https://github.com/iguptashubham/satellite_landscape_classifier_system

Satellite Landscape classifier system by MobileNetV2.
https://github.com/iguptashubham/satellite_landscape_classifier_system

Last synced: about 2 months ago
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Satellite Landscape classifier system by MobileNetV2.

• Host: GitHub
• URL: https://github.com/iguptashubham/satellite_landscape_classifier_system
• Owner: iguptashubham
• License: mit
• Created: 2024-11-25T12:14:48.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-11-25T14:54:15.000Z (6 months ago)
• Last Synced: 2025-01-30T09:23:36.845Z (4 months ago)
• Language: Jupyter Notebook
• Size: 73.2 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# Satellite Landscape Classification System (SLCS) using MobileNetV2

![p4](https://github.com/user-attachments/assets/55f3c6cc-e6b9-4efb-8e60-5d8e913d6c1b)

## Overview

The Satellite Landscape Classification System (SLCS) leverages the MobileNetV2 architecture to classify satellite images into distinct landscape categories. This project aims to provide an efficient and accurate solution for remote sensing and environmental monitoring applications.

## Features

- Utilizes MobileNetV2, a lightweight deep learning model.

- Classifies satellite images into four categories: `cloudy`, `desert`, `green_area`, `water`.

- Data augmentation for enhanced model robustness.

- Early stopping to prevent overfitting.

## Installation

To run this project, you need to have Python 3.x and the necessary libraries installed.

1. Clone the repository:

    ```sh

    git clone https://github.com/iguptashubham/satellite_landscape_classifier_system.git

    cd satellite_landscape_classification

    ```

2. Install the required libraries:

    ```sh

    pip install -r requirements.txt

    ```

## Usage

3. prediction

add your image path

```sh

    run predict.py

    ```