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license: apache-2.0 |
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tags: |
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- computer-vision |
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- image-segmentation |
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- satellite-imagery |
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- remote-sensing |
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- pytorch |
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- uncertainty-estimation |
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datasets: |
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- 38-cloud |
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metrics: |
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- iou |
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- dice |
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- precision |
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- recall |
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- f1 |
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--- |
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# 🌍 AERIS — Cloud Detection for Landsat 8 |
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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. |
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The model performs high-accuracy binary segmentation on 4-channel satellite inputs (RGB + NIR). |
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--- |
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## 🔎 Model Description |
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- **Architecture:** U-Net |
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- **Encoder:** ResNet34 |
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- **Input:** 4-channel (Red, Green, Blue, Near-Infrared) |
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- **Input Size:** 256 × 256 |
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- **Output:** Binary cloud segmentation mask |
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- **Framework:** PyTorch + segmentation-models-pytorch |
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- **Uncertainty Estimation:** MC Dropout (30 stochastic forward passes) |
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AERIS not only predicts cloud masks but also provides calibrated confidence estimates for more reliable remote sensing workflows. |
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--- |
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## 📊 Performance |
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Evaluation on the **38-Cloud dataset**: |
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| Metric | Score | |
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|--------|--------| |
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| Validation IoU | **92.20%** | |
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| Dice Coefficient | **94.28%** | |
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| Precision | 92.15% | |
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| Recall | 96.73% | |
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| F1 Score | 94.28% | |
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| Expected Calibration Error (ECE) | **0.70%** | |
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Low ECE indicates strong confidence calibration. |
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--- |
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## 🏋️ Training Details |
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- **Dataset:** 38-Cloud (16,800 training patches) |
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- **Loss Function:** Combined Dice + Binary Cross-Entropy |
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- **Optimizer:** AdamW (learning rate = 1e-4) |
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- **Epochs:** 30 |
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- **Hardware:** NVIDIA RTX 4060 |
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--- |
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## 🚀 Usage |
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### Installation |
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```bash |
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pip install torch torchvision |
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pip install segmentation-models-pytorch |
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``` |
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### Load Model |
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```python |
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import torch |
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import segmentation_models_pytorch as smp |
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model = smp.Unet( |
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encoder_name="resnet34", |
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in_channels=4, |
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classes=1 |
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) |
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model.load_state_dict(torch.load("Aeris_Model.pth", map_location="cpu")) |
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model.eval() |
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``` |
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### Inference |
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```python |
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with torch.no_grad(): |
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output = model(input_tensor) # input_tensor shape: [B, 4, 256, 256] |
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``` |
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For uncertainty estimation, run multiple stochastic forward passes with dropout enabled. |
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--- |
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## 📌 Intended Use |
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- Satellite image preprocessing |
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- Atmospheric correction pipelines |
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- Cloud cover estimation |
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- Weather monitoring systems |
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- Remote sensing research |
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## ⚠️ Limitations |
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- Trained only on Landsat 8 imagery |
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- Input size fixed at 256×256 patches |
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- Performance may degrade on unseen satellite domains |
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- Binary cloud detection (does not classify cloud types) |
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## 📜 License |
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This model is released under the **Apache 2.0 License**. |
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--- |
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## 🤝 Contributions |
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Contributions, improvements, and research collaborations are welcome. |
