VegMapper

Land cover classification using remote sensing observations.

Prerequisites

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.

1. Obtain an AWS account and EC2 instance

Follow steps on CLOUD.md.

2. Install Git and Conda

Git (for cloning this repository to the machine used)
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

3. Running JupyterLab on a remote server (EC2)

From local machine, run the following command:
```
ssh -L 8080:localhost:8080 <EC2_username>@<EC2_address>
```
to connect to EC2 with the port forwarding.
After logging into EC2, run the following command:
```
jupyter lab --no-browser --port=8080
```
to launch JupyterLab.
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.

4. Clone VegMapper Repository

Open a terminal, navigate to where you want the repository to be cloned to, and do

git clone https://github.com/NaiaraSPinto/VegMapper.git

5. Installation

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.

SitianXiong / VegMapper