https://github.com/kraina-ai/srai-tutorial
A tutorial for the SRAI library
https://github.com/kraina-ai/srai-tutorial
artificial-intelligence geospatial machine-learning python srai tutorial
Last synced: 3 months ago
JSON representation
A tutorial for the SRAI library
- Host: GitHub
- URL: https://github.com/kraina-ai/srai-tutorial
- Owner: kraina-ai
- License: apache-2.0
- Created: 2023-07-26T18:04:27.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-02-25T10:49:49.000Z (11 months ago)
- Last Synced: 2025-04-04T02:11:29.923Z (10 months ago)
- Topics: artificial-intelligence, geospatial, machine-learning, python, srai, tutorial
- Language: Jupyter Notebook
- Homepage: https://github.com/kraina-ai/srai
- Size: 87 MB
- Stars: 34
- Watchers: 2
- Forks: 5
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
README
# SRAI Tutorial
Introduction to Geospatial Machine Learning with SRAI
## Description
This is a collection of tutorials for the SRAI library and Geospatial Machine learning.
List of available tutorials:
| Event | Branch | Date |
|----------------|--------|------------|
| [GeoPython 2025](https://submit.geopython.net/geopython-2025/talk/PUVEDJ/) | [geopython2025](https://github.com/kraina-ai/srai-tutorial/tree/geopython2025) | 2025-02-26 |
| [State Of The Map 2024](https://cfp.openstreetmap.org.pl/state-of-the-map-europe-2024/talk/J9U3UW/) | [sotm2024](https://github.com/kraina-ai/srai-tutorial/tree/sotm2024) | 2024-07-19 |
| [OSM Data made easy with Quack OSM - Interview with Matt Forrest](https://www.youtube.com/watch?v=r6cWiSULgYs) | [osm-deep-dive](https://github.com/kraina-ai/srai-tutorial/tree/osm-deep-dive) | 2024-05-14 |
| [ML in PL 2023](https://conference2023.mlinpl.org/program#tutorial-3) | [ml-in-pl-2023](https://github.com/kraina-ai/srai-tutorial/tree/ml-in-pl-2023) | 2023-10-29 |
| [EuroSciPy 2023](https://pretalx.com/euroscipy-2023/talk/X8LYJY/) | [euroscipy2023](https://github.com/kraina-ai/srai-tutorial/tree/euroscipy2023) | 2023-08-14 |
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.
See related readme instructions on each branch, since setup can be different.