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README.md

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-license: mit
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+---
+license: mit
+---
+
+# Satellite Segmentation Model
+
+## Description
+This project contains a deep learning model for satellite image segmentation. The model is trained to classify different classes such as buildings, land, roads, vegetation, water, and unlabeled areas in satellite images.
+
+## How to Use
+To use the model, follow these steps:
+1. Install the required dependencies by running `pip install -r requirements.txt`.
+2. Load the trained model (`satellite_segmentation_full.h5`) using your preferred deep learning framework or library.
+3. Preprocess your input satellite image data according to the model's input requirements (e.g., resize, normalize).
+4. Feed the preprocessed images to the model for segmentation.
+5. Obtain the segmented output masks corresponding to different classes.
+
+### Input Requirements
+- Input images should be in RGB format.
+- Images should be preprocessed to match the model's input size and normalization.
+
+### Expected Output Format
+- Segmented masks representing different classes (e.g., Building, Land, Road, Vegetation, Water, Unlabeled) in the input image.
+
+## Dependencies
+- Python 3.8
+- TensorFlow 
+- NumPy
+- OpenCV
+- PIL (Python Imaging Library)
+- Matplotlib
+- scikit-learn
+- Other dependencies as specified in `requirements.txt`.
+
+## Additional Setup Instructions
+- For detailed installation and usage instructions, refer to the documentation in the repository or project wiki.
+- Make sure to have GPU support if training or inference requires significant computational resources.
+
+## License
+This project is licensed under the [MIT License](LICENSE).