The loss of biodiversity is progressing so rapidly that it can hardly be tracked by official monitoring using conventional mapping methods due to the large amount of time required. This interdisciplinary research project therefore focuses on the development of a species-independent biodiversity classification scheme that includes both species diversity and biotopes. In parallel, an innovative machine learning (ML) methodology is being developed to classify locations in Germany according to this scheme and to make local biodiversity visible (or audible). In addition to spatially and temporally high-resolution satellite data, Bio-O-Ton's ML-based approach is based on sound recordings collected by citizen scientists as part of ongoing projects. The results are presented in interactive browser-based user interfaces. This provides users with immediate feedback on local biodiversity and can thus support authorities with an early warning system for endangered landscapes. The main objective of this project is not only to investigate the technological aspects of this development, but also to ensure the acceptance and implementation of the resulting application in practice. This will be supported by targeted stakeholder processes to ensure that the results meet the needs and requirements of the relevant stakeholders and can support nature conservation authorities in monitoring.