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Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation
Coast Train is a library of images of coastal environments, annotations, and corresponding thematic label masks (or ‘label images’) collated for the purposes of training and evaluating machine learning (ML), deep learning, and other models for image segmentation. It includes image sets from both geospatial satellite, aerial, and UAV imagery and orthomosaics, as well as non-geospatial oblique and nadir imagery. Images include a diverse range of coastal environments from the U.S. Pacific, Gulf of Mexico, ... |
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Satellite-derived shorelines for the U.S. Gulf Coast states of Texas, Louisiana, Mississippi, and Florida for the period 1984-2022, obtained using CoastSat
This dataset contains shoreline positions derived from available Landsat satellite imagery for four states (Texas, Louisiana, Mississippi, and Florida) along the U.S. Gulf coast for the time period 1984 to 2022. An open-source toolbox, CoastSat (Vos and others, 2019a and 2019b), was used to classify coastal Landsat imagery and detect shorelines at the sub-pixel scale. Resulting shorelines are presented in CSV format. Significant uncertainty is associated with the locations of shorelines in extremely dynamic ... |
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