Land Use and Land Cover Classification with CNNs

Setup Instructions

1. Create a virtual environment with Python 3.10:

     # Install Python 3.10 if needed
     brew install python@3.10
     
     # Create and activate virtual environment
     python3.10 -m venv venv
     source venv/bin/activate

2. Install dependencies:

     pip install -r requirements.txt

3. Download the dataset:

   • Download from https://zenodo.org/records/7711810
   • Extract the files to the data/ directory

     mkdir -p data
     # Extract EuroSAT_RGB and EuroSAT_MS folders to data/

   • Ensure you have both RGB and multispectral (MS) data

4. Run the Jupyter notebook:

   jupyter notebook imgClassification.ipynb

Training the Model

1. Run through the jupyter notebook to train the model
2. Make sure the model is saved as SatCoverClassifier.keras in the correct folder::

   # The model should be in models/SatCoverClassifier.keras
   mkdir -p models

Running the Flask App with Docker

build and run the docker container

  docker-compose up --build
  # access the app on http://localhost:7860
  # or Update your docker-compose.yml to use a different port

to stop

  # Press Ctrl+C in the terminal or run:
  docker-compose down