juliensimon/mercury-crater-degradation

---
license: cc-by-4.0
pretty_name: "Mercury Crater Degradation (Kinczyk et al. 2020)"
language:
- en
description: "Degradation classification of 3,253 Mercury impact craters (D >= 40 km) from MESSENGER imagery. 5-class degradation scale from fresh to highly degraded."
task_categories:
- tabular-classification
tags:
- space
- mercury
- crater
- degradation
- planetary-science
- messenger
- open-data
- tabular-data
- parquet
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: data/mercury_crater_degradation.parquet
default: true
---

# Mercury Crater Degradation (Kinczyk et al. 2020)

<div align="center">
<img src="banner.jpg" alt="The Moon seen from Apollo 8, showing craters and surface detail" width="400">
<p><em>Credit: NASA/Apollo 8</em></p>
</div>

*Part of the [Planetary Science Datasets](https://huggingface.co/collections/juliensimon/planetary-science-datasets-69c2d4683bd6a66c34fb4af2) collection on Hugging Face.*

A global catalog of **3,253** Mercury impact craters (diameter >= 40 km) classified by morphological degradation state using MESSENGER imagery. Each crater is assigned a degradation class from 1 (freshest) to 5 (highly degraded), providing a relative chronology of Mercury's surface.

## Dataset description

Crater degradation is the primary tool for determining the relative ages of planetary surfaces where radiometric dating is unavailable. On Mercury, impact craters undergo progressive modification through subsequent impacts, seismic shaking, volcanic infilling, and regolith gardening. Fresh craters display sharp rims, prominent ejecta blankets, and well-defined interior structures, while highly degraded craters are barely distinguishable from the surrounding terrain.

Kinczyk et al. (2020) developed a 5-point degradation classification scheme calibrated across Mercury's entire surface using MESSENGER's global imaging coverage. The classification correlates with established stratigraphic systems (e.g., the Kuiperian, Mansurian, and Calorian periods) and provides constraints on the cratering flux history of the innermost planet.

This dataset complements the [Mercury Crater Database (Herrick et al. 2011)](https://huggingface.co/datasets/juliensimon/mercury-craters-herrick), which provides morphological classification of all 16,876 craters. Together, they enable studies of crater shape evolution as a function of degradation state.

## Degradation classes

| Class | Description | Count |
|:-----:|-------------|------:|
| 0 | unclassified | 3 |
| 1 | freshest (sharp rim, prominent ejecta) | 1,048 |
| 2 | fresh (well-defined rim, partial ejecta) | 1,300 |
| 3 | moderately degraded (subdued rim, no ejecta) | 521 |
| 4 | degraded (heavily subdued rim) | 219 |
| 5 | highly degraded (barely distinguishable rim) | 22 |
| 6 | unknown | 140 |

## Schema

| Column | Type | Description |
|--------|------|-------------|
| `crater_id` | int64 | Unique crater identifier (FID) |
| `longitude_deg` | float64 | Crater center longitude (degrees East) |
| `latitude_deg` | float64 | Crater center latitude (degrees North) |
| `diameter_km` | float64 | Crater rim-to-rim diameter (km) |
| `degradation_class` | int64 | Degradation state (1=freshest to 5=highly degraded) |
| `degradation_label` | string | Human-readable degradation description |
| `size_class` | string | Derived: small (<50 km), medium (50-100), large (100-300), giant (>300) |

## Quick stats

- **3,253** craters with degradation classification
- Diameter range: 40 -- 1539 km (median 61 km)
- All craters D >= 40 km on Mercury's surface

## Usage

```python
from datasets import load_dataset

ds = load_dataset("juliensimon/mercury-crater-degradation", split="train")
df = ds.to_pandas()

# Degradation class distribution
import matplotlib.pyplot as plt
df["degradation_class"].value_counts().sort_index().plot(kind="bar")
plt.xlabel("Degradation Class")
plt.ylabel("Count")
plt.title("Mercury Crater Degradation Distribution")
plt.show()

# Size vs degradation
df.boxplot(column="diameter_km", by="degradation_class")
plt.ylabel("Diameter (km)")
plt.title("Crater Size by Degradation Class")
plt.show()

# Map colored by degradation
plt.scatter(df["longitude_deg"], df["latitude_deg"],
c=df["degradation_class"], cmap="RdYlGn_r",
s=df["diameter_km"] / 20, alpha=0.6)
plt.colorbar(label="Degradation Class")
plt.xlabel("Longitude (°E)")
plt.ylabel("Latitude (°N)")
plt.title("Mercury Craters by Degradation State")
plt.show()
```

## Data source

Kinczyk, M.J., Prockter, L.M., Byrne, P.K., Susorney, H.C.M., and Chapman, C.R. (2020),
*A morphological evaluation of crater degradation on Mercury: Revisiting crater classification.*
Icarus, 341, 113637. [doi:10.1016/j.icarus.2020.113637](https://doi.org/10.1016/j.icarus.2020.113637)

Data: [Mendeley Data](https://data.mendeley.com/datasets/35nvbpfggx/1), CC-BY-4.0.

## Related datasets

- [Mercury Craters (Herrick 2011)](https://huggingface.co/datasets/juliensimon/mercury-craters-herrick) — 16,876 Mercury craters with morphology
- [Mars Craters (Robbins 2012)](https://huggingface.co/datasets/juliensimon/mars-craters-robbins) — 384K+ Mars craters
- [Lunar Craters (Robbins 2019)](https://huggingface.co/datasets/juliensimon/lunar-craters-robbins) — 2M+ Moon craters
- [IAU Planetary Nomenclature](https://huggingface.co/datasets/juliensimon/planetary-nomenclature) — Named features across all bodies

## Pipeline

Source code: [juliensimon/space-datasets](https://github.com/juliensimon/space-datasets)

## Support

If you find this dataset useful, please give it a ❤️ on the [dataset page](https://huggingface.co/datasets/juliensimon/mercury-crater-degradation) and share feedback in the Community tab! Also consider giving a ⭐ to the [space-datasets](https://github.com/juliensimon/space-datasets) repo.

## Citation

```bibtex
@dataset{mercury_crater_degradation,
author = {Simon, Julien},
title = {Mercury Crater Degradation (Kinczyk et al. 2020)},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/juliensimon/mercury-crater-degradation},
note = {Based on Kinczyk et al. (2020) via Mendeley Data}
}
```

## License

[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)