juliensimon/asterank-asteroid-mining

Asterank Asteroid Mining Economics

Part of the Orbital Mechanics Datasets collection on Hugging Face.

Economic analysis of 600,000 asteroids for space mining potential, combining NASA/JPL orbital data with estimated accessibility and resource value from the Asterank project.

Dataset description

Asterank ranks nearly 600,000 cataloged asteroids by estimated mining profitability. It combines multiple data sources -- NASA/JPL Small-Body Database orbital elements, spectral classifications, and published scientific papers on asteroid composition -- to estimate each asteroid's resource value and the cost of reaching it.

Key economic fields:

• estimated_value_usd -- total estimated resource value based on spectral type and size
• estimated_profit_usd -- value minus estimated mission cost (delta-v dependent)
• closeness_score -- accessibility metric (lower delta-v = higher closeness)
• asterank_score -- composite ranking combining value, profit, and accessibility

Schema

Column	Type	Description
full_name	string	Full formatted name (e.g. "1 Ceres")
name	string	IAU name if assigned (e.g. "Ceres")
designation_number	int	Permanent designation number
provisional_designation	string	Provisional designation (e.g. "2024 YR4")
orbit_class	string	Orbital class (MBA, APO, ATE, AMO, etc.)
spectral_type_smassii	string	SMASS II spectral classification
spectral_type_bus	string	Bus (Tholen-like) spectral classification
spectral_type_tholen	string	Tholen spectral classification
is_neo	bool	Near-Earth Object flag
is_pha	bool	Potentially Hazardous Asteroid flag
absolute_magnitude	float64	Absolute magnitude H
magnitude_slope	float64	Magnitude slope parameter G
diameter_km	float64	Measured diameter (km)
diameter_sigma_km	float64	Diameter uncertainty (km)
albedo	float64	Geometric albedo
extent_km	string	Tri-axial extents (km)
rotation_period_h	float64	Rotation period (hours)
gm_km3_s2	float64	GM gravitational parameter (km^3/s^2)
semi_major_axis_au	float64	Semi-major axis (AU)
eccentricity	float64	Orbital eccentricity
inclination_deg	float64	Orbital inclination (degrees)
ascending_node_deg	float64	Longitude of ascending node (degrees)
argument_perihelion_deg	float64	Argument of perihelion (degrees)
mean_anomaly_deg	float64	Mean anomaly (degrees)
perihelion_au	float64	Perihelion distance (AU)
aphelion_au	float64	Aphelion distance (AU)
orbital_period_yr	float64	Orbital period (years)
mean_motion_deg_day	float64	Mean motion (degrees/day)
tisserand_jupiter	float64	Tisserand parameter w.r.t. Jupiter
earth_moid_au	float64	Minimum orbit intersection distance to Earth (AU)
earth_moid_ld	float64	Earth MOID in Lunar Distances
jupiter_moid_au	float64	Minimum orbit intersection distance to Jupiter (AU)
estimated_value_usd	float64	Estimated total resource value (USD)
estimated_profit_usd	float64	Estimated mining profit (USD)
closeness_score	float64	Accessibility score (higher = easier to reach)
asterank_score	float64	Composite Asterank ranking score
orbit_condition_code	float64	JPL orbit condition code (0=best, 9=worst)
data_arc_days	float64	Observation arc length (days)
n_obs_used	float64	Number of observations used in orbit fit
first_obs_date	datetime	Date of first observation
last_obs_date	datetime	Date of last observation
orbit_rms	float64	Orbit fit RMS residual
color_index_bv	float64	B-V color index
color_index_ub	float64	U-B color index

Quick stats

• 600,000 asteroids ranked by mining economics
• 1,800 Near-Earth Objects, 0 Potentially Hazardous
• 595,200 with measured diameters, 600,000 with spectral types
• 7 distinct orbital classes
• Most valuable: 511 Davida (1903 LU) at $15,382,627,787,839,414,272 (profit: $1,064,010,723,169,747,328)
• Median estimated value: $0
• Total estimated value of all asteroids: $105,731,413,739,251,945,177,088

Usage

from datasets import load_dataset

ds = load_dataset("juliensimon/asterank-asteroid-mining", split="train")
df = ds.to_pandas()

# Top 20 most profitable asteroids
top_profit = df.nlargest(20, "estimated_profit_usd")[
    ["full_name", "orbit_class", "spectral_type_smassii",
     "estimated_value_usd", "estimated_profit_usd", "earth_moid_au"]
]

# Near-Earth asteroids sorted by profit
neo_mining = df[df["is_neo"] == True].nlargest(50, "estimated_profit_usd")

# Value distribution by orbit class
by_class = df.groupby("orbit_class")["estimated_value_usd"].agg(["count", "median", "sum"])
by_class = by_class.sort_values("sum", ascending=False)

# Accessible targets: low MOID + high profit
accessible = df[
    (df["earth_moid_au"] < 0.1) &
    (df["estimated_profit_usd"] > 1e9)
].sort_values("estimated_profit_usd", ascending=False)

Data source

Asterank by Ian Webster, combining data from NASA/JPL Small-Body Database, spectral survey data, and published asteroid composition models.

Related datasets

• neo-close-approaches -- NEO close approaches from NASA JPL
• space-track-satcat -- Full NORAD satellite catalog
• space-launch-log -- Global launch history

Pipeline

Source code: juliensimon/space-datasets

Citation

@dataset{asterank_mining,
  author = {Simon, Julien},
  title = {Asterank Asteroid Mining Economics},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/juliensimon/asterank-asteroid-mining},
  note = {Based on Asterank (asterank.com) asteroid mining economics data}
}

License

CC-BY-4.0