Machine learning models are powerful, yet they often sideline the domain experts who understand the data best. In conventional active learning pipelines, the model drives the process while the user simply responds, leaving domain knowledge underutilised and treating the annotator as a passive responder rather than an active contributor. This Read more
Passive Acoustic Monitoring (PAM) enables continuous and non-invasive biodiversity monitoring, but analysing large acoustic datasets remains difficult because sound event detectors usually require temporally precise annotations. Creating such instance-level labels is expensive and requires expert knowledge. At the same time, museum and community-run sound libraries provide large numbers of animal Read more
Passive acoustic monitoring (PAM) has become a powerful tool for studying wildlife by continuously recording environmental soundscapes. However, analysing large acoustic datasets remains highly time-consuming, as recordings are often annotated manually by domain experts. In this work, we investigate how machine learning can support scalable biodiversity monitoring by enabling efficient Read more
Building reliable AI models depends not only on how much data is annotated, but on the quality and meaning of the labels used during annotation. In many workflows, labels are flat, task-specific class names. They are easy to apply, but lack explicit semantic structure, provenance, and links to shared domain Read more
Image annotation remains a significant bottleneck in image analysis pipelines. In research settings especially, annotating large image corpora demands substantial effort, often forcing teams to compromise on quality by resorting to coarser methods like point annotations rather than full outlines. To address this, we collaborated with the Helmholtz Institute for Read more
The biodiversity crisis is maybe the most pressing issue of our time, which makes measuring the biodiversity an important precursor for improvement. Biodiversity assessment is limited by data management and analysis tools. Prior work has approached this problem by exploring the space of tools that can aide workflows of biodiversity Read more
Detecting animal sounds is an essential aspect of scientific research and conservation efforts. It provides valuable insights into biodiversity, species behavior, and ecosystem health. Monitoring of terrestrial and marine wildlife is a way to decrease biodiversity loss and improve species conservation. To achieve this goal passive acoustic monitoring (PAM) is Read more
Passive acoustic monitoring (PAM), the recording of sounds using microphones (e.g. in biosphere reserves), is an increasingly popular method for continuous, reproducible, scalable and cost-effective monitoring of wildlife [Sugai et al., 2018]. It is widely employed in various fields, including ecology, marine biology, and conservation, to study animal behavior, biodiversity, Read more