Welcome to the dedicated repository for advancing land cover recognition through the application of state-of-the-art models on satellite imagery. This repository serves as a comprehensive resource for researchers and practitioners in the field, providing access to research code, detailed setup instructions, and guidelines for conducting experiments with satellite image timeseries data.
This repository highlights contributions to the field through the following research publications:
For the initial setup, please follow the instructions for downloading and installing Miniconda available at the official Conda documentation.
Creating the Environment: Navigate to the code directory in your terminal and create the environment using the provided .yml
file by executing:
conda env create -f deepsatmodels_env.yml
Activating the Environment: Activate the newly created environment with:
source activate deepsatmodels
PyTorch Installation: Install the required version of PyTorch along with torchvision and torchaudio by running:
conda install pytorch torchvision torchaudio cudatoolkit=10.1 -c pytorch-nightly
data/datasets.yaml
file..yaml
file for each experiment, located in the configs
folder. These configuration files encapsulate default parameters aligned with those used in the featured research. Modify these .yaml
files as necessary to accommodate custom datasets.This project is made available under the Apache License 2.0. Please see the LICENSE file for detailed licensing information.
Copyright © 2023 by Michail Tarasiou