ENV-408 / 5 credits

Teacher(s): Skaloud Jan, Berne Alexis, Tuia Devis

Language: English


Summary

In this course students get acquainted with the process of image (orthophoto and DEM) creation, 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

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

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%)

In the programs

  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Sensing and spatial modeling for earth observation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Sensing and spatial modeling for earth observation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Exam form: Written (summer session)
  • Subject examined: Sensing and spatial modeling for earth observation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22