AI Model for Automated Detection and Mapping of Intertidal Vegetation

There is a growing need for a single source of truth data about intertidal zones that can used by lawmakers, resource managers, conservationists, engineers, pharmaceutical and food manufacturers, farmers and fishers, and scientists for better environmental planning and protection, particularly for the land-sea interface. Our Phase II research will solve this challenge by developing a data analytics platform that 1) compiles explicit oceanographic, biological, and spectral data gathered over time for macroalgae in intertidal zones, 2) performs objective, high-accuracy analyses leveraging Machine and Deep Learning technologies, 3) provides an AI-driven platform built to produce accurate results at multiple scales, and 4) distributes data and insights that can be consumed by organizations through an easy cloud-based, graphical user interface (GUI). The platform will deliver data solutions that incentivize conservation of resources, automate workflows enabling efficient and accurate modeling, offer simple and intuitive selfservice interfaces, and provide analytics and insights by experts.