Publications
Labeling poststorm coastal imagery for machine learning: Measurement of interrater agreement 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...
Authors
Evan Goldstein, Daniel D. Buscombe, Eli Lazarus, Somya Mohanty, Shah Rafique, K Anarde, Andrew Ashton, Tomas Beuzen, Katherine Castagno, Nicholas Cohn, Matthew Conlin, Ashley Ellenson, Megan Gillen, Paige Hovenga, Jin-Si Over, Rose V. Palermo, Katherine Ratlif, Ian Reeves, Lily Sanborn, Jessamin Straub, Luke Taylor, Elizabeth Wallace, Jonathan Warrick, Phillipe Alan Wernette, Hannah Williams
By
Ecosystems Mission Area, Natural Hazards Mission Area, Coastal and Marine Hazards and Resources Program, Pacific Coastal and Marine Science Center, Southwest Biological Science Center, Woods Hole Coastal and Marine Science Center, Supplemental Appropriations for Disaster Recovery Activities, Hurricane Florence, Hurricane Isaias, Hurricane Michael, Hurricanes
Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation
IntroductionStructure from motion (SFM) has become an integral technique in coastal change assessment; the U.S. Geological Survey (USGS) used Agisoft Metashape Professional Edition photogrammetry software to develop a workflow that processes coastline aerial imagery collected in response to storms since Hurricane Florence in 2018. This report details step-by-step instructions to create...
Authors
Jin-Si Over, Andrew Ritchie, Christine Kranenburg, Jenna A. Brown, Daniel Buscombe, Tom Noble, Christopher Sherwood, Jonathan A. Warrick, Phillipe Wernette
By
Ecosystems Mission Area, Natural Hazards Mission Area, Coastal and Marine Hazards and Resources Program, Maryland-Delaware-D.C. Water Science Center, Pacific Coastal and Marine Science Center, Southwest Biological Science Center, St. Petersburg Coastal and Marine Science Center, Woods Hole Coastal and Marine Science Center, Hurricane Florence, Hurricanes
Impact of SST and surface waves on Hurricane Florence (2018): A coupled modeling investigation Impact of SST and surface waves on Hurricane Florence (2018): A coupled modeling investigation
Hurricane Florence (2018) devastated the coastal communities of the Carolinas through heavy rainfall that resulted in massive flooding. Florence was characterized by an abrupt reduction in intensity (Saffir-Simpson Category 4 to Category 1) just prior to landfall and synoptic-scale interactions that stalled the storm over the Carolinas for several days. We conducted a series of numerical...
Authors
Joseph Zambon, Ruoying He, John Warner, Christie Hegermiller