DL-LULC-Classifier

DL-LULC-Classifier is a deep learning project for Land Use and Land Cover (LULC) classification using Convolutional Neural Networks (CNNs). It features can support multiple models, easy integration with Django and HTMX as frontend. This tool is ideal for environmental monitoring and geospatial analysis.

Installation

Prerequisites

• Python 3.x
• Django 5
• TensorFlow/Keras
• Pillow
• NumPy
• Simple you can install the requirements.txt fil

Steps

1. Clone the repository:

   git clone https://github.com/muhammednurgobena/DL-LULC-Classifier.git
   cd LULC_Predictor

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

4. Apply migrations:

   python manage.py migrate

5. Run the development server:

   python manage.py runserver

6. Access the application by navigating to http://127.0.0.1:8000/ in your web browser.

Usage

Upload a satellite image, select a model, and view the LULC predictions.

Customization

Add more models, or modify LULC classes in utils.py.

Contributing

Contributions are welcome!

License

This project is licensed under the MIT License.

Acknowledgments

Thanks to the open-source community for the tools and libraries used in this project.

1. Sentinel-2 Satellite images RGB from EuroSAT Dataset-link https://www.kaggle.com/datasets/apollo2506/eurosat-dataset

2. https://github.com/fedlunur

The model can be made advanced and the output trained model can be togled in django view to enable multipple model runing on the dashboard.

This web app and the backen model is developed for educational purpose only unle tunned seriously

Demo