---
license: apache-2.0
tags:
- computer-vision
- image-segmentation
- satellite-imagery
- remote-sensing
- pytorch
- uncertainty-estimation
datasets:
- 38-cloud
metrics:
- iou
- dice
- precision
- recall
- f1
---

# 🌍 AERIS — Cloud Detection for Landsat 8

AERIS (Automated Environmental Remote Imaging System) is a deep learning model for **cloud segmentation in Landsat 8 satellite imagery** with built-in **uncertainty quantification** using Monte Carlo Dropout.

The model performs high-accuracy binary segmentation on 4-channel satellite inputs (RGB + NIR).

---

## 🔎 Model Description

- **Architecture:** U-Net  
- **Encoder:** ResNet34  
- **Input:** 4-channel (Red, Green, Blue, Near-Infrared)  
- **Input Size:** 256 × 256  
- **Output:** Binary cloud segmentation mask  
- **Framework:** PyTorch + segmentation-models-pytorch  
- **Uncertainty Estimation:** MC Dropout (30 stochastic forward passes)

AERIS not only predicts cloud masks but also provides calibrated confidence estimates for more reliable remote sensing workflows.

---

## 📊 Performance

Evaluation on the **38-Cloud dataset**:

| Metric | Score |
|--------|--------|
| Validation IoU | **92.20%** |
| Dice Coefficient | **94.28%** |
| Precision | 92.15% |
| Recall | 96.73% |
| F1 Score | 94.28% |
| Expected Calibration Error (ECE) | **0.70%** |

Low ECE indicates strong confidence calibration.

---

## 🏋️ Training Details

- **Dataset:** 38-Cloud (16,800 training patches)  
- **Loss Function:** Combined Dice + Binary Cross-Entropy  
- **Optimizer:** AdamW (learning rate = 1e-4)  
- **Epochs:** 30  
- **Hardware:** NVIDIA RTX 4060  

---

## 🚀 Usage

### Installation

```bash
pip install torch torchvision
pip install segmentation-models-pytorch
```

### Load Model

```python
import torch
import segmentation_models_pytorch as smp

model = smp.Unet(
    encoder_name="resnet34",
    in_channels=4,
    classes=1
)

model.load_state_dict(torch.load("Aeris_Model.pth", map_location="cpu"))
model.eval()
```

### Inference

```python
with torch.no_grad():
    output = model(input_tensor)  # input_tensor shape: [B, 4, 256, 256]
```

For uncertainty estimation, run multiple stochastic forward passes with dropout enabled.

---

## 📌 Intended Use

- Satellite image preprocessing  
- Atmospheric correction pipelines  
- Cloud cover estimation  
- Weather monitoring systems  
- Remote sensing research  

---

## ⚠️ Limitations

- Trained only on Landsat 8 imagery  
- Input size fixed at 256×256 patches  
- Performance may degrade on unseen satellite domains  
- Binary cloud detection (does not classify cloud types)

---

## 📜 License

This model is released under the **Apache 2.0 License**.

---

## 🤝 Contributions

Contributions, improvements, and research collaborations are welcome.