The following papers are featured in this repository:
- ViTs for SITS: Vision Transformers for Satellite Image Time Series (CVPR 2023). More information in README_TSVIT.md.
- Context-self contrastive pretraining for crop type semantic segmentation (IEEE Transactions on Geoscience and Remote Sensing). More information in README_CSCL.md.
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Follow the instruction in https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html for downloading and installing Miniconda
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Open a terminal in the code directory
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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
- 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.