Labeling Poststorm Coastal Imagery for Machine Learning: Measurement of Interrater Agreement
Evan B. Goldstein, Daniel Buscombe, Eli D. Lazarus, Somya D. Mohanty, Shah Nafis Rafique, Katherine Anarde, Andrew D. Ashton, Tomas Beuzen, Katherine A. Castagno, Nicholas Cohn, Matthew P. Conlin, Ashley Ellenson, Megan N. Gillen, Paige A. Hovenga, Jin‐Si R. Over, Rose V. Palermo, Katherine Ratliff, Ian Reeves, Lily H. Sanborn, Jessamin A. Straub, Luke Taylor, Elizabeth Wallace, Jonathan A. Warrick, Phillipe A. Wernette, Hannah Williams
Abstract
Abstract 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.