| --- |
| 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) |
|
|
