Deep Learning for Imbalanced Data
The Data Science Chair works with environmental data to contribute to the ongoing climate research. Based on low-cost sensors, map and geographical data, we develop novel approaches to environmental questions. These questions include "What is the air pollution at a given spot on earth?" and "What type of wine should I grow on my land given the development in climate in the next few decades?". While answering such questions is one concern of our research, the other objective is to raise awareness for the environment in the society.
We are currently working on the following projects:
Here is a list of selected publications.
Anomaly Detection in Beehives using Deep Recurrent Autoencoders in Proceedings of the 9th International Conference on Sensor Networks (SENSORNETS 2020) (2020). 142–149.
Collective Sensing Platforms in Participatory Sensing, Opinions and Collective Awareness (2017). 115–133.
Participatory sensing, opinions and collective awareness (2017). Springer.
Participatory Patterns in an International Air Quality Monitoring Initiative in PLoS ONE (2015). 10(8) e0136763.
Awareness and learning in participatory noise sensing in PLOS ONE (2013). 8(12) e81638.