SDKs and Developer Resources
Planet SDK for Python and CLI
The Planet SDK for Python streamlines working with Planet APIs in Python. Starting with version 2.0, the Python SDK simplifies interaction with the Planet APIs, allowing users to focus on building workflows with the data. The Command-Line Interface provides a code-free way to interact with Planet APIs without using code. Refer to the No-Code CLI User Guide to learn more. The Tips & Tricks section includes examples of how you can use the Planet CLI alongside your favorite geospatial tools like GDAL/OGR, Fiona (fio), Kepler.gl, Placemark.io, and more.
The SDK and the CLI support the Orders, Data, and Subscriptions APIs. Refer to the
Quick Start Guide for
instructions to install it from pip using pip install planet, explore the CLI, and then dive into the
Python Guide.
Be sure to not miss the SDK Examples
that link to a number of Python Notebooks.
If you use the V1 Python Client, check out how to Upgrade from Version 1 to Version 2 for tips on migrating your code to V2.
Planet will continue to provide access to the Planet Python Client V1, but no new development is planned.
Sentinel Hub Python SDK
The sentinelhub Python package is the official Python interface for Sentinel Hub services. It supports most of the services described in the Sentinel Hub documentation and any satellite data collections, including Sentinel, Landsat, MODIS, DEM, and custom collections produced by users.
The package also provides a collection of basic tools and utilities for working with geospatial and satellite data. It builds on top of well known packages such as numpy, shapely, pyproj, etc. It is also a core dependency of eo-learn Python package for creating geospatial data-processing workflows.
Sentinel Hub Javascript SDK
The sentinelhub Python package is the official Python interface for Sentinel Hub services. It supports most of the services described in the Sentinel Hub documentation and any type of satellite data collections, including Sentinel, Landsat, MODIS, DEM, and custom collections produced by users.
The package also provides a collection of basic tools and utilities for working with geospatial and satellite data. It builds on top of well known packages such as numpy, shapely, pyproj, etc. It is also a core dependency of the eo-learn Python package for creating geospatial data-processing workflows.
eo-learn
eo-learn is a collection of open source Python packages that have been developed to seamlessly access and process spatiotemporal image sequences acquired by any satellite fleet in a timely and automatic manner. eo-learn is easy to use, its design modular. It encourages collaboration - sharing and reusing of specific tasks in typical EO-value-extraction workflows, such as cloud masking, image co-registration, feature extraction, classification, etc. Everyone is free to use any of the available tasks and is encouraged to improve, develop new ones, and share them with the rest of the community.
eo-grow
Earth observation framework for scaled-up processing in Python.
Analyzing Earth Observation (EO) data is complex and solutions often require custom tailored algorithms. In the EO domain most problems come with an additional challenge: How do we apply the solution on a larger scale?
Working with EO data is made easy by the eo-learn package, while the eo-grow package runs the solutions at a large scale. In eo-grow an EOWorkflow-based solution is wrapped in a pipeline object, which handles parametrization, logging, storage, multi-processing, EOPatch management and more. However, pipelines are not necessarily bound to EOWorkflow execution and can be used for other tasks such as training ML models.
Open Resources
Jupyter Notebooks
Planet has created a collection of Apache 2.0-licensed Jupyter Notebooks, along with a Docker image that makes it easy to run your own geospatially-enabled Jupyter instance.
The interactive guides in this collection are designed to help Python-familiar developers explore Earth observation data, work with the Planet Public APIs, and learn how to extract information from the Planet archive of high-cadence satellite imagery.
QGIS Plugin
QGIS is the most widely used free and open-source desktop geographic information system (GIS). The Planet QGIS Plugin, which makes it easy for QGIS users to discover, stream, and download Planet imagery, is also open source on GitHub.