Machine Learning for Ecosystems and Climate Modeling

In the past few years, applying data science and machine learning to ecosystems, environmental & climate data has become a central research area at our chair. We have successfully developed deep learning methods for improving climate models in the BigData@Geo project as well as the follow up project BigData@Geo 2.0 as well as machine learning-based air pollution models in the EveryAware and p2Map project. We’re also analyzing data from smart beehives to understand bee behavior and detect anomalies as swarming events in the we4Bee and BeeConnected projects.

• EveryAware - A project for collaborative collection of environmental sensor data with low cost sensors.

• p2Map - Learning understandable maps of air pollution leveraging a combination of low-cost mobile sensors.