---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: mask
    dtype: image
  splits:
  - name: train
    num_bytes: 93594923
    num_examples: 790
  download_size: 93617398
  dataset_size: 93594923
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- image-segmentation
tags:
- geodata
- satellite
- sentinel2
- ESA
pretty_name: Simple Satelite Segmentation (Norway)
size_categories:
- n<1K
---

# Satellite Segmentation

Summary
-------
This dataset contains paired Sentinel-2 RGB tiles and corresponding land-cover masks (derived from ESA WorldCover) prepared for semantic segmentation. Each example has:
- `image`: RGB image (PNG)
- `mask`: integer-labelled mask (PNG, uint8)

Key details
-----------
- Source imagery: Sentinel-2 L2A via Microsoft Planetary Computer (STAC)
- Land-cover masks: ESA WorldCover (derived)
- Spatial resolution: 10 m (aligned to Sentinel-2 grid)
- CRS: EPSG:4326 
- Number of samples: 790
- Train/validation split: Train

Provenance & license
--------------------
This dataset was derived from third‑party datasets:
- Sentinel‑2 (Copernicus)
- ESA WorldCover 

The user of this dataset must respect the original licenses and terms of use. The repository contains derived files (tiles). 

Data format
-----------
- Images: PNG, RGB, 3 channels
- Masks: PNG, integer values representing classes (do not normalize/convert to RGB)
- Filenames: `{prefix}.png` and `{prefix}_mask.png` (paired by prefix)

How to load
-----------
Example (datasets library):

```python
from datasets import load_dataset
ds = load_dataset("nikolkoo/SatelliteSegmentation")
```

Example evaluation/training snippet
-----------------------------------
Use CrossEntropyLoss with logits and integer masks:

```python
# pseudocode
images = batch["image"]  # (B,H,W,3) -> to tensor & permute
masks  = batch["mask"]   # (B,H,W) ints
logits = model(images)
loss = torch.nn.CrossEntropyLoss()(logits, masks)
```

Citation
--------
If you publish results using this dataset, cite the original data providers (Copernicus / ESA / Microsoft Planetary Computer) and this dataset repo.

Contact
-------
Feel free to add a comment in the Community 🤗