Coast Train: Massive Library of Labeled Coastal Images to Train Machine Learning for Coastal Hazards and Resources Active

Scientists who study coastal ecosystems and hazards such as hurricanes, flooding, and cliff failure collect lots of photographs of coastal environments from airplanes and drones. A large area can be surveyed at high resolution and low cost. Additionally, satellites such as Landsat have provided imagery of the Nation’s coastlines every few days for decades. Scientist’s ability to understand coastal hazards would be greatly improved if this wealth of imagery could be ‘mined’ automatically by computers. We want to automate the process of identifying and labelling each region of the image from a set of categories (e.g. bare land, water, woody vegetation, herbaceous vegetation). We need to train a computer to recognize the same things that a human can and that will require a very large number of labeled images. We’d like to work with STEP-UP students to do most of the labelling using software we write especially for the purpose.



Principal Investigator : Phillipe A Wernette

Co-Investigator : Sara L Zeigler, Nicholas M Enwright

Cooperator/Partner : Daniel D Buscombe, Evan Goldstein