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README.md

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-license: mit
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+````
+# AERIS v5.0: 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
+```