Reeves Ian R. B.

No Thumbnail Available
Last Name
Reeves
First Name
Ian R. B.
ORCID
0000-0002-6675-3756

Filters

2021 - 2023 2
2
2
Reset filters

Settings

Search Results

Now showing 1 - 2 of 2
  • 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
    Sediment exchange across coastal barrier landscapes alters ecosystem extents
    (American Geophysical Union, 2023-07-17) Reeves, Ian R. B. ; Moore, Laura J. ; Valentine, Kendall ; Fagherazzi, Sergio ; Kirwan, Matthew L.
    Barrier coastlines and their associated ecosystems are rapidly changing. Barrier islands/spits, marshes, bays, and coastal forests are all thought to be intricately coupled, yet an understanding of how morphologic change in one part of the system affects the system altogether remains limited. Here we explore how sediment exchange controls the migration of different ecosystem boundaries and ecosystem extent over time using a new coupled model framework that connects components of the entire barrier landscape, from the ocean shoreface to mainland forest. In our experiments, landward barrier migration is the primary cause of back-barrier marsh loss, while periods of barrier stability can allow for recovery of back-barrier marsh extent. Although sea-level rise exerts a dominant control on the extent of most ecosystems, we unexpectedly find that, for undeveloped barriers, bay extent is largely insensitive to sea-level rise because increased landward barrier migration (bay narrowing) offsets increased marsh edge erosion (bay widening).