Download Planet Data

Repo for downloading PlanetScope data based on a set of predefined grids.

Initial Setup

Some one time repo initialization work.

Install Conda

You can install miniconda if on Mac or Linux. On Windows install Anaconda.

Miniconda

Follow directions HERE

For Mac, perfer to install miniconda using brew.

brew doctor
brew update
brew upgrade
brew upgrade --cask --greedy
brew install --cask miniconda

Anaconda

Follow directions HERE

Open Terminal

On Windows, open an Andaconda Terminal, on Linux/Mac open a regular terminal.

Install Git

Check if git is installed. It will return something e.g. /usr/bin/git if git is installed.

# Linux/Mac
which git
# Windows
where git

If git is not installed, install it.

# Windows
conda install git
# Mac
brew install git

Clone repo

This command will create a new folder planet_download in your terminal's current directory. If you want it installed somewhere specific, move to that folder first (cd SOMEWHERE/ELSE)

git clone git@github.com:kyle-dorman/planet_download.git

After cloning the repo, enter the folder

cd planet_download

Create conda environment

This repo uses Conda as the primary environment setup path.

conda env create -f environment.yml

Activate conda environment

conda activate planet_download

Download data

Activate conda environment

conda activate planet_download

Edit the config file

Start from the tracked example config and create a local config.yaml.

cp configs/download.example.yaml config.yaml

You MUST set the grid_dir and save_dir variables. You should also add a processing_dir if you wish the data to be copied for further Neural Network processing. Local config.yaml files are ignored by git.

grid_dir - The path to a folder of geojson grid files in the wgs84 CRS. save_dir - The path to a folder where you want to save the data. During the download process, data will be saved to folders following the convention: save_dir/YEAR/MONTH/GRID_ID processing_dir - The path to a folder where you process the surface reflectance and UDM data with a Neural Network. e.g. Y:\planet\stateMap\processing

Planet API Key

Look up your API key from the Planet Account Settings.

Optionally create a local .env file:

cp .env.example .env

Option 1: Run download script via Jupyter

1. Launch jupyter notebook

jupyter notebook --notebook-dir=notebooks --port=8892

2. Open the run.ipynb.
3. Set the MONTH, YEAR, CONFIG_FILE, and PL_API_KEY variables.
4. Run the remaining cells

Option 2: Run download script via CLI

Inpsect run.py script to see how it can be used.

python src/scripts/run.py --help

You will be prompted to enter your Planet API Key the first time you run the script.

Example 1:

Download files for a single month/year combination

python src/scripts/run.py --config-file config.yaml --year 2022 --month 11

This will download data for the year 2022 and the month of November (11).

Example 2:

Download files for multiple month/year combinations

python src/scripts/run.py --config-file config.yaml --year 2020 --year 2021 --year 2022 --month 09 --month 10 --month 11

This will download data for 3 years (2020, 2021, 2022) and 3 months (Set, Oct, Nov) for a total of 9 year/month combindations. You must provide at least 1 year and 1 month.

Inspect Results

You can inspect the results of a download using an included jupyter notebook.

Launch jupyter notebook

jupyter notebook --notebook-dir=notebooks --port=8892

Run the notebook inspect.ipynb to visualize the downloaded results.

Format code

conda activate planet_download
./lint.sh

Update dependencies

After changing the environment.yml file, run

conda activate planet_download
conda env update --file environment.yml --prune
conda activate planet_download