Instructor Notes

Instructor notes

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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

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• 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

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Introduction to Vector Data

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Coordinate Reference Systems

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The Geospatial Landscape

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Access satellite imagery using Python

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Read and visualize raster dataResampling the raster image

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Vector data in Python

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Crop raster data with rioxarray and geopandas

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Raster Calculations in Python

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Calculating Zonal Statistics on Rasters

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Parallel raster computations using Dask

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Data cubes with ODC-STAC

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