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Deep learning models for remote sensing applications

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Repository for training land cover recognition models for satellite imagery

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Setting up a python environment

  • Follow the instruction in https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html for downloading and installing Miniconda

  • Open a terminal in the code directory

  • Create an environment using the .yml file:

      conda env create -f deepsatmodels_env.yml
    
  • Activate the environment:

      source activate deepsatmodels   
    
  • Install required version of torch:

      conda install pytorch torchvision torchaudio cudatoolkit=10.1 -c pytorch-nightly
    

Initial steps for setting up experiments

  • Specify the base directory and paths for the training and evaluation files in the "data/datasets.yaml" file.
  • Utilize a distinct ".yaml" configuration file for each experiment. Example files can be found in the "configs" folder. These files contain default values corresponding to parameters used in the associated studies.
  • Adjust the ".yaml" configuration files as needed to train with your custom data.
  • Refer to the instructions provided in the specific README.MD files for additional guidance on setting up and running your experiments.

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