Land cover classification using remote sensing observations.
If you own a cloud environment, follow steps 1-5 below. If you are using OpenSARlab, go straight to step 4.
The VegMapper software is intended to be used on a cloud-computing platform (e.g., AWS EC2) as the volume of remote sensing data is very large normally. However, VegMapper can still be used on a local machine but only Linux and macOS platforms have been tested.
Follow steps on CLOUD.md.
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Git (for cloning this repository to the machine used)
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Miniconda (for installing other required Python packages)
After Miniconda is istalled, install jupyterlab, mamba, and kernda into the base environment by running:
conda install -n base -c conda-forge jupyterlab mamba kernda
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From local machine, run the following command:
ssh -L 8080:localhost:8080 <EC2_username>@<EC2_address>to connect to EC2 with the port forwarding.
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After logging into EC2, run the following command:
jupyter lab --no-browser --port=8080to launch JupyterLab.
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After launching JupyterLab, in the output message it will provide URLs for accessing the Jupyter server. Copy and paste one of them (for example, http://localhost:8080/lab?token=your_token) in your browser to open the Jupyter Lab web interface.
Open a terminal, navigate to where you want the repository to be cloned to, and do
git clone https://github.com/NaiaraSPinto/VegMapper.git
To create a conda environment and install VegMapper software, navigate to the VegMapper folder and run this Jypyter notebook: INSTALL.ipynb. This notebook also includes instructions for obtaining credentials to download imagery from NASA and JAXA.