"
places = pd.read_parquet(base + "places.parquet")
scores = pd.read_parquet(base + "target_scores.parquet", filters=[("target", "==", "jezero")])

best = scores.merge(places, on="cell").query("known_site_within_150km.isna()").nlargest(10, "match_score")
print(best[["region", "country", "lat", "lon", "match_score", "emit_clay_share", "emit_carbonate_share"]])

With 🤗 Datasets: load_dataset("ABRUBAB/shadowastra-atlas", "places").

Limits

• 55 km cells: many famous analog sites were chosen for something much smaller (an ice cave, a lake, a hot spring), so they can rank low here.
• Earth can never copy gravity, vacuum, radiation or lunar nights. The website shows these next to every score; this dataset scores only what Earth can copy.
• The Moon and Mars targets are 18 named places; ai_twins covers every 1° cell of both.
• Scores rank places for study. Visiting any site needs permission, local partners and care for the land; Antarctic sites need Treaty permits.

Sources and licence

This dataset is released under CC BY 4.0: please credit "Team ShadowAstra, ShadowAstra Atlas (NASA Space Apps Challenge 2026)" and the original sources: NASA POWER (LaRC); NASA GIBS and Terra MODIS; NASA EMIT L3 ASA v002 (JPL, LP DAAC, doi:10.5067/EMIT/EMITL3ASA.002); SRTM via AWS Terrain Tiles; LRO LOLA and MGS MOLA (USGS Astrogeology); LRO Diviner, Lunar Prospector GRS and Mars Odyssey GRS (PDS Geosciences); NASA MODIS water mask (MOD44W, via GIBS); Smithsonian Global Volcanism Program; Earth Impact Database (PASSC); GLiM (Hartmann and Moosdorf 2012, CC BY); Natural Earth (public domain). NASA does not endorse this project.

Team ShadowAstra, Dhaka

Abdullah Rubab (lead) · Tanvir Hossin (planetary terrain) · Md. Tanvir Hasan (scientific validation) · Mohammad Saniul Alam (experience design) · Tasnuva Arif (development and testing) · Mohammed Mahmud Parvez (film editing).

@misc{shadowastra2026,
  title  = {ShadowAstra Atlas: Earth analog sites for Moon and Mars missions},
  author = {Team ShadowAstra},
  year   = {2026},
  note   = {NASA Space Apps Challenge 2026, Dhaka},
  url    = {https://huggingface.co/datasets/ABRUBAB/shadowastra-atlas}
}