Labeling Poststorm Coastal Imagery for Machine Learning: Measurement of Interrater Agreement
Name
Earth and Space Science - 2021 - Goldstein - Labeling Poststorm Coastal Imagery for Machine Learning Measurement of.pdf
Size
1.34 MB
Format
Adobe PDF
Checksum (MD5)
88b7a0038d8c3ce9daf5a766399c5242
Date Issued
September 3, 2021
Journal
Earth and Space Science
Publisher
American Geophysical Union
Citation
Goldstein, E. B., Buscombe, D., Lazarus, E. D., Mohanty, S. D., Rafique, S. N., Anarde, K. A., et al. (2021). Labeling poststorm coastal imagery for machine learning: Measurement of interrater agreement. Earth and Space Science, 8, e2021EA001896.
Version
Final published version
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.
MIT Department
Joint Program in Oceanography/Applied Ocean Science and Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1029/2021EA001896
