Update README.md

README.md

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 **Project Organisation**: MIT Critical Data Colombia.
 
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+## Model Description
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+- **Repository:** [Code](https://github.com/sebasmos/satellite.extractor)
+- **ESA project:** Sponsoring request ID 1c081a - Towards a Smart Eco-epidemiological Model of Dengue in Colombia using Satellite in Collaboration with MIT Critical Data Colombia)
+- **Point of Contact:** [Sebastian A. Cajas Ordóñez](mailto:scajasordonez@gmail.com)
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 ## Summary 
 
 
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   *Pre-processing*: The first step is to apply Contrast Limited Adaptive Histogram Equalization (CLAHE) to the image, with a clip limit of 6.0 and a tile grid size of 16 by 16. This technique enhances the contrast of the image while preventing over-amplification of noise. Secondly, we apply the RGBShift augmentation technique, which randomly shifts the values of pixels in the red, green, and blue channels of the image. This is done with a probability of 100% and is applied to 30 pixels per channel. Finally, we apply the RandomBrightnessContrast augmentation technique with a probability of 50%. This technique randomly adjusts the brightness and contrast of the image to create variations in the dataset.
 
 * **SAT5_dataset_10_best_cities_augmented_v2**: These images are improved to remove near black images method using a recursive [forward-backward artefact removal algorithm with inter-band data augmentation on satellite imagery](https://github.com/sebasmos/satellite.extractor/tree/main/src/PART_2_satellite-augmentation). Augmented data with aligned metadata. Improved version using Albumentation wrapper modules with extra augmented data. Data extracted using recursive artifact removal, cloud removal based on LeastCC, and Nearest Interpolation for spatial resolution. Implemented [Notebook](https://github.com/sebasmos/satellite.extractor/blob/main/notebooks/PART_2_satellite_imagery_augmentation.ipynb) and augmentations applied to RGB channels while leaving other satellite channels unchanged. 
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   *Pre-processing*: The image augmentation techniques used in this process include various forms of Gaussian noise, implemented through IAAAdditiveGaussianNoise with a probability of 20% and mean zero, and standard deviation of 0.01 * 255 or 0.05 * 255. The GaussNoise technique was also employed with a mean of zero and default variance of (10.0, 50.0). General blurring was implemented through MotionBlur (p=.2), MedianBlur (blur_limit=3, p=0.1), Blur(blur_limit=3, p=0.1), and ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=0.2). Distortion techniques included OpticalDistortion(p=0.3), GridDistortion(p=.1), IAAPiecewiseAffine(p=0.3), and IAAAffine(scale=(0.8, 1.2), translate_percent=0.1, rotate=15, shear=10, p=0.2). Finally, brightness adjustments were made using CLAHE(clip_limit=2), IAASharpen(), IAAEmboss(), RandomBrightnessContrast(), and HueSaturationValue(p=0.3).
 
 ## Sponsors