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OmniCloudMask Combined Training Dataset
A combined multi-source dataset for training cloud and cloud shadow segmentation models on Sentinel-2 satellite imagery. The dataset contains 103,548 image-label pairs (100,528 training + 1,070 validation + 1,950 test) drawn from 4 source datasets: CloudSEN12, Kappaset, OCM hard negative, and OCM scribble. CloudSEN12 is represented in several variants (different processing levels, super-resolution, and re-downloaded imagery) to improve model generalisation.
This dataset was used to train the v4 weights of the OmniCloudMask model from Training sensor-agnostic deep learning models for remote sensing: Achieving state-of-the-art cloud and cloud shadow identification with OmniCloudMask . The spatial distrobution of this dataset is shown in this map.
Sentinel-2 Bands
Each image contains 3 spectral bands stored as a 3-channel GeoTIFF:
| Channel | Sentinel-2 Band | Description | Native GSD |
|---|---|---|---|
| 0 | B04 | Red | 10 m |
| 1 | B03 | Green | 10 m |
| 2 | B8A | NIR Narrow | 20 m (upsampled to 10 m) |
Label Classes
4 semantic classes plus an ignore value:
| Value | Class | Description |
|---|---|---|
| 0 | Clear | No cloud or shadow |
| 1 | Thick Cloud | Opaque cloud |
| 2 | Thin Cloud | Semi-transparent cloud |
| 3 | Cloud Shadow | Shadow cast by clouds |
| 99 | No-data | Ignore during training |
Storage Format
The dataset is stored as Parquet shards. Each row contains one image-label pair with the following columns:
| Column | Type | Description |
|---|---|---|
subset |
string | Source sub-dataset name (e.g. "CloudSEN12 high") |
processing_level |
string | "L1C", "L2A", or "" |
image_filename |
string | Original filename for traceability |
label_filename |
string | Original label filename |
image |
binary | Raw GeoTIFF bytes (3-band, uint16, LZW compressed) |
label |
binary | Raw GeoTIFF bytes (1-band, uint8, LZW compressed) |
GeoTIFF Details
- Image dtype:
uint16— standard Sentinel-2 encoding (reflectance × 10,000, e.g. values in the 0–10,000+ range) - Label dtype:
uint8 - Geolocation: CRS and affine transform preserved inside each GeoTIFF (UTM projections, WGS84 datum). Exception: Kappaset images are not georeferenced.
Kappaset note: The original Kappaset NetCDF files store band values normalised by 65,535 (uint16 max). During conversion to GeoTIFF, values are multiplied by 65,535 to restore standard Sentinel-2 DN scale.
Usage Example
import io
import rasterio
from datasets import load_dataset
ds = load_dataset("NickWright/OmniCloudMask-Combined-Training-Dataset", split="train")
row = ds[0]
with rasterio.open(io.BytesIO(row["image"])) as src:
image = src.read() # shape: (3, H, W), dtype: uint16
crs = src.crs # e.g. EPSG:32719
with rasterio.open(io.BytesIO(row["label"])) as src:
label = src.read(1) # shape: (H, W), dtype: uint8
Image Sizes
| Pixel Dimensions | Approximate Ground Coverage | Datasets |
|---|---|---|
| 509 x 509 px | 5.09 x 5.09 km | CloudSEN12 high, scribble, validation, test, Planetary Computer, super res tiles, Kappaset, OCM hard negative, OCM scribble |
| 1018 x 1018 px | 5.09 x 5.09 km (5 m) | CloudSEN12 super res raw |
| 2000 x 2000 px | 20 x 20 km | CloudSEN12 2k |
Why 509 instead of 512? The CloudSEN12 dataset — the largest and highest-quality source in this collection — uses 509 x 509 px tiles. To maintain consistency, all other datasets adopt the same dimensions. Kappaset images (originally 512 x 512 px) are clipped to 509 x 509 px to match.
Dataset Sources
CloudSEN12 High (16,980 images — 8,490 L1C + 8,490 L2A)
High-quality dense pixel-wise labels from the CloudSEN12 dataset. Includes both L1C (top-of-atmosphere) and L2A (surface reflectance) processing levels for each scene, sharing the same label mask.
- Source: TACO Foundation on HuggingFace (
cloudsen12-l1c,cloudsen12-l2a) - Size: 509 x 509 px
- Label type: Dense, human-annotated
- Split used: Train only
CloudSEN12 Scribble (20,000 images — 10,000 L1C + 10,000 L2A)
Sparse scribble annotations covering all splits. Original 7 classes remapped to 4:
| Original | Remapped | Meaning |
|---|---|---|
| 0 | 0 | Clear |
| 1, 2 | 1 | Thick Cloud |
| 3, 4 | 2 | Thin Cloud |
| 5, 6 | 3 | Cloud Shadow |
| 99 | 99 | No-data |
- Source: TACO Foundation on HuggingFace
- Size: 509 x 509 px
- Label type: Sparse scribble annotations (most pixels are 99/no-data)
- Splits used: Train + Val + Test
CloudSEN12 2k (1,694 images — 847 L1C + 847 L2A)
Larger tiles from CloudSEN12 with dense labels. Both L1C and L2A processing levels.
- Source: TACO Foundation on HuggingFace
- Size: 2000 x 2000 px
- Label type: Dense, human-annotated
- Splits used: Train + Val + Test
CloudSEN12 Planetary Computer (8,403 images — L2A only)
The same scenes as CloudSEN12 high, but the L2A imagery was re-downloaded from Microsoft Planetary Computer. Labels are identical to CloudSEN12 high. This provides imagery processed through a different atmospheric correction pipeline, improving model generalisation.
- Source: Microsoft Planetary Computer STAC API,
sentinel-2-l2acollection - Size: 509 x 509 px
- Label type: Dense, human-annotated (same labels as CloudSEN12 high)
- Processing level: L2A only
- Note: ~87 scenes could not be matched on Planetary Computer and were skipped
CloudSEN12 Super Resolution Tiles (33,960 images — L1C only)
Derived from CloudSEN12 high L1C train images using a 2x ESRGAN super-resolution model. Each 509x509 source image is upscaled to 1018x1018 px (~5 m effective resolution), then split into a 2x2 grid of 509x509 tiles. Labels are pixel-repeated to match.
Colour statistics (mean, std) are transferred from the original image back to the super-resolved output to preserve radiometric consistency.
- Super-resolution model:
Phips/2xNomosUni_esrgan_multijpg - Size: 509 x 509 px (4 tiles per source image)
- Label type: Dense (pixel-repeated from original)
- Processing level: L1C only
CloudSEN12 Super Resolution Raw (8,490 images — L1C only)
Same super-resolution pipeline as above, but stored as full 1018x1018 px images (not tiled).
- Size: 1018 x 1018 px
- Label type: Dense (pixel-repeated from original)
- Processing level: L1C only
Kappaset (9,250 images — L1C only)
An independent cloud labelling dataset converted from NetCDF to GeoTIFF. Original 6 classes remapped to 4:
| Original | Remapped | Meaning |
|---|---|---|
| 0 | 99 | No-data |
| 1 | 0 | Clear |
| 2 | 3 | Cloud Shadow |
| 3 | 2 | Thin Cloud |
| 4 | 1 | Thick Cloud |
| 5 | 99 | No-data |
- Source: Zenodo record 7100327
- Size: 509 x 509 px
- Label type: Dense, human-annotated
- Processing level: L1C only
OCM hard negative (920 images — L2A only)
Cloud-free scenes that the model previously misclassified as cloudy. All labels are entirely class 0 (clear). These scenes were specifically curated to include cloud-like surfaces (snow, sand, haze, bright surfaces).
- Source: Microsoft Planetary Computer (
sentinel-2-l2a), custom curated - Size: 509 x 509 px
- Label type: All-zero masks (every pixel = clear)
- Processing level: L2A
- Scene dates: 2018–2024, global coverage
OCM scribble (831 images — L2A only)
Custom scribble-annotated scenes downloaded from Planetary Computer, targeting scenarios underrepresented in CloudSEN12 and Kappaset.
- Source: Microsoft Planetary Computer (
sentinel-2-l2a), custom curated - Size: 509 x 509 px
- Label type: Sparse scribble annotations
- Processing level: L2A
CloudSEN12 Validation (1,070 images — 535 L1C + 535 L2A)
Held-out validation set with dense labels. Used only for evaluation, never for training.
- Source: TACO Foundation on HuggingFace
- Size: 509 x 509 px
- Label type: Dense, human-annotated
- Processing levels: L1C and L2A
CloudSEN12 Test (1,950 images — 975 L1C + 975 L2A)
Held-out test set with dense labels. Used only for final evaluation, never for training or validation.
- Source: TACO Foundation on HuggingFace
- Size: 509 x 509 px
- Label type: Dense, human-annotated
- Processing levels: L1C and L2A
Image Count Summary
| Dataset | Images | L1C | L2A | Role |
|---|---|---|---|---|
| CloudSEN12 high | 16,980 | 8,490 | 8,490 | Train |
| CloudSEN12 scribble | 20,000 | 10,000 | 10,000 | Train |
| CloudSEN12 2k | 1,694 | 847 | 847 | Train |
| CloudSEN12 high planetary computer | 8,403 | — | 8,403 | Train |
| CloudSEN12 high super res tiles | 33,960 | 33,960 | — | Train |
| CloudSEN12 high super res raw | 8,490 | 8,490 | — | Train |
| Kappaset | 9,250 | 9,250 | — | Train |
| OCM Hard negative | 920 | — | 920 | Train |
| OCM scribble | 831 | — | 831 | Train |
| CloudSEN12 validation | 1,070 | 535 | 535 | Val |
| CloudSEN12 test | 1,950 | 975 | 975 | Test |
| Total | 103,548 | 72,547 | 31,001 |
Dataset Weights
Each sub-dataset is assigned a loss weight during training to reflect label quality and reliability:
| Dataset | Weight |
|---|---|
| CloudSEN12 high | 1.0 |
| CloudSEN12 scribble | 1.0 |
| CloudSEN12 2k | 0.8 |
| CloudSEN12 high super res tiles | 1.1 |
| CloudSEN12 high super res raw | 1.0 |
| CloudSEN12 high planetary computer | 1.0 |
| Kappaset | 0.2 |
| OCM Hard negative | 0.7 |
| OCM scribble | 1.1 |
Citations
If you use this dataset, please cite the original sources:
- CloudSEN12: Aybar, C., et al. "CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2." Sci Data, 2022. Paper | Project page | Dataset
- Kappaset: Domnich, M., et al. Paper | Dataset.
- OCM hard negative & OCM scribble: Custom datasets created for this work.
- Sentinel-2 imagery: Copernicus Sentinel data, processed by ESA and Microsoft Planetary Computer.
License
Please refer to the individual source dataset licenses:
- CloudSEN12: Creative Commons Zero v1.0 Universal
- Kappaset: See Creative Commons Attribution 4.0 International
- OCM hard negative & OCM scribble: Creative Commons Zero v1.0 Universal
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