Summary

Students get acquainted with the process of mapping from images (orthophoto and DEM), as well as with methods for monitoring the Earth surface using remotely sensed data. Methods will span from machine learning to geostatistics and model the spatiotemporal variability of processes.

Content

Keywords

Geostatistics, spatial variability, variograms, kriging interpolation

Learning Prerequisites

Recommended courses

Basic statistics

Important concepts to start the course

Good Python programming skills are required

Learning Outcomes

By the end of the course, the student must be able to:

• Explain pipelines of image acquisition and their conversion to 3D models
• Assess / Evaluate problems related to spatial correlation
• Design solutions to address those
• Implement state of the art geostatistical and machine learning approaches in Python

Transversal skills

• Demonstrate the capacity for critical thinking
• Access and evaluate appropriate sources of information.

Teaching methods

Ex-cathedra lectures and exercice sessions

Assessment methods

Tests during the semester (30%) and final exam (70%)

Resources

Moodle Link

• https://go.epfl.ch/ENV-408