ENV-540 / 4 credits

Teacher: Tuia Devis

Language: English


Summary

This course covers optical remote sensing from satellites and airborne platforms. The different systems are presented. The students will acquire skills in image processing and machine/deep learning to extract end-products from the images such as land cover or risk maps.

Content

Courses content:

  1. Basic concepts of remote sensing and digital imaging
  2. Platforms and sensors
  3. Information extraction, filtering, visual information
  4. Image classification, with machine and deep learning algorithms
  5. Project: study a real world problem using remote sensing and image processing techniques.

Learning Prerequisites

Recommended courses

Machine learning CS-433

Important concepts to start the course

Intermediate skills in Python programming are considered a pre-requisite. All the exercises will be in Python. Basic knowledge in Pytorch is an advantage.

Learning Outcomes

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

  • Design an appropriate image processing pipeline to address a geospatial problem
  • Analyze the results of a machine learning methods
  • Implement an end to end machine learning pipeline
  • Discuss the pros and cons of different remote sensing sensors

Transversal skills

  • Collect data.
  • Access and evaluate appropriate sources of information.
  • Summarize an article or a technical report.
  • Write a scientific or technical report.
  • Use both general and domain specific IT resources and tools

Expected student activities

- Following classes

- exercises (individual or in small groups)

- preparing presentations

- reading club or research papers

- final projects in small groups

Assessment methods

  • Mid-term written test (50% of the final mark)
  • Project report (50% of the final mark)

 

In the programs

  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Image processing for Earth observation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Project: 1 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Image processing for Earth observation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Project: 1 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Image processing for Earth observation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Project: 1 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Image processing for Earth observation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Project: 1 Hour(s) per week x 14 weeks
  • Type: optional
  • Exam form: During the semester (winter session)
  • Subject examined: Image processing for Earth observation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Image processing for Earth observation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Image processing for Earth observation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Image processing for Earth observation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Image processing for Earth observation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Type: optional

Reference week

Friday, 9h - 11h: Lecture INF3

Friday, 11h - 12h: Exercise, TP INF3

Friday, 12h - 13h: Project, other INF3

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