Introduction to Geospatial Machine Learning
07-20, 10:00–11:30 (Poland), Room CA3

This tutorial offers a thorough introduction to Geospatial Artificial Intelligence in Python using the SRAI library. Participants will learn how to use this library for geospatial tasks like downloading and processing OpenStreetMap data, extracting features from GTFS data, dividing an area into smaller regions, and representing regions in a vector space using various spatial features. Additionally, participants will learn to pre-train embedding models and train predictive models for downstream tasks.

In this tutorial, we intend to provide a comprehensive introduction to the Spatial Representations for Artificial Intelligence (srai) library. Participants will learn how to utilize this library for various geospatial applications, such as downloading and processing OpenStreetMap data, extracting features from GTFS data, splitting a given area into smaller regions, and embedding regions into a vector space based on different spatial features. Moreover, users will learn how to pre-train a model of their choice and build predictive models for use in downstream tasks.

By the end of the tutorial, attendees will be able to:
1. Install and set up the SRAI library.
2. Use SRAI to download and process geospatial data.
3. Apply various regionalization and embedding techniques to geospatial data.
4. Utilize pre-trained embedding models for clustering and similarity search.
5. Build predictive models on top of SRAI embeddings

If you want to follow along, please find the material and installation instructions at We encourage you to set up the repository and install the dependencies before the tutorial.

Lastly, if you're not familiar with geospatial data, we recommend a great tutorial by Joris Van den Bossche: Introduction to geospatial data analysis with GeoPandas.
Understanding this tutorial is not required, but it might help you gain a deeper understanding of geospatial data and tooling in this domain. Consider it an optional pre-reading.

Spatial Data Scientist and an ML Engineer passionate about the geospatial domain and an author of highway2vec. Background in Computer and Data Science from the Wrocław University of Science and Technology and a proud member of the KRAINA Lab tackling geospatial problems