Update README.md

README.md

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 ## 7. Outcome
 The result is a harmonized dataset linking AERONET-derived AOD (interpolated to 550 nm) and co-located Sentinel-2 imagery for 17 sites in the Middle East. The dataset supports both per-site model development and combined regional modeling for AOD estimation.
 
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-# Part II: Feature Extraction Pipeline
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-## 8. Feature Extraction Overview
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-This section provides a comprehensive explanation of the feature extraction procedure used to prepare Sentinel-2 satellite imagery for Aerosol Optical Depth (AOD) estimation. The process transforms raw 12-band Sentinel-2 images (399×399 pixels) into a structured set of engineered features suitable for machine learning regression models.
 
-The feature extraction pipeline involves:
-- Image preprocessing and quality filtering
-- Extraction of statistical, textural, and spectral indices
-- Rigorous data cleaning and normalization
-- Train-test feature alignment to ensure consistency
 
-The final output consists of normalized feature matrices with matched dimensions for both training and testing datasets.
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-**Input data:**
-- **Training dataset**: `BM_Middle_East_train_dataset.csv` containing metadata for training images
-- **Testing dataset**: `BM_Middle_East_test_dataset.csv` containing metadata for testing images
-- **Sentinel-2 images**: 12-band multispectral images (399×399 pixels) stored as `.tif` files
-- **Target variable**: AOD at 550nm (interpolated using Ångström relation from AERONET measurements)
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-The dataset covers 17 AERONET sites across the Middle East with an 80-20 train-test split per site.
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 license: mit
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