The rapid and correct identification of specimens is an essential prerequisite for monitoring biodiversity and analysing biodiversity change. As there are fewer and fewer taxonomists who are able to correctly identify specimens, the use of artificial intelligence is becoming increasingly important for this task. However, the possibilities and limitations of the methodology have not yet been sufficiently explored. So far, apps that can identify findings only exist for a small fraction of Europe's biodiversity.

The project aims at providing training data for identifying shells of terrestrial molluscs through AI models that can be used in natural history collections and during fieldwork. The project is divided into two subprojects. The first aims at providing training data for the identification of the 17 European Vertigo species, a group of small gastropods including species listed in the EU Habitats and Species Directive. The second subproject aims at providing training data for identifying the ca 100 land snails from Tenerife, Canary Islands, which is part of the Mediterranean biodiversity hotspot. It is envisioned that the AI identification systems will greatly accelerate and facilitate species identification and will, thus, be of great interest for people involved in conservation planning and also for people working with natural collections, as well as students of malacology and citizen scientists. Both models will be tested in natural history collections and in the field.