LV-UAV-Guide | Learning Outcomes

Learning Outcomes What you will know

This page collects together all of the “outcomes” associated with individual modules. Outcomes identify what students will know and be able to do if they master the material.

Course-level outcomes

Students mastering the material in this course will achieve the following student learning outcomes for the Master of Physical Geography program:

Base Competences: Students can apply knowledge of spatial data computing and simple mathematics appropriate to their discipline background.
Medium Competences: Students can analyze a problem, and identify and define the conceptual and computing requirements appropriate to its solution.
High Competences: Students can design, implement, evaluate and document a scientific question in an adequate way.

The module is intended to provide a solid overview of the extensive range of structure-from-motion (SfM) photogrammetry software and its use:

Identify which of the software packages provide good results and are cost effective.
Using the commercial software Agisoft Metashape, an optimized workflow for orthoimage generation will be demonstrated and practiced.
Optionally, this workflow is also explained and illustrated with the open source software OpenDroneMap.
Optional tutorials are available for special tasks.

Referencing modules: Creating High Quality Products

The module is intended to enable them to implement autonomous flight planning confidently and successfully. In particular:

Overview of the hardware and software market, especially of the planning and control software.
Hardware-independent optimized planning of the flight area taking into account the air law and the protection of the environment with optimal safety and efficiency.
Manual optimization of the flight plan and practice of the workflow
Safe execution of the flight task

Referencing modules: Autonomous flights made easy

This introduction is intended to serve three main purposes:

To get a first overview of the research approaches and projects in the broadest sense of applied environmental science.
To integrate the reading and reception of primary literature, i.e. studies, reviews and other compilations, into the research process.
To learn to compare the evaluation of what has been read with one’s own assessment and thus to create the basis for valid procedures.

Referencing modules: UAV based remote Sensing - A Brief Overview

The module is intended to provide an initial insight into the automated and evaluation of high-resolution orthoimages and the point clouds. Since this topic easily fills several courses, the possibilities of more complex evaluations with QGIS and comparable software will be presented. The module offers:

A lightning introduction to and classification of Object Based Image Analysis (OBIA), Machine Learning (ML and Deep Learning (DL) in relation to the task of the course.
a template example of an OBIA classification run with QGIS and Orfeo Toolbox (OTB)
the optional implementation of DL and ML classifications with R
A first classification and analysis of point clouds with the goal to develop at least in principle in this area

Referencing modules: Analysis of high resolution aerial data