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SRAI Tutorial
Introduction to Geospatial Machine Learning with SRAI
Description
Tutorial offers a thorough introduction to the geospatial domain with Python libraries. Participants will learn how to use, analyse and visualize open-source geospatial data. Additionally, participants will learn to pre-train embedding models and train predictive models for downstream tasks.
Most of the tutorial will be showing capabilities of the library srai (Spatial Representation for Artificial Intelligence), as well as GeoPandas, Shapely, osmnx and scikit-learn.
Beginner knowledge of Python is expected from the participants. Tutorial materials is provided in the form of Jupyter notebooks.
Setup
Initialize Python virtual environment (optional but recommended):
python -m venv venv
source venv/bin/activate
And then install the dependencies:
pip install -r requirements.txt
After that, the notebooks can be run. They are available in the tutorial directory. You can start with the introductory one.
Solutions to the assignments are available in the answers directory
Slideshow
It is possible also to run the jupyter notebooks as a slideshow:
> jupyter notebook
Edit/Exit RISE Slideshow (or alt+r)
in the opened notebook
Export to pdf
Use decktape. An exemplary command:
./node_modules/.bin/decktape rise -s 1920x1080 http://localhost:8888/notebooks/02_srai.ipynb?token=<copy-token> ./export/02_srai.pdf
Exported Slideshow
Visit export folder for the rendered slides.
Generate tutorial
The following command will generate files in the tutorial
folder.
nbgrader generate_assignment MLinPL -f