Globally standardised species monitoring with sensor networks and deep learning models

With computer vision and deep learning, camera traps have become an important tool to improve our understanding of species responses to environmental change. Through computer eyes, it is potentially possible to effectively, continuously, and non-invasively observe organisms throughout diurnal and seasonal cycles and deep learning models can provide estimates of their abundance, biomass, and diversity. I will unpack and visualize the rich and multidimensional data that novel camera-enabled monitoring systems are capable of generating automatically and in a globally standardized manner. Through results from national and continental scale programs deploying insect camera traps as an example, I will provide a glimpse into the insights that can be derived from the trap images and what the future of automated monitoring might look like. I will also highlight outstanding challenges and future research avenues to facilitate the broad scale implementation of camera traps for day active and nocturnal insects as well as other taxa.