ghosteau/minecraft-chunks

Minecraft Chunks

2,040 full Minecraft chunks (~200 million voxels) exported block-by-block from vanilla 1.21 worlds, collected to train 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 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 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):

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:

from datasets import load_dataset

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

One chunk as a 3D array:

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
• Training pipeline, plugin source, and fine-tuning notebook: 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.