In this talk, we will present multiple geospatial representation learning methods based on OpenStreetMap features. We will cover contextual embeddings, road network representations and word2vec-like semantic embeddings. Finally, we will present a library that aggregates those methods with additional data engineering capabilities.

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.