Goldstein Evan B.

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Last Name
Goldstein
First Name
Evan B.
ORCID
0000-0001-9358-1016

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  • Article
    Labeling poststorm coastal imagery for machine learning: measurement of interrater agreement
    (American Geophysical Union, 2021-09-03) Goldstein, Evan B. ; Buscombe, Daniel ; Lazarus, Eli ; Mohanty, Somya D. ; Rafique, Shah Nafis ; Anarde, Katherine A. ; Ashton, Andrew D. ; Beuzen, Tomas ; Castagno, Katherine ; Cohn, Nicholas ; Conlin, Matthew P. ; Ellenson, Ashley ; Gillen, Megan N. ; Hovenga, Paige A. ; Over, Jin-Si ; Palermo, Rose V. ; Ratliff, Katherine M. ; Reeves, Ian R. B. ; Sanborn, Lily H. ; Straub, Jessamin A. ; Taylor, Luke A. ; Wallace, Elizabeth J. ; Warrick, Jonathan ; Wernette, Phillipe ; Williams, Hannah E.
    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 process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.
  • Article
    Anthropogenic controls on overwash deposition : evidence and consequences
    (John Wiley & Sons, 2015-12-29) Rogers, Laura J. ; Moore, Laura J. ; Goldstein, Evan B. ; Hein, Christopher J. ; Lorenzo-Trueba, Jorge ; Ashton, Andrew D.
    Accelerated sea level rise and the potential for an increase in frequency of the most intense hurricanes due to climate change threaten the vitality and habitability of barrier islands by lowering their relative elevation and altering frequency of overwash. High-density development may further increase island vulnerability by restricting delivery of overwash to the subaerial island. We analyzed pre-Hurricane Sandy and post-Hurricane Sandy (2012) lidar surveys of the New Jersey coast to assess human influence on barrier overwash, comparing natural environments to two developed environments (commercial and residential) using shore-perpendicular topographic profiles. The volumes of overwash delivered to residential and commercial environments are reduced by 40% and 90%, respectively, of that delivered to natural environments. We use this analysis and an exploratory barrier island evolution model to assess long-term impacts of anthropogenic structures. Simulations suggest that natural barrier islands may persist under a range of likely future sea level rise scenarios (7–13 mm/yr), whereas developed barrier islands will have a long-term tendency toward drowning.