Skip to main content

Ctrl+K

Hands-on GeoAI 2026 documentation

Course information

General Information
Course environment
Communicating with Slack
License and terms of usage
Attribution
Useful data sources
Install Python + libraries (optional)

Tutorials

Tutorial 0: Meeting Git
Tutorial 1: Introduction to Python and Jupyter
Lecture 2 - Basic ML Tutorial Notebook 1: ML Classification with Vienna Airbnb Data
Lecture 2 - BasicML Tutorial Notebook 2: ML Regression with Vienna Airbnb Data
Lecture 3 - Deep Learning Tutorial Notebook 1: Introduction to Deep Learning with PyTorch
Lecture 4 - Deeplearning Tutorial Notebook: Convolutional Neural Networks with PyTorch using EuroSAT
Lecture 4 - Deep Learning Tutorial Notebook: Pool Detection with YOLO
LLMs & Vision LLMs for GeoAI — Part 1: Foundations
LLMs & Vision LLMs for GeoAI — Part 2: Hands-on Lab
LLMs & Vision LLMs for GeoAI — Part 2: Solutions

Week 1

L0: Course Overview
L1: Introduction to GeoAI
P1: Course Environment

Week 2

Lecture 2 - Machine-Learning-Basics Part 1
Lecture 2 - Machine-Learning-Basics Part 2

Week 3

Lecture 3 - Deep-Learning

Week 4

Lecture 4 - GeoAI in image processing

Week 5

Lecture 5 - Advanced Aspects of GeoAI

Week 6

Lecture 6 - LLMs and Foundation Models

Week 7

Lecture 7 - GeoAI applications and research

Repository
Suggest edit

.rst

Useful data sources

Useful data sources#

previous

Attribution

next

Install Python + libraries (optional)

By TU Graz & Aalto University

© Copyright 2026, TU Graz & Aalto University.