---
license: cc-by-4.0
tags:
  - semantic-segmentation
  - sentinel-2
  - kelp-mapping
  - pytorch
  - skema
---

# SKeMa: Satellite-based Kelp Mapping

## Model Description

SKeMa is a Python-based tool for semantic segmentation of kelp in Sentinel-2 satellite imagery, developed for coastal monitoring. It uses a deep learning model from the Segmentation Models PyTorch library, with a MaxViT-Tiny-512 encoder and UNet decoder. The model outputs a binary GeoTIFF (kelp = 1, non-kelp = 0).

**Authors**: Mohsen Ghanbari, Neil Ernst, Taylor A. Denouden, Luba Y. Reshitnyk, Piper Steffen, Alena Wachmann, Alejandra Mora-Sotoa, Eduardo Loos, Margot Hessing-Lewis, Nic Dedeluke, Maycira Costa  
**License**: Creative Commons Attribution 4.0 International (CC-BY-4.0)  
**Citation**: SKeMa models by Mohsen Ghanbari et al., SPECTRAL Remote Sensing Laboratory, University of Victoria, DOI: [10.57967/hf/6790](https://huggingface.co/m5ghanba/SKeMa)  
**GitHub**: [m5ghanba/skema](https://github.com/m5ghanba/skema)  
**PyPI**: [skema-kelp](https://pypi.org/project/skema-kelp/)

## Intended Use

- **Primary Use**: Mapping kelp in Sentinel-2 imagery for environmental research and industry applications.
- **Users**: Researchers, coastal ecologists, and industries analyzing Sentinel-2 data.
- **Out of Scope**: Non-Sentinel-2 imagery or non-kelp segmentation tasks.

## Model Architecture

- **Framework**: PyTorch, Segmentation Models PyTorch (SMP) library.
- **Encoder**: MaxViT-Tiny-512.
- **Decoder**: UNet.
- **Input Channels**: 12 (Sentinel-2 bands B2, B3, B4, B8, B5; substrate; bathymetry; slope; spectral indices with the order NDVI; NDWI; GNDVI; ClI; NDVIRE).
- **Model Types**:
  - `model_full`: Uses all 13 channels, including substrate, bathymetry, and slope (optimized for BC coast).
  - `model_s2bandsandindices_only`: Uses Sentinel-2 bands and indices only (no bathymetry/substrate/slope).

## Input Data

- **Required**: Sentinel-2 `.SAFE` folder (download from [Copernicus Browser](https://dataspace.copernicus.eu/)).
- **For `model_full`**: Bathymetry (`Bathymetry_10m.tif`) and substrate TIFFs (`NCC_substrate_20m.tif`, etc.) from BC coast sources (see GitHub README).

## Output Format

- Binary GeoTIFF (`output.tif`): Kelp = 1, non-kelp = 0.
- Intermediate files:
  - `<SAFE_name>_B2B3B4B8.tif`: 10m resolution, 4-band GeoTIFF (B02, B03, B04, B08).
  - `<SAFE_name>_B5B6B7B8A_B11B12.tif`: 20m resolution, 6-band GeoTIFF.
  - For `model_full`: `<SAFE_name>_Bathymetry.tif`, `<SAFE_name>_Substrate.tif`.

## Usage Instructions

### Installation via pip (Recommended)

```bash
pip install skema-kelp
```

### Installation from source

1. **Install**:
   ```bash 
   git clone https://github.com/m5ghanba/skema.git
   cd skema
   python -m venv skema_env
   source skema_env/bin/activate  # On Windows: skema_env\Scripts\activate
   pip install .
   ```
   See [GitHub README](https://github.com/m5ghanba/skema) for detailed setup.

2. **Run**:
   ```bash
   skema --input-dir path/to/sentinel2/safe/folder --output-filename output.tif --model-type model_full
   # Or for S2-only model:
   skema --input-dir path/to/sentinel2/safe/folder --output-filename output.tif --model-type model_s2bandsandindices_only
   
   # For help:
   skema --help
   ```

3. **Dependencies** (from `requirements.txt`):
   - PyTorch (GPU: `torch==2.1.0`, see GitHub for CUDA setup).
   - Others: `numpy`, `rasterio`, `segmentation-models-pytorch`.

## Training Data

- **Source**: Sentinel-2 imagery from Copernicus Browser, BC coast bathymetry/substrate data.
- **Region**: Optimized for British Columbia coastal waters.
- **Note**: Bathymetry/substrate files required for `model_full` are available online (see GitHub README).

## Limitations

- Optimized for BC coast; performance may vary elsewhere.
- `model_full` requires specific bathymetry/substrate data.
- Assumes Sentinel-2 `.SAFE` format input.

## Citation

Please cite as:

> SKeMa model by Mohsen Ghanbari et al., SPECTRAL Remote Sensing Laboratory, University of Victoria, DOI: [10.57967/hf/6790](https://huggingface.co/m5ghanba/SKeMa).

**BibTeX:**

```bibtex
@software{skema_2026,
  author       = {Mohsen Ghanbari, Neil Ernst, Taylor A. Denouden, Luba Y. Reshitnyk, Piper Steffen, Alena Wachmann, Alejandra Mora-Sotoa, Eduardo Loos, Margot Hessing-Lewis, Nic Dedeluke, Maycira Costa},
  title        = {SKeMa: Satellite-based Kelp Mapping using Semantic Segmentation on Sentinel-2 imagery},
  year         = 2026,
  publisher    = {Hugging Face},
  doi          = {10.57967/hf/6790},
  url          = {https://huggingface.co/m5ghanba/SKeMa}
}
```

## Links

- **PyPI Package**: https://pypi.org/project/skema-kelp/
- **GitHub Repository**: https://github.com/m5ghanba/skema
- **Documentation**: https://github.com/m5ghanba/skema#readme
- **Issues/Support**: https://github.com/m5ghanba/skema/issues