BiodivKI-2: Biodiversity assessment of biotope types through machine learning based on citizen science sound recordings and satellite images (Bio-O-Ton-2)
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- startdate:
2025
- enddate:
2028
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How biodiverse are our landscapes, and how quickly is this changing?
These seemingly simple questions are often difficult to answer in practice without substantial effort. Bio-O-Ton therefore takes a new approach: we combine audio recordings from citizen-science projects such as Dawn Chorus with high-resolution satellite data and analyze them using modern machine learning methods.
These seemingly simple questions are often difficult to answer in practice without substantial effort. Bio-O-Ton therefore takes a new approach: we combine audio recordings from citizen-science projects such as Dawn Chorus with high-resolution satellite data and analyze them using modern machine learning methods.
At the core of the project are three research questions:
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How can biodiversity be classified across taxa and in an application-oriented way?
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Can AI methods, particularly machine learning approaches, successfully perform this classification using citizen-science audio recordings and satellite imagery?
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How can the continuous integration of governmental practice into method development be designed and implemented as a co-creation process?
With this approach, we aim to develop a method that makes ecological changes visible at an earlier stage, reliably captures trends, and supports authorities, researchers, and the public in protecting valuable habitats.
Stakeholder-Driven Development of a Biodiversity Classification Scheme
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