Datasets:
File size: 3,204 Bytes
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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)
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