Micro-Remote Sensing

Welcome to the course website for LV-19-050-228 — gisma spatial science resources.

The Micro Remote Sensing course offers a comprehensive introduction to reproducible flight planning, UAV-based data acquisition, pre-processing, and the example-based analysis of high-resolution, low-cost image data — using both commercial and open-source software tools.

The course places equal emphasis on the practical acquisition of high-resolution aerial imagery with UAV-based camera systems and on the generation, analysis, interpretation, and scientific validation of the resulting data products.

Who Should Take This Course?

This course is intended for learners of all levels who possess a basic understanding of their operating system’s structure. While basic programming knowledge is not required, it may be helpful.

The course is particularly suitable for students and practitioners interested in UAV-based remote sensing, reproducible geospatial workflows, and the critical evaluation of spatial products derived from image data.

Course Objective

Throughout this course you will learn how to acquire, process, analyse, and scientifically validate high-resolution UAV-based image data and derived products.

The overarching goal is to develop a reproducible and well-argued workflow that links field acquisition, data processing, product generation, and critical evaluation. This is not a purely technical exercise, but a research-oriented workflow challenge that connects practical UAV operation, spatial data processing, and scientific reasoning.

The course evolves in three conceptual phases:

  1. Exploration – Identify what makes a UAV-based workflow, product, or interpretation robust, weak, reproducible, or misleading. Define what “quality” and “validation” mean in relation to your chosen task.
  2. Development – Apply methods learned in class to iteratively improve your workflow, including flight planning, acquisition strategy, preprocessing, product generation, and evaluation.
  3. Synthesis – Present and defend a transparent, reproducible workflow and a scientifically interpretable final product, including a critical discussion of limitations, uncertainty, and methodological choices.

Didactic Concept

The course consists of a sequential (but not strictly linear) set of modules, each designed to be completed in roughly 2–4 weeks.

Each module follows this structure:

The labels provide a quick overview of the content and importance of each unit within a module:

  • mandatory units define the minimum course requirements
  • recommended units should be considered strongly
  • other resources support internal differentiation for learners seeking deeper or broader engagement

Final Deliverables

At the end of the course, each group submits three complementary products:

1. Transparent repository (core submission)

A public or shared repository (e.g. GitHub, GitLab, or institutional server) that contains:

  • all relevant scripts, settings, metadata, and documentation used to derive the final products
  • a reproducible workflow showing how you moved from acquisition planning and raw data to processed outputs and final interpretation
  • a clear project structure and a short README explaining the workflow logic, data organisation, and main methodological decisions

The repository represents the traceable backbone of the project and demonstrates reproducibility, technical care, and collaborative organisation.

2. Poster

A concise visual summary of:

  • the research question or task and the chosen workflow
  • the main methodological steps, from acquisition to final product
  • the final outputs (e.g. orthomosaic, point cloud, classification, segmentation, or derived analysis result)
  • the main quality criteria, validation steps, and limitations

The poster should communicate the project as if presented at a professional conference in geoinformatics, remote sensing, or applied spatial analysis.

3. Five-minute pitch (presentation)

Each group presents its poster in a short oral pitch (max. 5 minutes).

The goal is to argue convincingly why the workflow, products, and interpretation are methodologically sound, reproducible, and scientifically meaningful. The pitch should demonstrate technical understanding, critical reflection, and the ability to justify key decisions.

Evaluation Criteria

Each final submission is evaluated across five dimensions, 0–2 points each (max. 10 points):

Criterion 0 1 2
Workflow design unclear or ad hoc workflow partly structured workflow coherent, transparent, and logically structured workflow from acquisition to product
Methodological rigour undocumented or arbitrary choices partly justified methods well-justified and reproducible methodological decisions, including parameters and processing logic
Product quality & validation outputs not interpretable or not checked outputs plausible but weakly evaluated outputs critically assessed with explicit quality criteria, validation, and discussion of uncertainty
Transparency & reproducibility no clear documentation partial scripts / incomplete metadata complete, documented, and reproducible repository with clear structure
Communication quality unclear poster / pitch partly coherent presentation clear, concise, visually convincing, and scientifically well-argued presentation

Learning Outcome

By completing the full project you will be able to:

  • plan and document UAV-based data acquisition in a methodologically transparent way
  • generate and evaluate spatial products from high-resolution aerial imagery
  • critically assess the quality, limits, and scientific validity of derived results
  • communicate and defend a reproducible workflow from data acquisition to final interpretation

Instructor

Chris Reudenbach
Geoinformatics Working Group (GISMA)
Department of Geography, University of Marburg

The course materials are developed and hosted on
GitHub

The responsibility for the content lies with the instructors.
Statements, opinions, and conclusions are those of the instructors and do not necessarily reflect the views of the University of Marburg.