Planet Interactive Guides
In this repository, you'll find a collection of Jupyter notebooks from the software developers, data scientists, and developer advocates at Planet. These interactive, open-source (APLv2) guides are designed to help you explore Planet data, work with our APIs and tools, and learn how to extract information from our massive archive of high-cadence satellite imagery. We hope these guides will inspire you to ask interesting questions of Planet data. Need help? Find a bug? Please file an issue and we'll get back to you.
Install and use these notebooks
NOTE: After installing Docker, Windows users should install WSL2 Backend when prompted.
Clone or update repo:
If you've never cloned the Planet notebooks repo, run the following:
git clone https://github.com/planetlabs/notebooks.git cd notebooks
If you have previously cloned the Planet notebooks repo in the past, make sure to update to pull any changes locally that might have happened since you last interacted with the Planet notebooks:
cd notebooks git pull
Access your Planet API Key in Python
Authentication with Planet's API Key can be achieved by using a valid Planet API Key.
You can export your API Key as an environment variable on your system:
If you wish to have your API Key be persistent (forever stored as
PL_API_KEY), then you may enter this
export command in your
~/.zshrc file. If you are using our Docker environment, as is defined below, it will already be set.
In Python, we set up an API Key variable,
PLANET_API_KEY, from an environment variable to use with our API requests:
# Import the os module in order to access environment variables import os # Set up the API Key from the `PL_API_KEY` environment variable PLANET_API_KEY = os.getenv('PL_API_KEY')
Now, your Planet API Key is stored in the variable
PLANET_API_KEY and is ready to use in your Python code.
Run Planet Notebooks in Docker
Planet Notebooks rely on a complex stack of technologies that are not always easy to install and properly configure. To ease this complexity we provide a docker container for running the notebook on docker compatible systems. To install docker on your system please see docker's documentation for your operating system.
Download prebuilt Docker image (recommended)
The Docker image for these notebooks is hosted in the planetlabs/notebooks repo on DockerHub. To download and prepare the image for use, run:
cd notebooks docker pull planetlabs/notebooks docker tag planetlabs/notebooks planet-notebooks # If you get errors running the above, you might have to add sudo to the beginning: #sudo docker pull planetlabs/notebooks #sudo docker tag planetlabs/notebooks planet-notebooks
If you want to re-build the Docker image yourself, this is documented below in the "Appendix: Build the Docker image" section.
Run the container
To run the container (after building or downloading it), add your Planet API key below and issue the following command from the git repository root directory:
docker run -it --rm -p 8888:8888 -v $PWD:/home/jovyan/work -e PL_API_KEY='[YOUR-API-KEY]' planet-notebooks # If you get a permissions error running the above, you should add sudo to the front: # sudo docker run -it --rm -p 8888:8888 -v $PWD:/home/jovyan/work -e PL_API_KEY='[YOUR-API-KEY]' planet-notebooks # Windows users run: winpty docker run -it --rm -p 8888:8888 -v "/$PWD":/home/joyvan/work -e PL_API_KEY='[YOUR-API-KEY]' planet-notebooks
This does several things:
Maps the docker container's
8888port to your system's
8888port. This makes the container available to your host systems web browser.
Maps a host system path
$PWDto the docker container's working directory. This ensures that the notebooks you create, edit, and save are available on your host system under the
jupyter-notebookssub-directory and are not destroyed when you exit the container. This also allows for running tests in the
Ensures that the directory in the Docker container named
/home/jovyan/workthat has the notebooks in them is accessible to the Jupyter notebook server.
Starts an interactive terminal that is accessible through http://localhost:8888.
Sets an environment variable with your unique Planet API key for authenticating against the API.
--rmoption to clean up the notebook after you exit the process.
Open Jupyter notebooks
Once the Docker container is running, the CLI output will display a URL that you will use to access Jupyter notebooks with your browser.
NOTE: This security token will change every time you start your Docker container.
PSScene item type as soon as possible.NOTE: PSScene3Band and PSScene4Band item type and assets will be deprecated by January 2023. These item types will not be available for new customers provisioned after March 1, 2022. We recommend all customers who are interested in medium resolution imagery use the
Search, activate, download with the Data API
- Explore the Planet Data API with Python
- Search, activate, and download imagery with the Planet Python Client
- Search & Download Quickstart Guide
- Planet Data API reference
Ordering, delivery, and tools with the Orders API
Process Planet data
- Create a mosaic from multiple PlanetScope scenes
- Calculate a vegetation index from 4-band satellite imagery
- Convert PlanetScope metadata from radiance to reflectance
- Visualize and convert a UDM to a binary mask
- Work with the Usable Data Mask (UDM2)
Analyze and visualize Planet data
- Analytics quickstart:
- Analytics user guide:
- Other analytics notebooks:
- Detect, count, and visualize ships in Planet imagery
- Pixel-by-pixel comparison of PlanetScope and Landsat Scenes
- Calculate Coverage for a Search Query
- Segment and Classify Crops
- Identify Forest Degradation
- Identify the Temporal Signature of Crops
- Converting Raster Results to Vector Features
- Creating a Heatmap of Vector Results
- Introduction to Cloud Native Geospatial Tools
Soon we hope to add notebooks from the researchers, technologists, geographers, and entrepreneurs who are already using Planet data to ask interesting and innovative questions about our changing Earth. If you're working with our imagery and have a notebook (or just an idea for a notebook) that you'd like to share, please file an issue and let us know.
Appendix: Build the Docker image
This documents how to build the docker image yourself, rather than using the recommended step of downloading pre-built Docker images. This is useful if you are a developer adding dependencies or a new Jupyter notebook to this repo, for example.
First you must build the docker image. Note, this only has to be done the first time you use it. After checking out the repository, you run:
cd planet-notebook-docker docker build --rm -t planet-notebooks . cd ..
This will build and install the Docker image on your system, making it available to run. This may take some time (from 10 minutes to an hour) depending on your network connection and how long Anaconda takes to configure its environment.