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. Both monitoring the health of coastal waterways and natural systems and documenting their pace and pattern of change are vital for informing coastal management and guiding solutions. In this webinar, Tracking Coastal Change with Geospatial Artificial Intelligence Techniques, we will explore a series of emerging technologies for achieving such coastal monitoring and change assessment tasks. Over the course of a series of short presentations, we will share the scope of interdisciplinary work being pursued by members of the University of Florida's Center for Coastal Solutions in the rapidly growing space of Geospatial AI, as well as highlight innovative application examples. These examples will include the exploration of: 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). We will welcome questions to engage and include the audience through the webinar.

Date: 21 November 2024
Time: : 4:00 p.m. CET | 10:00 a.m. EST | 11:00 p.m. CST

Webinar ID: 842 6788 0032

Webinar Secretariat: journal.webinar@mdpi.com

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