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Labeled satellite imagery for training machine learning models that predict the suitability of semantic segmentation model outputs for shoreline extraction

March 24, 2025
A collection of data releases containing labeled satellite imagery for the purpose of training Machine Learning models to automate the task of shoreline extraction from satellite imagery. Shoreline mapping from satellite imagery, known as Satellite-Derived Shorelines or SDS, has the potential to transform coastal shoreline mapping for erosion hazard mapping and coastal resource assessment, among many potential uses. Automation of such tasks using Machine Learning is a crucial component of cost-saving and quality assurance for large-scale routine shoreline mapping. The associated satellite images with labeled classifications (https://doi.org/10.5066/P13EOBZQ) and image suitability (https://doi.org/10.5066/P14MDKVJ) datasets are available.
Publication Year 2025
Title Labeled satellite imagery for training machine learning models that predict the suitability of semantic segmentation model outputs for shoreline extraction
DOI 10.5066/P1N4VI7H
Authors Daniel (Contractor) D Buscombe
Product Type Data Release
Record Source USGS Asset Identifier Service (AIS)
USGS Organization Pacific Coastal and Marine Science Center
Rights This work is marked with CC0 1.0 Universal
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