Introduction to Machine Learning in Remote Sensing

Garant předmětu:

Tomáš Bayer, Martin Landa

Lecturer:

Tomáš Bayer, Martin Landa

Tutor:

Tomáš Bayer, Martin Landa

Supervisor:

Department of Geomatics

Synopsis:

Machine learning is an integral part of data analysis and predictive modelling in many fields, including remote sensing. The aim of the course is to acquire basic knowledge of machine learning algorithms and principles of model generalization and practical design of process lines. In the course, students work independently on assigned examples of machine learning applications using remote sensing data. A prerequisite for the course is correct generalization of the trained model, including theoretical evaluation of overfitting and underfitting. In the projects, students create their own Python scripts and critically evaluate the results.

Requirements:

Informatics 2 (Python) and basics of GIS and remote sensing

Syllabus of lectures:

See Course Contents section

Syllabus of tutorials:

See Course Contents section

Study Objective:

The aim of the course is to acquire basic knowledge of practical application of machine and deep learning models in remote sensing and geoinformatics in general. It includes acquiring theoretical and practical knowledge of proper model generalization.

Study materials:

https://geo.fsv.cvut.cz/gwiki/155YUSU

Note:

Further information:

https://geo.fsv.cvut.cz/gwiki/155YUSU

Time-table for winter semester 2023/2024:

Time-table is not available yet

Time-table for summer semester 2023/2024:

Time-table is not available yet

The course is a part of the following study plans:

• Geodézie a kartografie, specializace Geomatika (compulsory elective course)
• Geodézie a kartografie, specializace Geomatika (compulsory elective course)