Topics Webinar | EO&GEO Series: Tracking Coastal Change with Geospatial Artificial Intelligence Techniques

Article Published by Remote Sensing

A Deep Learning Approach to Segment Coastal Marsh Tidal Creek Networks from High-Resolution Aerial Imagery

Authors: Richa Dutt, Collin Ortals, Wenchong He, Zachary Charles Curran, Christine Angelini, Alberto Canestrelli, Zhe Jiang

Remote Sens. 2024, 16(14), 2659; https://doi.org/10.3390/rs16142659

Coastal waterways and the natural systems that surround them are changing at an unprecedented rate due to population growth, intensification of land use, and climate change. In this webinar, we explore a series of emerging technologies developed by the University of Florida's Center for Coastal Solutions team for accomplishing such coastal monitoring and change assessment tasks. These include: 1) a new deep-learning based method for identifying tidal creeks and wetland features from high resolution remotely sensed images, 2) an AI-enhanced technique for assessing salt marsh morphology from UAV-derived images, and 3) a neural network classifier that combines remote sensing data with spatiotemporally distributed in situ sample data to monitor blooms of the potentially harmful algae, Karenia brevis (or red tide).

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