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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 determining the suitability of a deep-learning-based image segmentation model output for the task of estimating the shoreline location. These data were used to train a Machine Learning model to recognize the quality of an image segmentation.
Complete Metadata
| @id | http://datainventory.doi.gov/id/dataset/e4273b1ca4d9ccb23ac3f0e0455ace15 |
|---|---|
| bureauCode |
[ "010:12" ] |
| identifier | USGS:2cef6b9f-2135-4f2c-8dbd-71c05f346ff1 |
| spatial | 180.0,-90.0,-180.0,90.0 |
| theme |
[ "geospatial" ] |
