Scarlett Raine

Papers

Total Citations

H-Index

About

Scarlett Raine is a researcher specializing in underwater computer vision and marine ecology, with a particular focus on applying machine learning techniques to automate the analysis of underwater imagery for ecological assessment. Her most recognized work addresses the challenging problem of detecting and classifying multiple seagrass species from underwater images, a task traditionally dependent on labor-intensive manual review by trained divers or operators. By leveraging automated image analysis, Raine's research offers a scalable solution to processing the vast quantities of visual data generated by diver-led or robotic underwater surveys, significantly reducing both the time and cost associated with marine habitat monitoring. This contribution sits at a compelling intersection of artificial intelligence, robotics, and marine biology, making advanced ecological data collection more accessible and efficient for researchers and conservationists alike. Her 2020 paper on multi-species seagrass detection has begun attracting academic attention, reflecting the growing relevance of her work as autonomous underwater vehicles become increasingly central to ocean science. Raine represents an emerging voice in the application of deep learning to environmental monitoring, with potential for meaningful impact on how we understand and protect fragile marine ecosystems.