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Region of Interests (ROI), Transects, and Reference Shorelines for Three Sites of Western Long Island, New York
This data release provides tidally corrected shoreline positions for three sites of western Long Island, NY (Rockaway Peninsula, Long Beach, and Jones Beach Island). GeoJSON files are derived from CoastSeg version 1.1.35 (Fitzpatrick and others, 2024) with settings derived from config files. These files contain the region of interests (ROIs), transects, and reference shorelines for each section. CoastSeg collects satellite images from Google Earth Engine to create shoreline data along with user-supplied ... |
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Tidally Corrected Shoreline Positions for Three Sites of Western Long Island, New York
This data release provides tidally corrected shoreline positions for three sites of western Long Island, NY (Rockaway Peninsula, Long Beach, Jones Beach Island). The CSVs are derived from the software CoastSeg (Fitzpatrick and others, 2024). CoastSeg collects satellite images from Google Earth Engine to create shoreline data along with user supplied inputs based on the CoastSat methodology (Vos and others, 2019). Data have been tidally corrected based on beach foreshore slopes (Farris and Webber, 2024). The ... |
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Region of interests (ROI), transects, and reference shorelines for five groin field sites around the United States
This data release provides the region of interest (ROI), transects, and the reference shoreline for five groin field sites of around the United States (Ventura, CA, Newport Beach, CA, Santa Monica, CA, Long Branch, NJ, Sandwich, MA). GeoJSON files are derived from CoastSeg version 1.2.16. (Fitzpatrick and others, 2024) with settings derived from config files. These files contain the region of interests (ROIs), transects, and reference shorelines for each section. CoastSeg collects satellite images from ... |
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Tidally corrected shoreline positions for five groin fields sites around the United States
This data release provides tidally corrected shoreline positions for five groin field sites of around the United States (Ventura, CA, Newport Beach, CA , Santa Monica, CA, Long Branch, NJ, Sandwich, MA). The CSVs are derived from the software CoastSeg (Fitzpatrick and others, 2024). CoastSeg collects satellite images from Google Earth Engine to create shoreline data along with user supplied inputs based on the CoastSat methodology (Vos and others, 2019). Data have been tidally corrected based on beach ... |
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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 ... |
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Labeled satellite imagery for training machine learning models that predict the suitability of imagery for shoreline extraction.
A labeled dataset of Landsat, Sentinel, and Planetscope satellite visible-band images of coastal shoreline regions, consisting of folders of images that have been labeled as either suitable or unsuitable for shoreline detection using existing conventional approaches such as CoastSat (Vos and others, 2019) or CoastSeg (Fitzpatrick and others, 2024). These data are intended to be used as inputs to models that determine the suitability or otherwise of the image. These data are only to be used as a training and ... |
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Labeled satellite imagery for training machine learning models that predict the suitability of semantic segmentation model outputs for shoreline extraction.
A dataset of semantic segmentations of Landsat, Sentinel, and Planetscope satellite images of coastal shoreline regions, consisting of folders of images that have been labeled as either suitable or unsuitable for shoreline detection using existing conventional approaches such as CoastSat (Vos and others, 2019) or CoastSeg (Fitzpatrick and others, 2024). These data are intended only to be used as a training and validation dataset for a machine learning model that is specifically designed for the task of ... |
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Satellite-derived shoreline positions from CoastSeg in multiple U.S. locations (1984-2023)
This dataset contains shoreline positions derived from available satellite imagery for multiple locations (Barter Island, Alaska; Elwha, Washington; Cape Cod, Massachusetts; Madeira Beach, Florida; and Rincon, Puerto Rico) across the United States for the time period 1984 to 2023. An open-source toolbox, CoastSeg (Fitzpatrick and others, 2024a; Fitzpatrick and others, 2024b), was used to classify coastal Landsat and Sentinel imagery and detect shorelines at the sub-pixel scale, using the CoastSat (Vos and ... |
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Satellite-derived shoreline vector files and settings from CoastSeg in multiple U.S. locations (1984-2023)
This dataset contains shorelines (as vectors, where vertices are positions determined along transects) derived from available satellite imagery for multiple locations (Barter Island, Alaska; Elwha, Washington; Cape Cod, Massachusetts; Madeira Beach, Florida; and Rincon, Puerto Rico) and associated settings used to derive the data across the United States for the time period 1984 to 2023. An open-source toolbox, CoastSeg (Fitzpatrick and others, 2024a; Fitzpatrick and others, 2024b), was used to classify ... |
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