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README.md

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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/
+├── train_set/     # 22,266 triplets (images / labels / labels_RGB), ~665 GB
+├── test_set/      # 100 triplets, ~2.4 GB
+└── extra_set/     # additional splits (train/val/test), ~3.0 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)
+
+## 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)