---
license: mit
pretty_name: Minecraft Chunks
tags:
- minecraft
- terrain-generation
- voxel
- procedural-generation
- 3d
- games
task_categories:
- other
size_categories:
- 100M<n<1B
configs:
- config_name: default
  data_files:
  - split: train
    path: chunks/*.csv
---

# Minecraft Chunks

**2,040 full Minecraft chunks (~200 million voxels) exported block-by-block from vanilla 1.21 worlds**, collected to train [STEVE-1](https://huggingface.co/ghosteau/STEVE-1), a style-conditioned terrain-generation VAE.

Each CSV file is one complete chunk: a 16 x 384 x 16 voxel column from bedrock (Y = -64) to build limit (Y = 319), one row per voxel, 98,304 rows per file.

## Schema

| Column | Type | Description |
|---|---|---|
| `x`, `z` | int | Block position within the chunk (0-15) |
| `y` | int | World height (-64 to 319) |
| `ChunkBiome` | str | Biome at the chunk's center column (one value per chunk) |
| `Biome` | str | Biome at this exact block |
| `Block_ID` | str | Bukkit material name (`STONE`, `GRASS_BLOCK`, `AIR`, ...) |
| `Is_Surface` | bool | Whether this block is the highest non-air block of its column |
| `Light_Level` | float | Light level at this block |
| `Block_to_Left` / `_Right` / `_Below` / `_Above` / `_in_Front` / `_Behind` | str | Material names of the 6 face-neighbours |

**A note on the context columns.** `Is_Surface`, `Light_Level`, and the six neighbour columns describe *the terrain itself*. They are included for analysis and visualisation, but if you train a terrain **generator**, do not condition on them -- at generation time they do not exist yet (that is data leakage, and it is exactly the mistake that motivated rebuilding this project's pipeline). A generator should condition only on position, biome, and noise. See the [training pipeline](https://github.com/ghosteau/generative-terrain) for the leakage-free reference implementation, including a unit test that enforces it.

## Biome distribution

Chunk counts by `ChunkBiome` (2,040 total):

| Biome | Chunks | | Biome | Chunks |
|---|---|---|---|---|
| FOREST | 278 | | SUNFLOWER_PLAINS | 55 |
| PLAINS | 271 | | LUKEWARM_OCEAN | 54 |
| RIVER | 208 | | DESERT | 54 |
| DARK_FOREST | 194 | | BIRCH_FOREST | 45 |
| MANGROVE_SWAMP | 154 | | BAMBOO_JUNGLE | 38 |
| OLD_GROWTH_BIRCH_FOREST | 147 | | COLD_OCEAN | 30 |
| ERODED_BADLANDS | 112 | | SAVANNA | 30 |
| BADLANDS | 99 | | OCEAN | 11 |
| JUNGLE | 95 | | SWAMP | 5 |
| SPARSE_JUNGLE | 85 | | FLOWER_FOREST | 2 |
| BEACH | 72 | | TAIGA | 1 |

The distribution is imbalanced (worlds are mostly forest and plains); the reference pipeline compensates with a biome-balanced sampler at training time.

## How it was collected

A custom [PaperMC plugin](https://github.com/ghosteau/generative-terrain) reads chunks block-by-block on a live server and writes one CSV per chunk:

- `/grabchunkdata` -- the current chunk
- `/grabchunkarea <radius>` -- every chunk in a square radius (bulk collection)
- `/grabbiome <biome> <count>` -- spiral-search targeted collection of rare biomes
- `/grabblock <block> <count>` -- targeted collection of chunks containing a block

Filenames follow `<prefix>_<chunkX>_<chunkZ>.csv` (targeted collection inserts the biome or material name into the prefix).

## Usage

**With the reference pipeline** (recommended -- this is the format its `GT_DATA_DIR` expects):

```python
from huggingface_hub import snapshot_download

path = snapshot_download("ghosteau/minecraft-chunks", repo_type="dataset")
# then: set GT_DATA_DIR to <path>/chunks and run the training notebook
```

**As a plain table:**

```python
from datasets import load_dataset

ds = load_dataset("ghosteau/minecraft-chunks")   # ~200M rows
```

**One chunk as a 3D array:**

```python
import numpy as np
import pandas as pd

df = pd.read_csv("chunks/cluster1_-100_-1.csv")
grid = np.full((16, 384, 16), "", dtype=object)
grid[df["x"], df["y"] + 64, df["z"]] = df["Block_ID"]   # [X, Y, Z]
```

## Related

- Model trained on this data: [ghosteau/STEVE-1](https://huggingface.co/ghosteau/STEVE-1)
- Training pipeline, plugin source, and fine-tuning notebook: [github.com/ghosteau/generative-terrain](https://github.com/ghosteau/generative-terrain)

## License and attribution

MIT. The data consists of block coordinates, material identifiers, and biome labels exported from procedurally generated Minecraft worlds. Not affiliated with or endorsed by Mojang or Microsoft; "Minecraft" is a trademark of Mojang Synergies AB.