Dataset Viewer
Auto-converted to Parquet Duplicate
crater_id
stringlengths
10
10
latitude_deg
float64
-105.38
85.6
longitude_deg
float64
-180
180
diameter_km
float64
1
3.41k
depth_km
float64
-0.42
4.95
size_class
stringclasses
4 values
01-1-00003
79.257
-148.095
19.53
0.11
medium
01-1-00004
78.697
-152.84
4.23
null
small
01-1-00012
77.791
-173.416
8.54
0.21
medium
01-1-00013
76.655
-165.191
7.97
0.19
medium
01-1-00014
76.978
-164.378
20.96
1.03
large
01-1-00015
77.863
-162.461
6.56
0.11
medium
01-1-00017
75.721
-148.499
6.95
0.17
medium
01-1-00018
77.168
-145.679
51.08
1.74
large
01-1-00020
75.575
-140.947
1.19
null
small
01-1-00021
75.742
-140.218
7.02
0.16
medium
01-1-00022
76.555
-140.153
4.21
null
small
01-1-00026
75.999
-123.998
2.7
null
small
01-1-00027
76.084
-121.76
8.05
0.21
medium
01-1-00029
78.118
-119.831
15.81
0.62
medium
01-1-00031
75.848
-114.815
4.55
0.05
small
01-1-00033
76.253
-99.107
3.61
null
small
01-1-00035
78.085
-98.47
2.75
null
small
01-1-00036
76.424
-94.787
10.68
0.28
medium
01-1-00038
74.972
-90.968
3.04
0.13
small
01-1-00039
75.187
-116.198
3.65
0.02
small
01-1-00044
75.647
-167.078
3.37
null
small
01-1-00045
75.109
-167.032
1.57
null
small
01-1-00046
75.218
-170.341
2.72
null
small
01-1-00047
75.534
-174.692
1.55
null
small
01-1-00050
73.909
-173.603
28.42
1.41
large
01-1-00052
74.525
-167.471
1.6
null
small
01-1-00053
73.529
-166.946
1.73
null
small
01-1-00054
74.445
-164.413
4.41
0.07
small
01-1-00055
73.636
-161.655
6.35
0.08
medium
01-1-00056
73.593
-154.393
3.24
0.04
small
01-1-00057
74.157
-150.131
2.1
null
small
01-1-00058
74.097
-147.617
10.75
0.78
medium
01-1-00059
74.214
-142.058
11.83
0.74
medium
01-1-00061
73.834
-131.751
1.63
null
small
01-1-00062
73.584
-131.317
2.66
null
small
01-1-00063
74.33
-131.088
1.91
null
small
01-1-00064
73.955
-128.689
8.8
0.23
medium
01-1-00066
74.115
-121.477
2.65
null
small
01-1-00069
73.834
-111.924
2.65
null
small
01-1-00070
74.129
-106.586
4.28
null
small
01-1-00073
73.703
-105.24
3.02
null
small
01-1-00074
73.84
-101.777
11.81
0.27
medium
01-1-00075
74.509
-99.615
2.29
null
small
01-1-00076
73.52
-90.472
6.25
0.04
medium
01-1-00077
73.12
-93.627
6.8
0.09
medium
01-1-00078
72.455
-95.456
1.4
null
small
01-1-00079
72.163
-96.065
2.35
null
small
01-1-00081
72.889
-105.721
2.08
null
small
01-1-00082
73.073
-105.842
15.17
0.46
medium
01-1-00083
72.196
-107.638
4.28
0.04
small
01-1-00084
72.424
-112.432
11.19
0.5
medium
01-1-00085
72.404
-117.562
5.23
0.04
medium
01-1-00090
73.219
-127.159
3.08
0.01
small
01-1-00092
72.331
-128.415
1.68
null
small
01-1-00093
72.943
-131.53
1.74
null
small
01-1-00094
72.973
-132.28
2.66
null
small
01-1-00095
72.553
-133.627
4.13
0.04
small
01-1-00097
73.273
-136.515
9.96
0.31
medium
01-1-00099
72.96
-144.416
5.22
0.12
medium
01-1-00100
72.689
-144.644
1.95
null
small
01-1-00101
72.818
-145.263
1.96
null
small
01-1-00102
72.462
-145.925
8.69
0.35
medium
01-1-00105
73.085
-150.874
3.51
0.05
small
01-1-00106
72.546
-154.511
2.37
null
small
01-1-00107
72.771
-155.327
1.91
null
small
01-1-00108
72.21
-155.876
1.64
null
small
01-1-00109
72.238
-156.627
1.96
null
small
01-1-00110
72.092
-157.002
2.18
null
small
01-1-00111
72.457
-170.248
4.27
null
small
01-1-00113
72.424
-177.845
1.82
null
small
01-1-00114
70.977
-179.367
7.12
0.12
medium
01-1-00115
71.361
-175.059
2.23
null
small
01-1-00117
71.542
-168.348
5.77
0.09
medium
01-1-00118
70.881
-166.427
36.86
1.98
large
01-1-00120
71.13
-155.676
4.42
null
small
01-1-00123
71.304
-137.687
1.43
null
small
01-1-00124
71.707
-136.202
5.91
0.15
medium
01-1-00125
71.53
-136.052
1.73
null
small
01-1-00126
71.216
-133.199
1.92
null
small
01-1-00127
71.66
-133.071
1.35
null
small
01-1-00128
71.882
-132.607
3.69
null
small
01-1-00129
71.092
-132.799
3.73
0.04
small
01-1-00130
71.235
-129.838
1.56
null
small
01-1-00131
71.728
-129.892
1.19
null
small
01-1-00133
70.786
-120.386
1.19
null
small
01-1-00134
70.744
-119.828
1.45
null
small
01-1-00135
70.71
-119.895
1.53
null
small
01-1-00136
71.391
-117.988
2.66
null
small
01-1-00137
70.869
-116.66
1.71
null
small
01-1-00138
70.812
-115.542
1.38
null
small
01-1-00139
71.54
-114.936
3.87
0.07
small
01-1-00140
70.807
-114.051
2.42
null
small
01-1-00141
71.219
-112.774
3.18
null
small
01-1-00142
71.468
-111.739
2.69
null
small
01-1-00143
71.063
-111.108
10.52
0.49
medium
01-1-00144
71.395
-110.691
2.2
null
small
01-1-00145
71.762
-110.369
5.12
0.05
medium
01-1-00146
70.956
-105.381
7.27
0.24
medium
01-1-00147
71.382
-103.67
7.29
0.12
medium
01-1-00148
71.223
-102.023
1.67
null
small
End of preview. Expand in Data Studio

Lunar Crater Database (Robbins 2019)

Part of the Planetary Science Datasets collection on Hugging Face.

The definitive lunar impact crater database, containing 384,278 craters with diameter >= 1 km. Essential reference for Artemis mission planning and lunar surface studies.

Dataset description

This database was compiled by Stuart J. Robbins (2019) using Lunar Reconnaissance Orbiter (LRO) imagery and LOLA topography. It is the most comprehensive catalog of lunar impact craters, covering the entire lunar surface with consistent methodology.

Schema

Column Type Description
crater_id int64 Unique crater identifier
latitude_deg float64 Crater center latitude (degrees, planetocentric)
longitude_deg float64 Crater center longitude (degrees, 0-360 E)
diameter_km float64 Crater rim-to-rim diameter (km)
depth_km float64 Rim-to-floor depth (km)
floor_elevation_km float64 Floor elevation (km)
depth_rim_sd_km float64 Rim depth standard deviation (km)
size_class string Derived: small (<5 km), medium (5-20), large (20-100), giant (>100)

Quick stats

  • 384,278 total craters
  • Size distribution: 336,675 small, 36,690 medium, 10,602 large, 311 giant
  • Diameter range: 1.00 -- 3411.7 km

Usage

from datasets import load_dataset

ds = load_dataset("juliensimon/lunar-craters-robbins", split="train")
df = ds.to_pandas()

# Size distribution
import matplotlib.pyplot as plt
df["diameter_km"].hist(bins=100, log=True)
plt.xlabel("Diameter (km)")
plt.ylabel("Count")
plt.title("Lunar Crater Size Distribution")
plt.show()

# South pole region (Artemis-relevant)
south_pole = df[(df["latitude_deg"] < -80)]
print(f"Craters near south pole: {len(south_pole):,}")
plt.scatter(south_pole["longitude_deg"], south_pole["latitude_deg"],
            s=south_pole["diameter_km"], alpha=0.5)
plt.title("Lunar South Pole Craters")
plt.show()

Data source

Robbins, S.J. (2019), *A New Global Database of Lunar Impact Craters >1-2 km:

  1. Crater Locations and Sizes, Comparisons With Published Databases, and Global Analysis.* Journal of Geophysical Research: Planets, 124, 871-892. Distributed by USGS Astrogeology Science Center.

Pipeline

Source code: juliensimon/space-datasets

Support

If you find this dataset useful, please give it a ❤️ on the dataset page and share feedback in the Community tab! Also consider giving a ⭐️ to the space-datasets repo.

Citation

@dataset{lunar_craters_robbins,
  author = {Simon, Julien},
  title = {Lunar Crater Database (Robbins 2019)},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/juliensimon/lunar-craters-robbins},
  note = {Based on Robbins (2019) via USGS Astrogeology}
}

License

CC-BY-4.0

Downloads last month
38

Collections including juliensimon/lunar-craters-robbins