Labeled satellite imagery for training machine learning models that predict the suitability of imagery for shoreline extraction.

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Frequently anticipated questions:

What does this data set describe?

Title:
Labeled satellite imagery for training machine learning models that predict the suitability of imagery for shoreline extraction.
Abstract:
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 validation dataset for a machine learning model that is specifically designed for the task of determining the suitability of an image for the task of estimating the shoreline location.
Supplemental_Information:
This data release was funded by the USGS Coastal and Marine Hazards and Resources Program. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. This data release contains modified Planetscope imagery, provided under the NASA (National Aeronautics and Space Administration) CSDA (Commercial Satellite Data Aquisition) program under the standard Scientific Use License available at https://cdn.earthdata.nasa.gov/conduit/upload/14226/PlanetEULA042220.pdf, and the End User license agreement available at https://earthdata.nasa.gov/s3fs-public/2022-02/Planet_Expanded_EULA_06-21.pdf. This license permits redistribution of imagery in significantly modified form. We provide only the visible (R, G, and B) bands of small sub-portions of downloaded tiles, in jpeg format. As such, the original imagery (multispectral scenes in GeoTIFF format) would have been cropped, its geospatial information removed, and re-encoded into 8bit JPEG format.
  1. How might this data set be cited?
    Buscombe, Daniel, Lundine, Mark A., Batiste, Sharon, and Janda, Catherine N., 20250325, Labeled satellite imagery for training machine learning models that predict the suitability of imagery for shoreline extraction.: data release DOI:10.5066/P14MDKVJ, U.S. Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, California.

    Online Links:

    Other_Citation_Details:
    Suggested Citation: Buscombe, D., Lundine, M.A., Batiste, S., and Janda, C.N., 2025, Labeled satellite imagery for training machine learning models that predict the suitability of imagery for shoreline extraction, U.S. Geological Survey data release, https://doi.org/10.5066/P14MDKVJ.
  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: 180.00000
    East_Bounding_Coordinate: -180.00000
    North_Bounding_Coordinate: 90.00000
    South_Bounding_Coordinate: -90.00000
  3. What does it look like?
    Global_map_of_image_locations.png (PNG)
    Image map showing locations of satellite imagery.
  4. Does the data set describe conditions during a particular time period?
    Beginning_Date: 1984
    Ending_Date: 2024
    Currentness_Reference:
    collection years of satellite imagery.
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: JPEG
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      Indirect_Spatial_Reference:
      Data were generated within a numerical model scheme. The model training data presented are not for a particular geographic area.
    2. What coordinate system is used to represent geographic features?
  7. How does the data set describe geographic features?
    Entity_and_Attribute_Overview:
    These data are designed to train and test a Machine Learning model that is tasked with recognizing suitable and unsuitable imagery for the purposes of shoreline detection. These images have been manually classified. There is one zipped folder for images used for training a Machine Learning model, called ‘train’, and another zipped folder, called ‘test’, for images used for testing that model once trained. Inside the test and train zipped folders, there are two folders of JPEG images; ‘good’ (or suitable for shoreline extraction using CoastSeg or CoastSat), or ‘bad’ (or unsuitable for shoreline extraction using CoastSeg or CoastSat). This dataset consists of visible band images, which are inputs to segmentation and other machine learning models that are used for the purposes of shoreline detection. Each image name contains a string that includes the date and time, as well as the name of the sensor. PS denotes Plantscope imagery. S2 denotes Sentinel 2A imagery. L5 denotes Landsat 5. L7 denotes Landsat 7. L8 denotes Landsat 8. L9 denotes Landsat 9. Date and time of the projected data (UTC) are in yyy-mm-dd hh:MM:SS format (where yyyy is 4 digit year, mm is 2-digit month, dd is 2-digit day, hh is 2-digit hour in 24-hour format, MM is 2-digit minutes, and SS is 2-digit seconds). For example, the file name 'ID_spl62_datetime06-21-24__05_32_07_2017-08-27-10-56-52_RGB_S2.jpg' is for an image from Sentinel 2A collected on 2017-08-27 at 10:56:52. These data are ready to be ingested into a deep learning or machine learning model training pipeline, using software such as Tensorflow, Keras, or Pytorch. Images have a suffix identifier of either "ID_" or "Merbok_" which is not important for intended image use.
    Entity_and_Attribute_Detail_Citation: U.S. Geological Survey

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Daniel Buscombe
    • Mark A. Lundine
    • Sharon Batiste
    • Catherine N. Janda
  2. Who also contributed to the data set?
    This data release was funded by the USGS Coastal and Marine Hazards and Resources Program.
  3. To whom should users address questions about the data?
    U.S. Geological Survey, Pacific Coastal and Marine Science Center
    Attn: PCMSC Science Data Coordinator
    2885 Mission Street
    Santa Cruz, CA

    831-427-4747 (voice)
    pcmsc_data@usgs.gov

Why was the data set created?

These data provide resources for automatically detected coastal shoreline position for resource managers, science researchers, students, and the general public. These data can be used with image viewing software and can be used within specialist software for the purposes of training Machine Learning models to identify suitable and unsuitable imagery for the purposes of shoreline mapping. The imagery are organized into two folders; those for training and those for testing a Machine Learning model.

How was the data set created?

  1. From what previous works were the data drawn?
    Landsat imagery (source 1 of 5)
    U.S. Geological Survey, 2025, Landsat imagery (from Landsat 8-9): U.S. Geological Survey, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution:
    The archive of Landsat 7 satellite imagery was accessed through Google Earth Engine.
    Landsat imagery (source 2 of 5)
    U.S. Geological Survey, 2025, Landsat imagery (from Landsat 7): U.S. Geological Survey, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution:
    The archive of Landsat 7 satellite imagery was accessed through Google Earth Engine.
    Landsat imagery (source 3 of 5)
    U.S. Geological Survey, 2025, Landsat imagery (from Landsat 5): U.S. Geological Survey, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution:
    The archive of Landsat 5 satellite imagery was accessed through Google Earth Engine.
    Sentinel-2A imagery (source 4 of 5)
    Copernicus, a program of the European Union, 2025, Sentinel-2 imagery: Copernicus, a program of the European Union, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution:
    The archive of Sentinel 2A satellite imagery was accessed through Google Earth Engine.
    PlanetScope imagery (source 5 of 5)
    PBC, Planet Labs, 2025, PlanetScope imagery: Planet Labs PBC, online.

    Type_of_Source_Media: online database
    Source_Contribution:
    The archive of PlanetScope satellite imagery was accessed through the PlanetScope Application Programming Interface.
  2. How were the data generated, processed, and modified?
    Date: 01-Jul-2024 (process 1 of 4)
    Set up CoastSeg toolbox (Fitzpatrick and others, 2024) for implementation along the region of interest. Toolbox set up in python 3.10 to run for geography spanning coastline for numerous worldwide locations (see map), for the time period of 01 March 1984 to 31 December 2024. Images were then manually classified as either suitable or unsuitable for analysis.
    Date: 01-Aug-2024 (process 2 of 4)
    Ran CoastSeg toolbox on Landsat imagery available through Google Earth Engine (Gorelick and others, 2017) for geography and time period of interest. Imagery had horizontal resolution of between 4 and 30 m depending on source. Imagery with an original horizontal resolution of 30 m was pan-sharpened to 15 m. The geospatial information has been removed; it is not necessary for the intended purpose of training Machine Learning models to discriminate among suitable and unsuitable imagery. Only the red, blue, and green channels have been extracted from the original multispectral imagery and, after pansharpening, these three bands are saved as a JPEG format image. Data sources used in this process:
    • Sentinel-2A imagery
    • Landsat imagery
    • PlanetScope imagery
    Date: 01-Aug-2024 (process 3 of 4)
    Checked output to ensure quality results.
    Date: 01-Aug-2024 (process 4 of 4)
    Organized image data into folders of suitable and unsuitable images. Originating imagery dates/times are included in files. No positions are encoded in the files because this data is intended solely to train a Machine Learning model to identify imagery suitable for shoreline analysis (such as imagery in which the shoreline is visible to the human eye). The imagery are organized into two folders; those for training and those for testing a Machine Learning model.
  3. What similar or related data should the user be aware of?
    Fitzpatrick, S., Buscombe, D., Warrick, J.A., Lundine, M.A., and Vos, K., 2024, Sub-annual to multi-decadal shoreline variability from publicly available satellite imagery.

    Online Links:

    Other_Citation_Details:
    Fitzpatrick, S., Buscombe, D., Warrick, J.A., Lundine, M.A., and Vos, K., 2024, CoastSeg: an accessible and extendable hub for satellite-derived-shoreline (SDS) detection and mapping. Journal of Open Source Software, 9(99), 6683
    Vos, K., Harley, M.D., Splinter, K.D., Simmons, J.A., and Turner, I.L., 2019, Sub-annual to multi-decadal shoreline variability from publicly available satellite imagery.

    Online Links:

    Other_Citation_Details:
    Vos, K., Harley, M.D., Splinter, K.D., Simmons, J.A., and Turner, I.L., 2019, Sub-annual to multi-decadal shoreline variability from publicly available satellite imagery: Coastal Engineering, v. 150, p. 160-174.
    Gorelick, N., Hancher, M., Dixon, M., Ilyshechenko, S., Thau, D., and Moore, R., 2017, Google Earth Engine: Planetary-scale geospatial analysis for everyone..

    Online Links:

    Other_Citation_Details:
    Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R., 2017, Google Earth Engine: Planetary-scale geospatial analysis for everyone: Remote Sensing of Environment, v. 202, p. 18-27.

How reliable are the data; what problems remain in the data set?

  1. How well have the observations been checked?
  2. How accurate are the geographic locations?
  3. How accurate are the heights or depths?
  4. Where are the gaps in the data? What is missing?
    Data set is considered complete for the information presented. Users are advised to read the rest of the metadata record carefully for additional details.
  5. How consistent are the relationships among the observations, including topology?
    Data have undergone QA/QC and fall within expected/reasonable ranges.

How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?
Access_Constraints No access constraints
Use_Constraints USGS-authored or produced data and information are in the public domain from the U.S. Government and are freely redistributable with proper metadata and source attribution. Please recognize and acknowledge the U.S. Geological Survey as the originator(s) of the dataset and in products derived from these data.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey - CMGDS
    2885 Mission Street
    Santa Cruz, CA

    831-427-4747 (voice)
    pcmsc_data@usgs.gov
  2. What's the catalog number I need to order this data set? These data are available in zipped folders. There is one zipped folder for images used for training a Machine Learning model, called ‘train’, and another zipped folder for images used for testing that model once trained. Inside the test and train zipped folders, there are two folders of JPEG images; ‘good’ (or suitable for shoreline extraction using CoastSeg or CoastSat), or ‘bad’ (or unsuitable for shoreline extraction using CoastSeg or CoastSat). Each image name contains a string that includes the date and time, as well as the name of the sensor. PS denotes Plantscope imagery. S2 denotes Sentinel 2A imagery. L5 denotes Landsat 5. L7 denotes Landsat 7. L8 denotes Landsat 8. L9 denotes Landsat 9.
  3. What legal disclaimers am I supposed to read?
    Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty.
  4. How can I download or order the data?
    • Availability in digital form:
      Data format: zip files containing images in format JPEG Size: 3000
      Network links: https://doi.org/10.5066/P14MDKVJ
    • Cost to order the data: None.

  5. What hardware or software do I need in order to use the data set?
    These data can be viewed with image (picture) viewing software or numerical processing software such as python or Matlab.

Who wrote the metadata?

Dates:
Last modified: 25-Mar-2025
Metadata author:
U.S. Geological Survey, Pacific Coastal and Marine Science Center
Attn: PCMSC Science Data Coordinator
2885 Mission Street
Santa Cruz, CA

831-427-4747 (voice)
pcmsc_data@usgs.gov
Metadata standard:
Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)
This page is <https://cmgds.marine.usgs.gov/catalog/pcmsc/DataReleases/CMGDS_DR_tool/DR_P14MDKVJ/image_suitability_metadata.faq.html>
Generated by mp version 2.9.51 on Tue Apr 1 10:12:10 2025