---
license: cc-by-nc-sa-4.0
task_categories:
- text-to-image
- image-to-image
language:
- en
tags:
- remote-sensing
- image-generation
- semantic-segmentation
- 4K
- high-resolution
size_categories:
- 10K<n<100K
---

# ControlEarth-4K

**ControlEarth-4K** is a large-scale, high-resolution remote sensing dataset for **controllable 4K (4096×4096) image generation**. Each sample is a triplet of a real aerial/satellite image, a grayscale semantic label map, and an RGB colorized label map, supporting layout-controllable generation, semantic synthesis, and related evaluation protocols.

## Dataset Structure

```
ControlEarth-4K/
├── core/          # Core release (recommended starting point)
│   ├── train/     # 22,266 triplets (images / labels / labels_RGB), ~665 GB
│   └── test/      # 100 triplets, ~2.4 GB
└── extended/      # Extended release (full version, additional splits)
    ├── train/     # ~1.6 TB
    ├── val/       # ~156 GB
    └── test/      # ~1.3 TB
```

Each split contains three aligned folders:
- `images/` — real remote sensing images (4096×4096, RGB)
- `labels/` — grayscale semantic label maps (4096×4096, uint8)
- `labels_RGB/` — colorized label maps for visualization (4096×4096, RGB)

## How to Extract Tar Shards

Large splits (`core/train` and all `extended/*`) are stored as tar shards (~12–29 GB each). Each shard internally contains the aligned `images/`, `labels/`, and `labels_RGB/` folders. After downloading the shards of a split, extract them in place:

```bash
for f in *.tar; do tar -xf "$f"; done
```

This yields the three folders with all samples merged. (`core/test` is provided as plain files for direct preview.)

## Semantic Categories

13 foreground classes plus an *unlabeled* category (pixel value 0, ignored in training and evaluation):

| ID | Class |
|----|---------------|
| 0 | unlabeled (ignore) |
| 1 | water |
| 2 | road |
| 3 | ship |
| 4 | car |
| 5 | aircraft |
| 6 | vegetation |
| 7 | bareland |
| 8 | baseball diamond |
| 9 | basketball court |
| 10 | tennis court |
| 11 | storage tank |
| 12 | building |
| 13 | background |

## Sources

Curated and harmonized from multiple public remote sensing benchmarks, including AIRS, FAIR1M, GID, GLH-Bridge, GLH-water, HRSCD, Inria Aerial Image Labeling, LandCover.ai, MiniFrance, Potsdam, SpaceNet 1/2, STAR, WHU-Building, and iSAID. All images are re-annotated/unified into the 13-class scheme at native 4096×4096 resolution.

## Intended Use

- Controllable remote sensing image generation (label-to-image)
- High-resolution (4K) generative model training and benchmarking
- Semantic layout fidelity and texture-detail evaluation (e.g., FID, KID, CLIP-I, PSNR/SSIM, and texture/detail fidelity metrics)

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{controlearth4k,
  title  = {ControlEarth-4K: A Large-scale High-resolution Dataset for Controllable Remote Sensing Image Generation},
  author = {Ye, Ziqi},
  year   = {2026},
  url    = {https://huggingface.co/datasets/conquer997/ControlEarth-4K}
}
```

**Paper** (coming soon)

**GitHub**: https://github.com/conquer997/ControlEarth (code coming soon)