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Hurricane Isaias

Information about Hurricane Isaias.



New USGS Map Identifies Non-Native Aquatic Species Potentially Spread by Hurricane Isaias Floodwaters


USGS Field Crews Document Isaias' Effects Along Atlantic Coast


USGS Field Crews Document Isaias’ Effects Along Atlantic Coast


Labeling poststorm coastal imagery for machine learning: Measurement of interrater agreement

Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data-driven models are only as good as the data used for training, and this points to the importance of high-quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time-consuming, manual proc
Evan B. Goldstein, Daniel D. Buscombe, Eli D. Lazarus, Somya Mohanty, Shah N. Rafique, K A Anarde, Andrew D Ashton, Tomas Beuzen, Katherine A. Castagno, Nicholas Cohn, Matthew P. Conlin, Ashley Ellenson, Megan Gillen, Paige A. Hovenga, Jin-Si R. Over, Rose V. Palermo, Katherine Ratlif, Ian R Reeves, Lily H. Sanborn, Jessamin A. Straub, Luke A. Taylor, Elizabeth J. Wallace, Jonathan Warrick, Phillipe Alan Wernette, Hannah E Williams

A survey of storm-induced seaward-transport features observed during the 2019 and 2020 hurricane seasons

Hurricanes are known to play a critical role in reshaping coastlines, but often only impacts on the open ocean coast are considered, ignoring seaward-directed forces and responses. The identification of subaerial evidence for storm-induced seaward transport is a critical step towards understanding its impact on coastal resiliency. The visual features, found in the National Oceanic and Atmospheric
Jin-Si R. Over, Jenna A. Brown, Christopher R. Sherwood, Christie Hegermiller, Phillipe Alan Wernette, Andrew C. Ritchie, Jonathan Warrick