Keynote: Tanya Berger-Wolf

Title: Imageomics: Images as the Source of Information about Life Abstract: Introducing the new field of imageomics: from images to biological traits using biology-structured machine learning. Images are the most abundant, readily available source for documenting life on the planet. Coming from natural history collections, laboratory scans, field studies, camera traps, wildlife surveys, autonomous vehicles on the land, water, and in the air, as well as tourists’ cameras, citizen scientists’ platforms, and posts on social media, there are millions of images of living organisms. But their power is yet to be harnessed for science and conservation. Even the traits of organisms cannot be readily extracted from images. The analysis of traits, the integrated products of genes and environment, is critical for biologists to predict effects of environmental change or genetic manipulation and to understand the significance of patterns in the four billion year evolutionary history of life. Knowledge-guided machine learning and computer vision can turn massive collections of images into high resolution information database about living organisms, enabling scientific discovery, conservation, and policy decisions. This is our vision of the new scientific field of imageomics.