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

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.