Together with partners from the DFKI Niedersachsen and the Jade Hochschule this project accelerates natural science research through targeted identification and exploitation of capability resulting from digital technologies. The goal is the part automatisation of execution of experimentation by AI powered Change Event based Sensor Sampling (ChESS). ChESS’s purpose is to detect concept changes that may be relevant to investigate the health of ecosystems. E.g., in intertidal marine ecosystems sudden flooding may cause dramatic changes to biodiversity, and it may also be of scientific interest to take more frequent sensor samples during the time leading up to such events. An AI will be developed to detect early indications of environment change and trigger more frequent sampling. In extreme cases such a system may trigger missions to prevent an ecosystem observation site from serious damage. Thus, the goal is to develop an AI system integrated into a mobile Mission Control System enabling early concept change detection in the field. Such AI techniques will need to process multi-modal data streams. I.e. a water level sensor alone may not be able to detect imminent flooding, but several sensors combined, such as water level volatility, rainfall and speed/direction of wind etc. ChESS will need to combine and analyse diverse kinds of data in real-time.