Possible Topics
| Topic | Short description | Type of degree | Contact person |
|---|---|---|---|
| Multispectral observation of plants in the Bot. Garden | How stressed are plants by our pollution and can this be detected by camera? Together with FB10 and 11, crops are stressed by soil pollution in a controlled environment. In this thesis, photos are to be taken regularly with a multispectral camera and the data analysed. No previous knowledge necessary. Preferred start: as of now | Bachelor / Master | Mona Goebel |
| Measurement in the Forest |
Together with Departments 10 and 11, data will be collected to comprehensively assess tree health. In this thesis, a measurement setup is to be planned and implemented. A multispectral camera will be positioned vertically upwards beneath the tree canopies in the city forest near Lichtwiese. Recommended prior knowledge: none Preferred start: November 2024 |
Bachelor | Mona Goebel |
| Comparison of tree segmentation tools |
In recent years, numerous tools have been developed for the segmentation of forest point clouds. An overview can be found here. In this thesis, the 5 Python-based tools should be tested and compared. A point cloud will be provided by us. Prior knowledge recommended: Github and Python |
Bachelor | Mona Goebel |
| Segmentation of rare classes using foundation models on 2D images |
Segmentation of areas and detection of objects is one of the most fundamental tasks in the field of remote sensing and image analysis. Machine learning models that have been trained on a training data set are often used for this purpose. The typical classes of a training dataset are: buildings, roads, forests, agriculture, …. But what happens with not-before seen areas and objects? In the case of natural disasters, these models would struggle with destroyed buildings, flooded areas, muddied roads or burnt forests. Working on imageries of natural disasters, like the Ahrtal flooding in 2021, these unusual areas need to be identified using an already trained machine learning model. That model can be personalized to new objects using tools already available. Still, some knowledge of programming in python and Matlab is recommended. |
Master | Kevin Qiu and Rewanth Ravindran , Co-supervised by Fraunhofer IOSB |
| Tree parameter extraction using LiDAR 3D point cloud data | Machine Learning methods are really transforming what can be done using remote sensing images in various environmental applications – from monitoring deforestation to identifying cracks in polar ice sheets. With the advent of drone based Laser-Scanning (LiDAR) data, the quality of details in the data in urban or agriculture or forest contexts is higher than ever before. 3D point clouds from LiDAR and machine learning models provide a very powerful combination for extracting useful information such as tree canopy segmentation or estimating biomass of the woody stems and branches, with relatively high accuracy. Before the model can be used for its individual purpose, it must be trained by feeding it with labelled training data. Generating training data by manually mapping hundreds of canopies in aerial photographs, for example, is time-consuming, labour-intensive and annoying. But with lack of sufficient labelled training dataset to create a new model, what other options exist? Can we use already existing general-purpose models built for image segmentation (see SAM by Meta AI) for our applications? Can we minimally adapt these models using existing methods so that we produce better accuracy in estimating tree parameters? These are some of the questions that we aim to be addressed by the master thesis. | Master | Rewanth Ravindran and Kevin Qiu , Co-supervised by Fraunhofer IOSB |
| Your own topic proposal on the subject of 3D point clouds, laser scanning, mobile mapping | If you are interested in working with data from 3D sensor systems but the presented topics to not fully fit your interests/requirements/qualification, just go ahead and contact us. In most cases, we will be able to find a topic for you fitting your specific interests and skills, both on bachelor and master level. Currently, we are mostly focusing on capture, automated reconstruction and all kinds of analysis of 2 specific kinds of environments: indoor building environments and forests. But we are also flexible towards other kinds of application scenarios if you come along with a specific idea. | Bachelor / Master | Mona Goebel |
| Classification of scanned building fragments for the reconstruction of historical buildings | The fragments of a Jewish Bima were captured by laser scanning. From these fragments the whole building was reconstructed manually. In this work, point clouds of the laser scanned fragments will be classified as building parts using neural networks (e.g. PointNet++). This serves as a first step to simplify the puzzling together of the fragments. | Master | Jakob Schmidt |