SBIR-STTR Award

There is a dearth of accessible information and innovative tools to map and obtain biomass data for analysis, conservation, and sustainable resource management of intertidal vegetation in the United States and globally. A major technical problem is the integration of biological and physical data at small spatial scales (<0.5 meters) with available large scale remote sensing imagery and data products (>2 meters). We propose Unoccupied Aerial Systems (UAS) can be used for quality control, co-registration, training, and validation of satellite or high-altitude or space-based aerial imagery. Specifically, we propose to develop training data using Unoccupied Aerial Systems (UAS) to bridge the gap between small- and large-scale ecological and remote sensing data, and to use Artificial Intelligence (AI) and Machine Learning (ML) to accurately map, classify, and estimate biomass of intertidal vegetation such as macro algae. This Phase I study will assess the feasibility to develop a deep learning model based on UAS acquired data and satellite imagery in the detection, classification, and biomass estimation of intertidal vegetation at local, as well as state-wide or regional scales.

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.