|
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 ... |
Info |
|
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 ... |
Info |
|
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 ... |
Info |