Talks & Teaching
Since 2019, I have a teaching position at the University of Würzburg. In addition, I have been teaching at the AniMove Science Schools for several years. Find out about my talks and teaching below.
In this course, ecology students are introduced to remote sensing and geo-analysis for ecological research applications and learn to independently use remote sensing in their own research fields. In the first week, students are introduced to remote sensing and geo-spatial data and methods and learn to use
Duration: 10 sessions (full days, 2-weeks block)
In this guest lecture, Arctic winter ecology MSc. students were introduced to satellite and UAV remote sensing and geo-analysis for ecological research applications in the Arctic. The guest lecture was hold on invitation by Prof. Dr. Simone Lang (UNIS) as part of a six-week international MSc. course on Arctic winter ecology. On the first day, students were introduced to remote sensing research applications, physical principles of remote sensing and fundamental methods for remote sensing data analysis. In an hands-on session, they learned to handle, visualize and analyze remote sensing data from both Earth observation satellites and Unoccupied Aerial Vehicles (UAVs) using geo-spatial software, including QGIS and
Duration: 2 sessions (full days, 2-days block)
Interactive visualizations using
Duration: 3 sessions (1.5 hours each)
This methodological course introduces Deep Learning with a practical focus on how to use it for image processing in Earth observation. Students get to know the principles behind the design of Neural Networks and the training thereof using Deep Learning. They learn about loss, backpropagation, optimization, activation functions & vanishing gradients, over- & underfitting, regularization, augmentation, convolutions, layers of state vs. stateless layers, sequential and non-sequential network designs for image processing tasks such as classification and segmentation etc. The course is taught in
Duration: 12 sessions (1.5 hours each)
Remote sensing applications; physical principles (electromagnetic radiation, absorption, emission, reflectance, optics, spectral information); spatial raster & vector data types; coordinate reference systems & projections; QGIS &
Duration: 10 sessions (full days, 2-weeks block)
This modelling course introduces concepts and methods to Earth observation students who want to learn to work with animal movement trajectories, a special type of spatio-temporal data, and integrate such with Earth observation analyses. The course aims to find data-driven answers to questions such as: Why do animals move through the landscape the way they do? How are they impacted by their environments? And: Which environmental conditions are tied to what kind of of movement behavior? While the course introduces background knowledge on topics such as movement theory, the effects of discretely observing continuous processes (e.g. sampling rate, autocorrelation, bias etc.), scale-dependencies/matching, tracking approaches and location error, its practical focus lays on methods to handle and analyze tracking data (e.g. using geometric & variance component analyses, behavioral segmentation, area-metrics such as home ranges, remote-sensing driven trajectory analysis incl. corridor analysis & habitat analysis) as well as tracking data in combination with remotely sensed environmental data (e.g. through resource utilization modelling, resource selection modelling and step selection modelling). This leads students to eventually be able to independently spot patterns in movement data, make connections to environmental conditions, and, finally, jointly model movement tracking and remotely sensed environmental data.
Duration: 5 sessions (full days, 1-week block)
Basics of
Duration: 12 sessions (1.5 hours each)
A practical guide towards building custom GEE processing pipelines natively in
Duration: 1 session (1.5 hours)
This methodological course introduces Deep Learning with a practical focus on how to use it for image processing in Earth observation. Students get to know the principles behind the design of Neural Networks and the training thereof using Deep Learning. They learn about loss, backpropagation, optimization, activation functions & vanishing gradients, over- & underfitting, regularization, augmentation, convolutions, layers of state vs. stateless layers, sequential and non-sequential network designs for image processing tasks such as classification and segmentation etc. The course is taught in
Duration: 12 sessions (1.5 hours each)
This course introduces remote sensing field methods to ecologists. They are get to know field campaigning (sampling methods, routing, positioning etc.), in-situ data sampling (parameters, field devices such as spectrometers, soil moisture probes etc.) and UAS (drone) imagery acquisition (platforms, sensors, flight planning, licensing, training). This prepares them for a field day at a research site where data are collected under real-world research conditions. Afterwards, they learn to handle, process and analyse the recorded data and turn them into interpretable information.
Duration: 10 sessions (full days, 2-weeks block)
This modelling course introduces concepts and methods to Earth observation students who want to learn to work with animal movement trajectories, a special type of spatio-temporal data, and integrate such with Earth observation analyses. The course aims to find data-driven answers to questions such as: Why do animals move through the landscape the way they do? How are they impacted by their environments? And: Which environmental conditions are tied to what kind of of movement behavior? While the course introduces background knowledge on topics such as movement theory, the effects of discretely observing continuous processes (e.g. sampling rate, autocorrelation, bias etc.), scale-dependencies/matching, tracking approaches and location error, its practical focus lays on methods to handle and analyze tracking data (e.g. using geometric & variance component analyses, behavioral segmentation, area-metrics such as home ranges, remote-sensing driven trajectory analysis incl. corridor analysis & habitat analysis) as well as tracking data in combination with remotely sensed environmental data (e.g. through resource utilization modelling, resource selection modelling and step selection modelling). This leads students to eventually be able to independently spot patterns in movement data, make connections to environmental conditions, and, finally, jointly model movement tracking and remotely sensed environmental data.
Duration: 5 sessions (full days, 1-week block)
Remote sensing applications; physical principles (electromagnetic radiation, absorption, emission, reflectance, optics, spectral information); spatial raster & vector data types; coordinate reference systems & projections; QGIS &
Duration: 10 sessions (full days, 2-weeks block)
Basics of
Duration: 12 sessions (1.5 hours each)
Remote sensing of forests, vulnerabilities & risks, biodiversity, wildlife ecology, natural resources, fire & burnt areas, coasts, diseases & health, agriculture, soil, land cover & land use, human settlements, policy etc.
Duration: 12 sessions (1.5 hours each)
Duration: 1 session (2.5 hours)
Duration: 2 sessions (full days)
This course introduces remote sensing field methods to ecologists. They are get to know field campaigning (sampling methods, routing, positioning etc.), in-situ data sampling (parameters, field devices such as spectrometers, soil moisture probes etc.) and UAS (drone) imagery acquisition (platforms, sensors, flight planning, licensing, training). This prepares them for a field day at a research site where data are collected under real-world research conditions. Afterwards, they learn to handle, process and analyse the recorded data and turn them into interpretable information.
Duration: 10 sessions (full days, 2-week block)
This methodological course introduces Deep Learning with a practical focus on how to use it for image processing in Earth observation. Students get to know the principles behind the design of Neural Networks and the training thereof using Deep Learning. They learn about loss, backpropagation, optimization, activation functions & vanishing gradients, over- & underfitting, regularization, augmentation, convolutions, layers of state vs. stateless layers, sequential and non-sequential network designs for image processing tasks such as classification and segmentation etc. The course is taught in
Duration: 12 sessions (1.5 hours each)
(Interactive) visualizations using
Duration: 5 sessions (1.5 hours each)
This modelling course introduces concepts and methods to Earth observation students who want to learn to work with animal movement trajectories, a special type of spatio-temporal data, and integrate such with Earth observation analyses. The course aims to find data-driven answers to questions such as: Why do animals move through the landscape the way they do? How are they impacted by their environments? And: Which environmental conditions are tied to what kind of of movement behavior? While the course introduces background knowledge on topics such as movement theory, the effects of discretely observing continuous processes (e.g. sampling rate, autocorrelation, bias etc.), scale-dependencies/matching, tracking approaches and location error, its practical focus lays on methods to handle and analyze tracking data (e.g. using geometric & variance component analyses, behavioral segmentation, area-metrics such as home ranges, remote-sensing driven trajectory analysis incl. corridor analysis & habitat analysis) as well as tracking data in combination with remotely sensed environmental data (e.g. through resource utilization modelling, resource selection modelling and step selection modelling). This leads students to eventually be able to independently spot patterns in movement data, make connections to environmental conditions, and, finally, jointly model movement tracking and remotely sensed environmental data.
Duration: 5 sessions (full days, 1-week block)
Remote sensing applications; physical principles (electromagnetic radiation, absorption, emission, reflectance, optics, spectral information); spatial raster & vector data types; coordinate reference systems & projections; QGIS &
Duration: 10 sessions (full days, 2-weeks block)
Basics of
Duration: 12 sessions (1.5 hours each)
Remote sensing of forests, vulnerabilities & risks, biodiversity, wildlife ecology, natural resources, fire & burnt areas, coasts, diseases & health, agriculture, soil, land cover & land use, human settlements, policy etc.
Duration: 12 sessions (1.5 hours each)
This methodological course introduces Deep Learning with a practical focus on how to use it for image processing in Earth observation. Students get to know the principles behind the design of Neural Networks and the training thereof using Deep Learning. They learn about loss, backpropagation, optimization, activation functions & vanishing gradients, over- & underfitting, regularization, augmentation, convolutions, layers of state vs. stateless layers, sequential and non-sequential network designs for image processing tasks such as classification and segmentation etc. The course is taught in
Duration: 12 sessions (1.5 hours each)
Spectrometric field sampling, hyperspectral image processing, spectral unmixing in
Duration: 2 sessions (full days, 2-days block)
(Interactive) visualizations using
Duration: 5 sessions (1.5 hours each)
This modelling course introduces concepts and methods to Earth observation students who want to learn to work with animal movement trajectories, a special type of spatio-temporal data, and integrate such with Earth observation analyses. The course aims to find data-driven answers to questions such as: Why do animals move through the landscape the way they do? How are they impacted by their environments? And: Which environmental conditions are tied to what kind of of movement behavior? While the course introduces background knowledge on topics such as movement theory, the effects of discretely observing continuous processes (e.g. sampling rate, autocorrelation, bias etc.), scale-dependencies/matching, tracking approaches and location error, its practical focus lays on methods to handle and analyze tracking data (e.g. using geometric & variance component analyses, behavioral segmentation, area-metrics such as home ranges, remote-sensing driven trajectory analysis incl. corridor analysis & habitat analysis) as well as tracking data in combination with remotely sensed environmental data (e.g. through resource utilization modelling, resource selection modelling and step selection modelling). This leads students to eventually be able to independently spot patterns in movement data, make connections to environmental conditions, and, finally, jointly model movement tracking and remotely sensed environmental data.
Duration: 5 sessions (full days, 1-week block)
Basics of
Duration: 12 sessions (1.5 hours each)
Remote sensing of forests, vulnerabilities & risks, biodiversity, wildlife ecology, natural resources, fire & burnt areas, coasts, diseases & health, agriculture, soil, land cover & land use, human settlements, policy etc.
Duration: 12 sessions (1.5 hours each)
introduction into machine learning, deep neural networks, computer vision, discriminative modelling; (web-)APIs, web protocols, machine-to-machine communication; development environments, code maintenance, unit testing, continuous integration, version control etc.
Duration: 12 sessions (1.5 hours each)
Spectrometric field sampling, hyperspectral image processing, spectral unmixing in
Duration: 2 sessions (full days, 2-days block)
(Interactive) visualizations using
Duration: 5 sessions (1.5 hours each)
Duration: 1 session (2.5 hours)
Duration: 1 session (2.5 hours)
introduction into machine learning, deep neural networks, computer vision, discriminative modelling; (web-)APIs, web protocols, machine-to-machine communication; development environments, code maintenance, unit testing, continuous integration, version control etc.
Duration: 12 sessions (1.5 hours each)
Title of the talk: Potentials of integrating animal movement tracking data with remote sensing.
Duration: 1 session (2.5 hours)
Duration: 1 session (2.5 hours)
2025
Seminar “Introduction into remote sensing for ecological analyses”, Biology MSc., University of Würzburg
QGIS
and R
for geo-spatial analysis. In the second week, they apply remote sensing and geo-analysis to answer their own real-world ecological research questions. At the end of the course, they present their remote-sensing-enabled findings.
Guest Lecture “Satellite and UAV remote sensing for arctic ecology research”, Arctic Winter Ecology MSc., UNIS, Svalbard
R
. Afterwards, students were introduced to UAV remote sensing sensors and platforms as well as mission planning. The day finished with the design of a real-world field campaign for the next day, including in-situ snow data collection and UAV thermal and LiDAR data collection using two drones. On the second day, students learned in the field how to in-situ collecting snow parameters,, including snow depth, snow wetness, snow temperatures, snow hardness etc. To geo-locate in-situ measurements with high accuracy, students learned to use real-time kinematic (RTK) corrected GPS. Afterwards, two UAV systems were flown to map the research area, equipped with a thermal and a LiDAR sensor. Later-on, students had the chance to experiment with the data they had collected themselves, e.g. by validating UAV-derived snow depth or snow temperature data against their own measurements. The data collected during the course are of high potential value for Arctic ecology research, as they can be used to quantify the impact of snow properties on vegetation phenology and resource development in spring and summer.
2024
Seminar “Building web applications and interactive visualizations”, EAGLE MSc., University of Würzburg
shiny
for R
in combination with leaflet
, plotly
, ggplot2
and LaTeX
. Basics of web design using html
, css
and javascript
. Basics on Declarative Programming and Lazy Execution. Introduction into client-server communication using reactive contexts and event observation. Introduction into basic UI design such as dashboard UIs, interactive maps, interactive plots etc.
Seminar “Deep learning for Earth observation”, EAGLE MSc., University of Würzburg
R
and Pyhton
, mainly using keras
and tensorflow
.
Seminar “Introduction into remote sensing for ecological analyses”, Biology MSc., University of Würzburg
R
; R
for spatial & remote sensing data analysis; spectral indices; basic modelling (classifiers, regressions); generating experience-driven ground truth using digitization; supervised classifications; (statistical) validation; accuracy assessment; scale of data vs. scale of observation targets; resolutions.
Seminar “Animal movement tracking data analysis for Earth observation”, EAGLE MSc., University of Würzburg
2023
Seminar “Introduction into programming and geo-statistics”, EAGLE MSc., University of Würzburg
R
& QGIS; version control using git; R
in comparison to other languages (interpreter vs. compiler, memory management etc.); programming paradigms; procedural vs. object-oriented vs. functional programming; types/modes, structures, indexing; implicit vs. explicit type conversion; control flow constructs & vectorization; functions; package building; data visualization; statistics; spatial data analysis; image processing; classification models etc.
Seminar “Cloud Computing: Google Earth Engine in R”, EAGLE MSc., University of Würzburg
R
using rgee
.
Seminar “Deep learning for Earth observation”, EAGLE MSc., University of Würzburg
R
and Pyhton
, mainly using keras
and tensorflow
.
Seminar “Remote sensing field methods for ecological analyses”, Biology MSc., University of Würzburg
Seminar “Animal movement tracking data analysis for Earth observation”, EAGLE MSc., University of Würzburg
Seminar “Introduction into remote sensing for ecological analyses”, Biology MSc., University of Würzburg
R
; R
for spatial & remote sensing data analysis; spectral indices; basic modelling (classifiers, regressions); generating experience-driven ground truth using digitization; supervised classifications; (statistical) validation; accuracy assessment; scale of data vs. scale of observation targets; resolutions.
2022
Seminar “Introduction into programming and geo-statistics”, EAGLE MSc., University of Würzburg
R
& QGIS; version control using git; R
in comparison to other languages (interpreter vs. compiler, memory management etc.); programming paradigms; procedural vs. object-oriented vs. functional programming; types/modes, structures, indexing; implicit vs. explicit type conversion; control flow constructs & vectorization; functions; package building; data visualization; statistics; spatial data analysis; image processing; classification models etc.
Lecture “Applications of Earth observation”, EAGLE MSc., University of Würzburg
Seminar “Movement data visualization in R”, AniMove Science School 2022, Max Planck Institute for Animal Behavior, Radolfzell, Germany
Seminar “Introduction into Remote sensing for animal movement analysis”, AniMove Science School 2022, Max Planck Institute for Animal Behavior, Radolfzell, Germany
Seminar “Remote sensing field methods for ecological analyses”, Biology MSc., University of Würzburg
Seminar “Deep learning for Earth observation”, EAGLE MSc., University of Würzburg
R
and Pyhton
, mainly using keras
and tensorflow
.
Seminar “Scientific graphics”, EAGLE MSc., University of Würzburg
LaTeX
, leaflet
, plotly
, ggplot2
, shiny
, basics of web design using html
, css
, javascript
(including frameworks such as bootstrap
), static site generation frameworks such as hugo
, jekyll
etc.
Seminar “Animal movement tracking data analysis for Earth observation”, EAGLE MSc., University of Würzburg
Seminar “Introduction into remote sensing for ecological analyses”, Biology MSc., University of Würzburg
R
; R
for spatial & remote sensing data analysis; spectral indices; basic modelling (classifiers, regressions); generating experience-driven ground truth using digitization; supervised classifications; (statistical) validation; accuracy assessment; scale of data vs. scale of observation targets; resolutions.
2021
Seminar “Introduction into programming and geo-statistics”, EAGLE MSc., University of Würzburg
R
& QGIS; version control using git; R
in comparison to other languages (interpreter vs. compiler, memory management etc.); programming paradigms; procedural vs. object-oriented vs. functional programming; types/modes, structures, indexing; implicit vs. explicit type conversion; control flow constructs & vectorization; functions; package building; data visualization; statistics; spatial data analysis; image processing; classification models etc.
Lecture “Applications of Earth observation”, EAGLE MSc., University of Würzburg
Seminar “Deep Learning for Earth observation”, EAGLE MSc., University of Würzburg
R
and Pyhton
, mainly using keras
and tensorflow
.
Seminar “Hyperspectral remote sensing”, EAGLE MSc., University of Würzburg
R
& QGIS etc.
Seminar “Scientific graphics”, EAGLE MSc., University of Würzburg
LaTeX
, leaflet
, plotly
, ggplot2
, shiny
, basics of web design using html
, css
, javascript
(including frameworks such as bootstrap
), static site generation frameworks such as hugo
, jekyll
etc.
Seminar “Animal movement tracking data analysis for Earth observation”, EAGLE MSc., University of Würzburg
2020
Seminar “Introduction into programming and geo-statistics”, EAGLE MSc., University of Würzburg
R
& QGIS; version control using git; R
in comparison to other languages (interpreter vs. compiler, memory management etc.); programming paradigms; procedural vs. object-oriented vs. functional programming; types/modes, structures, indexing; implicit vs. explicit type conversion; control flow constructs & vectorization; functions; package building; data visualization; statistics; spatial data analysis; image processing; classification models etc.
Lecture “Applications of Earth observation”, EAGLE MSc., University of Würzburg
Seminar “Advanced Programming for Spatial Analysis”, EAGLE MSc., University of Würzburg
Seminar “Hyperspectral remote sensing”, EAGLE MSc., University of Würzburg
R
& QGIS etc.
Seminar “Scientific graphics”, EAGLE MSc., University of Würzburg
LaTeX
, leaflet
, plotly
, ggplot2
, shiny
, basics of web design using html
, css
, javascript
(including frameworks such as bootstrap
), static site generation frameworks such as hugo
, jekyll
etc.
2019
Seminar “Visualizing Animal Movement in Synchronicity with Environmental Data using moveVis”, AniMove Science School 2019, Yale University, New Haven, CT, USA.
Seminar “Introduction to Remote Sensing”, AniMove Science School 2019, Yale University, New Haven, CT, USA.
Seminar “Advanced Programming for Spatial Analysis”, EAGLE MSc., University of Würzburg
Guest Lecture “Animal Movement Tracking for Remote Sensing”, Geography BSc., University of Würzburg
2018
Seminar “Visualizing Animal Movement in Synchronicity with Environmental Data using moveVis”, AniMove Science School 2018, Max Planck Institute for Animal Behavior, Radolfzell, Germany
2017
Seminar “Visualizing Animal Movement in Synchronicity with Environmental Data using moveVis”, AniMove Science School 2017, Max Planck Institute for Animal Behavior, Radolfzell, Germany