Instructor Notes

Instructor notes


Episode 1

  • The exercise Exercise: Search satellite scenes using metadata filters needs extra attention. Its output search.json is required for the later episodes. Therefore we recommend:
    • Do not skip this exercise;
    • Think twice when you would like to change the query arguments in this exercise;
    • Make sure all the audience have the output search.json before continuing.

Episode 3

  • brpgewaspercelen_definitief_2020_small.gpkg was created because the original file was too large to download and load. Original file, which was ~500Mb could take several minutes to load, and could crash the Jupyter terminal.
  • The cropped version of brpgewaspercelen_definitief_2020_small.gpkg: data/fields_cropped.shp is required for later episodes.
  • The “Modify the geometry of a GeoDataFrame” section is optional and can be skipped without consequences.

Episode 4

  • It is not recommended to plot the visual band directly, due to its size (3 x 10980 x 10980). Please plot the overview as in the teaching material.
  • The clipped raster data: raster_clip.tif is required for later episodes.

Episode 5

  • The calculated NDVI: NDVI.tif is required for later episodes.
  • The calculated classification identifier: NDVI_classified.tif is required for later episodes.

Workshop setup


  • Consider using mamba for speeding up the Python environment setup.
  • Make sure the audience has downloaded the three vector datasets to the data repository.

Introduction to Raster Data


Introduction to Vector Data


Coordinate Reference Systems


The Geospatial Landscape


Access satellite imagery using Python


Read and visualize raster dataResampling the raster image


Vector data in Python


Crop raster data with rioxarray and geopandas


Raster Calculations in Python


Calculating Zonal Statistics on Rasters


Parallel raster computations using Dask


Data cubes with ODC-STAC