Upload Aeris_Model.pth

# AERIS: Cloud Detection in Satellite Imagery

Automated cloud detection system for Landsat 8 satellite imagery using deep learning with uncertainty quantification.

## Overview
AERIS (Automated Environmental Remote Imaging System) detects clouds in 4-channel satellite images using a U-Net architecture with ResNet34 encoder. The model includes Monte Carlo Dropout for uncertainty estimation, making predictions reliable and trustworthy.

## Model Architecture
- **Base:** U-Net with ResNet34 encoder
- **Input:** 4-channel images (256×256) - Red, Green, Blue, Near-Infrared
- **Output:** Binary cloud segmentation mask
- **Uncertainty:** MC Dropout with 30 iterations
- **Framework:** PyTorch + segmentation-models-pytorch

## Performance Metrics
| Metric | Score |
|--------|-------|
| Validation IoU | 92.20% |
| Dice Coefficient | 94.28% |
| Precision | 92.15% |
| Recall | 96.73% |
| F1 Score | 94.28% |
| ECE (Calibration) | 0.70% |

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

## Use Cases
- ✅ Satellite image preprocessing
- ✅ Atmospheric correction pipelines
- ✅ Weather analysis
- ✅ Remote sensing research
- ✅ Cloud cover estimation

## Quick Start
```python

Aeris_Model.pth

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