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A 1.2 billion pixel human-labeled dataset for data-driven classification of coastal environments

January 20, 2023

The world’s coastlines are spatially highly variable, coupled-human-natural systems that comprise a nested hierarchy of component landforms, ecosystems, and human interventions, each interacting over a range of space and time scales. Understanding and predicting coastline dynamics necessitates frequent observation from imaging sensors on remote sensing platforms. Machine Learning models that carry out supervised (i.e., human-guided) pixel-based classification, or image segmentation, have transformative applications in spatio-temporal mapping of dynamic environments, including transient coastal landforms, sediments, habitats, waterbodies, and water flows. However, these models require large and well-documented training and testing datasets consisting of labeled imagery. We describe “Coast Train,” a multi-labeler dataset of orthomosaic and satellite images of coastal environments and corresponding labels. These data include imagery that are diverse in space and time, and contain 1.2 billion labeled pixels, representing over 3.6 million hectares. We use a human-in-the-loop tool especially designed for rapid and reproducible Earth surface image segmentation. Our approach permits image labeling by multiple labelers, in turn enabling quantification of pixel-level agreement over individual and collections of images.

Citation Information

Publication Year 2023
Title A 1.2 billion pixel human-labeled dataset for data-driven classification of coastal environments
DOI 10.1038/s41597-023-01929-2
Authors Daniel Buscombe, Phillipe Alan Wernette, Sharon Fitzpatrick, Jaycee Favela, Evan B. Goldstein, Nicholas Enwright
Publication Type Article
Publication Subtype Journal Article
Series Title Scientific Data
Index ID 70240774
Record Source USGS Publications Warehouse
USGS Organization National Wetlands Research Center; Pacific Coastal and Marine Science Center

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