Thumper

The looming climate crisis threatens the integrity of habitats such as forests, which are essential for the survival of many species and the ecological balance. Protecting biodiversity requires effective monitoring of wildlife populations, which is often inefficient with current methods such as camera traps, especially in large, forested areas. The BAMBI project therefore utilises airborne light field sampling (ALFS) to detect animals despite vegetation cover using artificial intelligence. One challenge, however, is the detection of moving animals, which are difficult for AI to recognise. To improve this, the sister project THUMPER (Temporally-connected detection of Hidden, Underrepresented and Moving Populations for Estimation and Recognition) aims to develop new AI methods that combine temporally connected video images. In addition, synthetic data will be generated by CGI simulations and generative AI approaches to train the AI models.