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Labeled satellite imagery for training machine learning semantic segmentation models of coastal shorelines.

A dataset of Landsat, Sentinel, and Planetscope satellite images of coastal shoreline regions, and corresponding semantic segmentations. The dataset consists of folders of images and label images. Label images are images where each pixel is given a discrete class by a human annotator, among the following classes: a) water, b) whitewater/surf, c) sediment, and d) other. These data are intended only to be used as a training and validation dataset for a machine learning based image segmentation model that is specifically designed for the task of coastal shoreline satellite image semantic segmentation.

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Author(s) Daniel D Buscombe orcid
Publication Date 2025-03-25
Beginning Date of Data 1984
Ending Date of Data 2024
Data Contact
DOI https://doi.org/10.5066/P13EOBZQ
Citation Buscombe, D.D., 2025, Labeled satellite imagery for training machine learning semantic segmentation models of coastal shorelines.: U.S. Geological Survey data release, https://doi.org/10.5066/P13EOBZQ.
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Metadata Date 2025-03-25
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Citations of these data No citations of these data are known at this time.
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License http://www.usa.gov/publicdomain/label/1.0/
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Harvest Source: Coastal and Marine Geoscience Data System
Harvest Date: 2026-04-24T04:09:52.367Z