PlanetQuery

Warning: This project is under active development and in an early stage. Expect frequent changes.

Draw a box anywhere on Earth and explore RGB (Sentinel‑2), SAR (Sentinel‑1), and contextual layers (ESA WorldCover). PlanetQuery is a purely Python-based web map explorer that leverages open STAC catalogs and Cloud-Optimized GeoTIFFs (COGs). Support for additional STAC catalogs is in progress.

Credit to @scottyhq, @gbrencher, and @egagli for inspiration and code logic.

Many thanks to banesullivan's localtileserver package and 'issue' replies

Try it in your browser (no install)

What happens when you click the button:

• mybinder.org uses repo2docker to build a Docker image for this repo using binder/environment.yml and runtime.txt (Python 3.11 base)
• The conda environment is solved/installed with mamba from conda-forge; during the build, pip installs this repo in editable mode (-e .)
• The built image is cached by mybinder.org for the given Git ref; first builds can take several minutes, later builds are usually faster until the cache is evicted
• Sessions are ephemeral and resource-limited; they time out when idle (~10 mins) or after a short lifetime

Notes and expectations

• First launch: building can take several minutes; please be patient.
• If you see "Building' or "Pending," Binder is queuing your session or compiling the environment.
• If tiles don’t appear immediately after "Fetch & Display Data," wait a few seconds.
• Don't sample large areas -- this will drop performace. Less than 30km2 is a good bet.
• The app depends on external open data services; occasional slowdowns or gaps can occur.

What you can do

• Pick a date range, draw an Area of Interest (blue) on the map, then click "Fetch & Display Data."
• Toggle layers in the layer control:
  • RGB composite (Sentinel‑2) [lowest cloud coverage in date range is selected]
  • SAR backscatter (Sentinel‑1) [acquisition closest to Sentinel-2 image is selected]
  • Coopernicus 30-GLO DEM Multidirectional Hillshade
  • ESA WorldCover (classified land cover) [2021]
• Draw a red Region of Interest (ROI) to sample statistics from visible layers.

How it works (high level)

• You draw an area; the app searches the Microsoft Planetary Computer STAC API for relevant items
• It selects a low‑cloud Sentinel‑2 scene and a time‑aligned Sentinel‑1 scene
• It computes quicklook products:
  • Sentinel‑2 RGB
  • Sentinel‑1 VV/VH in decibels
  • Multidirectional hillshade from Copernicus GLO‑30
  • ESA WorldCover mapped to RGB colors
• Rasters are written to in‑memory COGs and served by localtileserver running in your Binder session.
• ipyleaflet requests tiles via Jupyter Server Proxy (e.g., /proxy/{port}); the browser only talks to the Binder pod, while the pod fetches data from the Planetary Computer.

NOTE: All layers / STAC endpoints are oprtional

Contributing

• TBD

Development (for contributors)

• This repo is Python-first and uses standard tools (ipywidgets, ipyleaflet, xarray, rioxarray, rasterio).
• Local setup suggestions (pick your preferred workflow):
  • Conda/mamba: create an environment similar to binder/environment.yml, then pip install -e .
  • Or use your own stack and ensure GDAL/Rasterio/PROJ are consistent.
• Run notebooks locally with JupyterLab or launch the Voila app:
  • voila notebooks/app.ipynb