---
license: apache-2.0
datasets:
- kostaspic/amfitrite-inland-waters-hab-sentinel2
metrics:
- f1
- accuracy
base_model:
- BIFOLD-BigEarthNetv2-0/rdnet_base-s2-v0.2.0
pipeline_tag: image-classification
library_name: timm
tags:
- earth-observation
- remote-sensing
- sentinel-2
- water-quality
- harmful-algal-blooms
- environmental
---

# AMFITRITE-Sentinel2-HAB-RDNet

This model is a deep learning classifier designed to detect **Harmful Algal Blooms (HABs)** in inland waters using **Sentinel-2 Level-2A** satellite imagery. It operates as a binary classifier, determining whether a water surface patch contains a bloom or not.

The model is built on the **RDNet Base** architecture. It was initialized with weights pretrained on [**BigEarthNet v2.0**](https://huggingface.co/BIFOLD-BigEarthNetv2-0/rdnet_base-s2-v0.2.0) (`rdnet_base-s2-v0.2.0`) and fine-tuned on the [**Amfitrite-Inland-Waters-HAB-Sentinel2**](https://huggingface.co/datasets/kostaspic/amfitrite-inland-waters-hab-sentinel2) dataset.

## Dataset & Training Details
The model was fine-tuned using the Amfitrite-Inland-Waters-HAB-Sentinel2 dataset.

* **Original Classes:** The source dataset contains three indicative classes: `High`, `Moderate` and `Low`.
* **Preprocessing for Detection:** To create a robust binary detector, these classes were merged:
    * **Bloom (Class 1):** Merged `High` and `Moderate` samples.
    * **No Bloom (Class 0):** Mapped from `Low` samples.

This merging strategy allows the model to focus on the critical distinction between "Clear Water" and "Algal Presence".


## Spectral Awareness
Utilizes 10 spectral bands from Sentinel-2 in the following order (B02, B03, B04, B05, B06, B07, B08, B8A, B11, B12) like the BigEarthNet v2.0 RDNet Base pretrained only with Sentinel-2 data.

## Performance
The model was evaluated on a strictly stratified test set to ensure reliability.

| Metric | Test Set | Validation Set |
| :--- | :--- | :--- |
| **F1 Score** | 0.926 | 0.934 |
| **Accuracy** | 0.898 | 0.909 |
| **Balanced Acc** | 0.892 | 0.909 |


## Limitations
* The model expects standard L2A Sentinel-2 surface reflectance values (scaled 0-10000).
* Performance may vary on water bodies that were not well-represented in the training set (e.g. with extremely high sediment loads).

## Preprocessing Requirements

To successfully run inference with this model, your input data must meet the following criteria:
1. **Dimensions:** The input image must be resized or cropped to exactly **256 x 256 pixels**.
2. **Channel Order:** The tensor must contain 10 bands in the following specific order: `B02, B03, B04, B05, B06, B07, B08, B8A, B11, B12`.
3. **Normalization:** The raw Sentinel-2 L2A pixel values (usually ranging from 0 to ~10000) must be divided by `10000.0` so that the input values generally fall between `0.0` and `1.0`.
4. **Shape:** The final tensor passed to the model must have the shape `(Batch_Size, 10, 256, 256)`.


## How to Use
This model requires the `timm` library.

### 1. Installation
```bash
pip install torch timm rasterio
```

### 2. Inference Code
```python
import rasterio
import torch
import timm
import numpy as np
import torch.nn.functional as F

# 1. Load Model
model = timm.create_model('rdnet_base', pretrained=False, num_classes=2, in_chans=10)

# Load weights (assuming the file is downloaded locally as 'model.pth'), use huggingface_hub to download directly
state_dict = torch.load("model.pth", map_location='cpu')
model.load_state_dict(state_dict)
model.eval()

def predict_patch(img_tensor):
    """
    Args:
        img_tensor (torch.Tensor): A tensor of shape (10, Height, Width) containing Sentinel-2 bands.
    """
    # 1. Add batch dimension -> Shape: (1, 10, H, W)
    img_tensor = img_tensor.unsqueeze(0) 
    
    # 2. Force resize to the required 256x256
    if img_tensor.shape[-2:] != (256, 256):
        img_tensor = F.interpolate(img_tensor, size=(256, 256), mode='bilinear', align_corners=False)
    
    # 3. Normalize Sentinel-2 DN values (0-10000) to 0-1 range
    img_tensor = img_tensor / 10000.0
    
    # 4. Predict
    with torch.no_grad():
        output = model(img_tensor)
        prob = torch.softmax(output, dim=1)[0, 1].item() # Probability of Bloom
        
    return prob

# --- Example Usage ---
# Dummy tensor representing a 10-band image that is 365x365 pixels
sample_image = torch.rand(10, 365, 365) * 5000
score = predict_patch(sample_image)
print(f"Score: {score:.1%}")

```

## Citations & References

If you use this model in your research, please cite the following papers for the underlying architecture and pre-training:

**1. This Model:**
Pikounis, K. (2026). *Sentinel-2 HAB Detector (RDNet Base)*. Amfitrite Project.
```bibtex
@misc{pikounis2026hab,
  author       = {Pikounis, Kostas},
  title        = {Sentinel-2 HAB Detector (RDNet Base)},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/kostaspic/AMFITRITE-Sentinel2-HAB-RDNet}},
  organization = {Amfitrite Project}
}
```

**2. Amfitrite Dataset:**
* [kostaspic/amfitrite-inland-waters-hab-sentinel2](https://huggingface.co/datasets/kostaspic/amfitrite-inland-waters-hab-sentinel2)

**3. BigEarthNet v2.0 (Pretraining Source):**
K. Clasen, L. Hackel, T. Burgert, G. Sumbul, B. Demir, V. Markl, "reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis", IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2025.
```bibtex
@inproceedings{clasen2025refinedbigearthnet,
  title={{reBEN}: Refined BigEarthNet Dataset for Remote Sensing Image Analysis},
  author={Clasen, Kai Norman and Hackel, Leonard and Burgert, Tom and Sumbul, Gencer and Demir, Beg{\"u}m and Markl, Volker},
  year={2025},
  booktitle={IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
}
```

**4. Library & Framework:**
L. Hackel, K. Clasen, B. Demir, "ConfigILM: A General Purpose Configurable Library for Combining Image and Language Models for Visual Question Answering.", SoftwareX 26 (2024): 101731.
```bibtex
@article{hackel2024configilm,
  title={ConfigILM: A general purpose configurable library for combining image and language models for visual question answering},
  author={Hackel, Leonard and Clasen, Kai Norman and Demir, Beg{\"u}m},
  journal={SoftwareX},
  volume={26},
  pages={101731},
  year={2024},
  publisher={Elsevier}
}
```