Urban environments x Biodiversity
At GEBIKI we develop a holistic, non-invasive approach for biodiversity monitoring by integrating real-time sequencing, artificial intelligence (AI), and remote sensing to assess biodiversity across all trophic levels. This project is funded in frame of the BMBF initative Biodiversity and AI. We use AI tools to classify urban areas in Local Climate Zones (LCZs) and NANOPORE metagenome sequencing to investigate how the LCZs classification can accurately predict microbial diversity in large geographical scales. We further plan to implement AI classification of NANOPORE reads to predict live/dead microbial pattern in soil and how LCZs can influence those patterns. By collaborating with social science and industry partners, the project ensures its findings are relevant to human society and health, ultimately seeking to drive practical improvements in biodiversity monitoring and management. The project is part of the BMBF-Forschungsinitiative zum Erhalt der Artenvielfalt (FEdA).
At GEBIKI we develop a holistic, non-invasive approach for biodiversity monitoring by integrating real-time sequencing, artificial intelligence (AI), and remote sensing to assess biodiversity across all trophic levels. This project is funded in frame of the BMBF initative Biodiversity and AI. We use AI tools to classify urban areas in Local Climate Zones (LCZs) and NANOPORE metagenome sequencing to investigate how the LCZs classification can accurately predict microbial diversity in large geographical scales. We further plan to implement AI classification of NANOPORE reads to predict live/dead microbial pattern in soil and how LCZs can influence those patterns. By collaborating with social science and industry partners, the project ensures its findings are relevant to human society and health, ultimately seeking to drive practical improvements in biodiversity monitoring and management. The project is part of the BMBF-Forschungsinitiative zum Erhalt der Artenvielfalt (FEdA).
