EeLISA: Combating Global Warming Through the Rapid Analysis of Eelgrass Wasting Disease

Authors

  • Brendan H. Rappazzo Cornell University
  • Morgan E. Eisenlord Cornell University
  • Olivia J. Graham Cornell University
  • Lillian R. Aoki Cornell University
  • Phoebe D. Dawkins University of California, Irvine
  • Drew Harvell Cornell University
  • Carla Gomes Cornell University

Keywords:

Semantic Segmentation, Computational Sustainability, AI System, Eelgrass, Segmentation, HCI, Deep Learning, Neural Networks, Computer Vision, Marine Ecology

Abstract

Global warming is the greatest threat facing our planet, and is causing environmental disturbance at an unprecedented scale. We are strongly positioned to leverage the advancements of Artificial Intelligence (AI) and Machine Learning (ML) which provide humanity, for the first time in history, an analysis and decision making tool at massive scale. Strong evidence supports that global warming is contributing to marine ecosystem decline, including eelgrass habitat. Eelgrass is affected by an opportunistic marine pathogen and infections are likely exacerbated by rising ocean temperatures. The necessary disease analysis required to inform conservation priorities is incredibly laborious, and acts as a significant bottleneck for research. To this end, we developed EeLISA (Eelgrass Lesion Image Segmentation Application). EeLISA enables ecologist experts to train a segmentation module to perform this crucial analysis at human level accuracy, while minimizing their labeling time and integrating into their existing workflow. EeLISA has been deployed for over 16 months, and has facilitated the preparation of four manuscripts including a critical eelgrass study ranging from Southern California to Alaska. These studies, utilizing EeLISA, have led to scientific insight and discovery in marine disease ecology.

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Published

2021-05-18

How to Cite

Rappazzo, B. H., Eisenlord, M. E., Graham, O. J., Aoki, L. R., Dawkins, P. D., Harvell, D., & Gomes, C. (2021). EeLISA: Combating Global Warming Through the Rapid Analysis of Eelgrass Wasting Disease. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15156-15165. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17779

Issue

Section

IAAI Technical Track on Highly Innovative Applications of AI