University of Iceland
Machine Learning for Earth Observation powered by Supercomputers
Every Spring Semester
Abstract
This course exposes the students to the physical principles underlying satellite observations of Earth by passive sensors, as well as parallel Deep Learning (DL) algorithms that scale on High Performance Computing (HPC) systems.
Course Description
This course exposes the students to the physical principles underlying satellite observations of Earth by passive sensors, as well as parallel Deep Learning (DL) algorithms that scale on High Performance Computing (HPC) systems.
For the different theoretical concepts (represented by 4 modules), the course provides hands-on exercises. These exercises are part of a project in the context of Remote Sensing (RS) image classification that the students are asked to develop during the whole duration of the course (see the official page of the course).
Learning Outcomes
Upon completion of the course, the student should:
- Know the fundamental laws of Remote Sensing (RS)
- Be able to define properties of satellite images
- Be capable to convert raw satellite images to a format suitable for Machine Learning
- Know how to enable distributed Deep Learning in HPC
- Be able to produce and evaluate land-cover classification maps
Course Material
Previous teaching
