A 1.2 Billion Pixel Human-Labeled Dataset for Data-Driven Classification of Coastal Environments.

ABSTRACT: The world's coastlines are spatially highly variable, coupled-human-natural systems that comprise a nested hierarchy of component landforms, ecosystems, and human interventions, each interacting over a range of space and time scales. Understanding and predicting coastline dynamics necessitates frequent observation from imaging sensors on remote sensing platforms. Machine Learning models that carry out supervised (i.e., human-guided) pixel-based classification, or image segmentation, have transformative applications in spatio-temporal mapping of dynamic environments, including transient coastal landforms, sediments, habitats, waterbodies, and water flows. However, these models require large and well-documented training and testing datasets consisting of labeled imagery. We describe "Coast T

SUBMITTER: Buscombe D 

PROVIDER: S-EPMC9860036 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature