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 https://github.com/kraina-ai/srai-tutorial/tree/SOTM2024. 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.