Gathering to train different machine learning models was not as easy as today. Analyzing and labeling these data is time- and human-consuming, particularly if data from several sensors are gathered. Furthermore, the labeling process gets more complex if the data representation is complex. In this Master thesis, an Automated Data Labeling Pipeline is proposed, implemented, and evaluated. The pipeline takes driving recordings of the Dahlem Center for Machine Learning and Robotics as input. A ROS bag reader extracts the most relevant data. Two stages process the stored camera images (Image Processing Stage) and the LiDAR point cloud data (LiDAR Processing Stage. The last stage consists of a Sensor Fusion, where the image and LiDAR data processing results are fused to form a sensor fusion dataset. Each pipeline stage is evaluated on its own. Furthermore, several publicly available datasets and a newly created one are used to train different object detection models.
By completing this Master thesis, an Automated Data Labeling Pipeline to support a human labeler is realized.

In this thesis patterns of circadian rhythms in honey bee colonies are investigated, focusing on age-dependent variations, the effects of external influences, and the role of social interactions. Using comprehensive tracking data over two time periods (2016 and 2019), bee circadian rhythms are modeled by a cosinor model and statistically analyzed to examine their evolution over the life course. It is shown that circadian rhythms can be already observed in young bees and that they undergo a nonlinear maturation process with age. Synchronization of rhythms and earlier peaks of activity are more pronounced in older bees. The influence of external factors, especially weather conditions, on bee activity is investigated, showing that bees with robust circadian rhythms are more sensitive. In addition, the contribution of physical contact interactions to activity transmission within the colony is examined, which may form a component of social learning of circadian rhythms.