/Satellite-data

This repository provides Python code for converting satellite data into a format suitable for deep learning models. It supports various deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory networks (LSTMs).

Primary LanguagePython

Data Conversion for Satellite Imagery Analysis

Introduction

This repository provides Python code for converting satellite data into a format suitable for deep learning models. It supports various deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory networks (LSTMs).

Requirements

  • Python 3.x
  • pandas
  • NumPy

Usage

  1. Clone the Repository: Clone this repository to your local machine. 
    git clone https://github.com/your-username/satellite-data-.git
  2. Install Dependencies: Install the required Python packages if you haven't already. 
    pip install -r requirements.txt
  3. Prepare Your Data: Replace satellite_data.csv with your dataset. Ensure that your CSV file contains satellite data with features and the target variable.
  4. Customize the Code: Open and modify preprocess_satellite_data.py according to your data preprocessing requirements.
  5. Run the Script: Execute the preprocessing script. 
    python preprocess_satellite_data.py
  6. Check Output: The preprocessed data will be saved as NumPy arrays (X_train.npy, X_test.npy, y_train.npy, y_test.npy) in the same directory.

Preprocessing Steps

add this section

n the data-saving step as per your requirements.

License

This project is licensed under the MIT License - see the LICENSE file for details.