Sensing and spatial modeling for earth observation

ENV-408 / 5 credits

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

Language: English

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

The course is organized in three main parts.

1. 3D reconstruction from images

  • Processes of image creation
  • Image matching, orientation and camera calibration
  • Construction of digital elevation models (DEM) and orthophotos

2. Environmental monitoring with machine learning

  • Extracting features from elevation or image data
  • Prediction with linear and nonlinear regression

3. Geostatistics:

  • Definitions and spatial context
  • Structural analysis
  • Interpolation using kriging

Learning Prerequisites

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:

  • Implement state of the art geostatistical and machine learning approaches in Python
  • Establish 3D model from captured imagery.
  • Analyze various properties of obtained 3D model.
  • Plan relavant aspects in drone & airborne mapping.

Assessment methods

Final exam (80%) + 1 graded exercise (20%)

Supervision

Office hours No
Assistants Yes
Forum No

Resources

Virtual desktop infrastructure (VDI)

Yes

Moodle Link

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
  • Labs: 1 Hour(s) per week x 14 weeks
  • Type: optional
  • 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
  • Labs: 1 Hour(s) per week x 14 weeks
  • Type: optional
  • 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
  • Labs: 1 Hour(s) per week x 14 weeks
  • Type: optional
  • 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
  • Labs: 1 Hour(s) per week x 14 weeks
  • Type: optional
  • 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
  • Type: optional
  • 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
  • Type: optional

Reference week

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