Summer School: Program
The dates of the summer school are Thursday, 31 July to Saturday, 02 August, 2025.
Summer School Schedule
| Day & Date | Topic | Details |
|---|---|---|
| Day 1, Thu 31 July | Introduction | Introducing general concepts of machine learning. |
| Day 2, Fri 01 Aug | Data Challenge - Toolbox and Data | Introducing a toolbox and data for the different challenges. Students work in groups independently to solve the challenges. |
| Day 3, Sat 02 Aug | Presentation | Groups will present their results. |
This schedule is subject to change until the final program has been released.
Introduction
Speakers: TBA
The first day is used to introduce the audience to general concepts of machine learning. More details will be provided soon
Tool box
Prepared and delivered by Yongze Song
Topic: Tools for remote sensing and geospatial intelligence analysis: An example of climate impact on bushfire
Contents:
-
Remote sensing data collection (bushfire and climate data).
Tool: Google Earth Engine (GEE) -
Temporal analysis for remote sensing data (temporal trend analysis and correlation).
Tool: Python and R -
Spatial analysis for remote sensing data (spatial hotspot and factor analysis).
Tool: R -
Geospatial artificial intelligence (GeoAI) for remote sensing data (future scenarios
prediction).
Tool: Python
You are asked to bring your own laptop to the summer school.
Data challenge
Prepared and delivered by Lynn Miller, TBA
Guest presenters: Marta Yebra, TBA
Topic: Introducing the data challenge and data needed to solve the challenge
Confirmed data challenges are:
- Fire: Burnt area detection
- Fire: Vegetation moisture content estimation
Further challenges will be announced soon.
Supervisors and mentors will be available to support students to get started to work on their data challenge.
Presentation
Charing: Petra Helmholz, Lynn Miller, Yongze Song
Each group will be given the opportunity to present their results. The presentation should be between 15 and 20 min followed by discussion with the wider audience.